Models of the kinetics of proteins and ion channels associated with neuron
activity represent the lowest level of modeling in a computational
neurogenetic model. The altered activity of proteins in some diseases,
such as the amyloid beta protein in Alzheimer's disease, must be modeled at the molecular level to accurately predict the effect on cognition. Ion channels, which are vital to the propagation of action potentials, are another molecule that may be modeled to more accurately reflect biological processes. For instance, to accurately model synaptic plasticity (the strengthening or weakening of synapses) and memory, it is necessary to model the activity of the NMDA receptor (NMDAR). The speed at which the NMDA receptor lets Calcium ions into the cell in response to Glutamate is an important determinant of Long-term potentiation via the insertion of AMPA receptors (AMPAR) into the plasma membrane at the synapse of the postsynaptic cell (the cell that receives the neurotransmitters from the presynaptic cell).
Genetic regulatory network
An example of a model of a gene network. The genes, G1 through G4,
are modified by either inhibitory signals, represented by bars and
negative coefficients, or excitatory signals, represented by arrows and
positive coefficients. The interactions are represented numerically by
the matrix on the right, R.
In most models of neural systems neurons are the most basic unit modeled.
In computational neurogenetic modeling, to better simulate processes
that are responsible for synaptic activity and connectivity, the genes
responsible are modeled for each neuron.
A gene regulatory network,
protein regulatory network, or gene/protein regulatory network, is the
level of processing in a computational neurogenetic model that models
the interactions of genes and proteins relevant to synaptic activity and general cell functions. Genes and proteins are modeled as individual nodes,
and the interactions that influence a gene are modeled as excitatory
(increases gene/protein expression) or inhibitory (decreases
gene/protein expression) inputs that are weighted to reflect the effect a
gene or protein is having on another gene or protein. Gene regulatory
networks are typically designed using data from microarrays.
Modeling of genes and proteins allows individual responses of
neurons in an artificial neural network that mimic responses in
biological nervous systems, such as division (adding new neurons to the
artificial neural network), creation of proteins to expand their cell
membrane and foster neurite
outgrowth (and thus stronger connections with other neurons),
up-regulate or down-regulate receptors at synapses (increasing or
decreasing the weight (strength) of synaptic inputs), uptake more neurotransmitters, change into different types of neurons, or die due to necrosis or apoptosis.
The creation and analysis of these networks can be divided into two
sub-areas of research: the
gene up-regulation that is involved in the normal functions of a neuron,
such as growth, metabolism, and synapsing; and the effects of mutated
genes on neurons and cognitive functions.
A model of an individual neuron. The inputs, x0 to xm, are modified by the input weights, w0 to wm, and then combined into one input, vk. The transfer function, , then uses this input to determine the output, yk.
An artificial neural network generally refers to any computational model that mimics the central nervous system,
with capabilities such as learning and pattern recognition. With
regards to computational neurogenetic modeling, however, it is often
used to refer to those specifically designed for biological accuracy
rather than computational efficiency. Individual neurons are the basic
unit of an artificial neural network, with each neuron acting as a node.
Each node receives weighted signals from other nodes that are either excitatory or inhibitory. To determine the output, a transfer function (or activation function)
evaluates the sum of the weighted signals and, in some artificial
neural networks, their input rate. Signal weights are strengthened (long-term potentiation) or weakened (long-term depression) depending on how synchronous the presynaptic and postsynaptic activation rates are (Hebbian theory).
The synaptic activity of individual neurons is modeled using equations to determine the temporal (and in some
cases, spatial) summation of synaptic signals, membrane potential, threshold for action potential
generation, the absolute and relative refractory period, and optionally ion receptor channel kinetics and Gaussian noise
(to increase biological accuracy by incorporation of random elements).
In addition to connectivity, some types of artificial neural networks,
such as spiking neural networks, also model the distance between neurons, and its effect on the synaptic weight (the strength of a synaptic transmission).
Combining gene regulatory networks and artificial neural networks
For
the parameters in the gene regulatory network to affect the neurons in
the artificial neural network as intended there must be some connection
between them. In an organizational context, each node (neuron) in the
artificial neural network has its own gene regulatory network associated
with it. The weights (and in some networks, frequencies of synaptic
transmission to the node), and the resulting membrane potential of the
node (including whether an action potential
is produced or not), affect the expression of different genes in the
gene regulatory network. Factors affecting connections between neurons,
such as synaptic plasticity,
can be modeled by inputting the values of synaptic activity-associated
genes and proteins to a function that re-evaluates the weight of an
input from a particular neuron in the artificial neural network.
Incorporation of other cell types
Other cell types besides neurons can be modeled as well. Glial cells, such as astroglia and microglia, as well as endothelial cells,
could be included in an artificial neural network. This would enable
modeling of diseases where pathological effects may occur from sources
other than neurons, such as Alzheimer's disease.
Factors affecting choice of artificial neural network
While
the term artificial neural network is usually used in computational
neurogenetic modeling to refer to models of the central nervous system
meant to possess biological accuracy, the general use of the term can be
applied to many gene regulatory networks as well.
Time variance
Artificial neural networks, depending on type, may or may not take into account the timing of inputs. Those that do, such as spiking neural networks,
fire only when the pooled inputs reach a membrane potential is reached.
Because this mimics the firing of biological neurons, spiking neural
networks are viewed as a more biologically accurate model of synaptic
activity.
Growth and shrinkage
To accurately model the central nervous system, creation and death of neurons should be modeled as well. To accomplish this, constructive artificial neural networks that are able to grow or shrink to adapt to inputs are often used. Evolving connectionist systems are a subtype of constructive artificial neural networks (evolving in this case referring to changing the structure of its neural network rather than by mutation and natural selection).
Randomness
Both synaptic transmission and gene-protein interactions are stochastic
in nature. To model biological nervous systems with greater fidelity
some form of randomness is often introduced into the network. Artificial
neural networks modified in this manner are often labeled as
probabilistic versions of their neural network sub-type (e.g., pSNN).
Incorporation of fuzzy logic
Fuzzy logic is a system of reasoning that enables an artificial neural network to deal in non-binary and linguistic variables. Biological data is often unable to be processed using Boolean logic,
and moreover accurate modeling of the capabilities of biological
nervous systems requires fuzzy logic. Therefore, artificial neural
networks that incorporate it, such as evolving fuzzy neural networks (EFuNN) or Dynamic Evolving Neural-Fuzzy Inference Systems (DENFIS),
are often used in computational neurogenetic modeling. The use of fuzzy
logic is especially relevant in gene regulatory networks, as the
modeling of protein binding strength often requires non-binary
variables.
Types of learning
Artificial
Neural Networks designed to simulate of the human brain require an
ability to learn a variety of tasks that is not required by those
designed to accomplish a specific task. Supervised learning
is a mechanism by which an artificial neural network can learn by
receiving a number of inputs with a correct output already known. An
example of an artificial neural network that uses supervised learning is
a multilayer perceptron (MLP). In unsupervised learning,
an artificial neural network is trained using only inputs. Unsupervised
learning is the learning mechanism by which a type of artificial neural
network known as a self-organizing map
(SOM) learns. Some types of artificial neural network, such as evolving
connectionist systems, can learn in both a supervised and unsupervised
manner.
Improvement
Both
gene regulatory networks and artificial neural networks have two main
strategies for improving their accuracy. In both cases the output of the
network is measured against known biological data using some function,
and subsequent improvements are made by altering the structure of the
network. A common test of accuracy for artificial neural networks is to
compare some parameter of the model to data acquired from biological
neural systems, such as from an EEG. In the case of EEG recordings, the local field potential (LFP) of the artificial neural network is taken and compared to EEG data acquired from human patients. The relative intensity ratio (RIRs) and fast Fourier transform (FFT) of the EEG are compared with those generated by the artificial neural networks to determine the accuracy of the model.
Genetic algorithm
Because the amount of data on the interplay of genes and neurons and their effects is not enough to construct a rigorous model,
evolutionary computation is used to optimize artificial neural networks and gene regulatory networks, a common technique being the genetic algorithm.
A genetic algorithm is a process that can be used to refine models by
mimicking the process of natural selection observed in biological
ecosystems. The primary advantages are that, due to not requiring
derivative information, it can be applied to black box problems and multimodal optimization. The typical process for using genetic algorithms to refine a gene
regulatory network is: first, create a population; next, to create offspring via a crossover operation and
evaluate their fitness; then, on a group chosen for high fitness, simulate mutation via a mutation operator;
finally, taking the now mutated group, repeat this process until a desired level of fitness is demonstrated.
Evolving systems
Methods
by which artificial neural networks may alter their structure without
simulated mutation and fitness selection have been developed. A dynamically evolving neural network
is one approach, as the creation of new connections and new neurons can
be modeled as the system adapts to new data. This enables the network to
evolve in modeling accuracy without simulated natural selection. One
method by which dynamically evolving networks may be optimized, called
evolving layer neuron aggregation, combines neurons with sufficiently
similar input weights into one neuron. This can take place during the
training of the network, referred to as online aggregation, or between
periods of training, referred to as offline aggregation. Experiments
have suggested that offline aggregation is more efficient.
Potential applications
A
variety of potential applications have been suggested for accurate
computational neurogenetic models, such as simulating genetic diseases,
examining the impact of potential treatments, better understanding of learning and cognition, and development of hardware able to interface with neurons.
The simulation of disease states is of particular interest, as
modeling both the neurons and their genes and proteins allows linking
genetic mutations and protein abnormalities to pathological effects in
the central nervous system. Among those diseases suggested as being
possible targets of computational neurogenetic modeling based analysis
are epilepsy, schizophrenia, mental retardation, brain aging and
Alzheimer's disease, and Parkinson's disease.
Within personality psychology, personal construct theory (PCT) or personal construct psychology (PCP) is a theory of personality and cognition developed by the AmericanpsychologistGeorge Kelly in the 1950s. The theory addresses the psychological reasons for actions. Kelly proposed that individuals can be psychologically evaluated according to similarity–dissimilarity poles, which he called personal constructs (schemas, or ways of seeing the world). The theory is considered by some psychologists as forerunner to theories of cognitive therapy.
From the theory, Kelly derived a psychotherapy approach, as well as a technique called the repertory grid interview,
that helped his patients to analyze their own personal constructs with
minimal intervention or interpretation by the therapist. The repertory grid was later adapted for various uses within organizations, including decision-making and interpretation of other people's world-views. The UK Council for Psychotherapy, a regulatory body, classifies PCP therapy within the experiential subset of the constructivist school.
Principles
A
main tenet of PCP theory is that a person's unique psychological
processes are channeled by the way they anticipate events. Kelly
believed that anticipation and prediction are the main drivers of our
mind. "Every man is, in his own particular way, a scientist", said
Kelly: people are constantly building up and refining theories and
models about how the world works so that they can anticipate future
events. People start doing this at birth (for example, a child discovers
that if they start to cry, their mother will come to them) and continue
refining their theories as they grow up.
Kelly proposed that every construct is bipolar, specifying how
two things are similar to each other (lying on the same pole) and
different from a third thing, and they can be expanded with new ideas.
(More recent researchers have suggested that constructs need not be
bipolar.)
People build theories—often stereotypes—about other people and also try
to control them or impose on others their own theories so as to be
better able to predict others' actions. All these theories are built up
from a system of constructs. A construct has two extreme points, such as
"happy–sad," and people tend to place items at either extreme or at
some point in between. People's minds, said Kelly, are filled up with
these constructs at a low level of awareness.
A given person, set of persons, any event, or circumstance can be
characterized fairly precisely by the set of constructs applied to it
and by the position of the thing within the range of each construct. For
example, Fred may feel as though he is not happy or sad (an example of a
construct); he feels as though he is between the two. However, he feels
he is more clever than he is stupid (another example of a construct). A
baby may have a preverbal construct of what behaviors may cause their
mother to come to them. Constructs can be applied to anything people put
their attention to, and constructs also strongly influence what people
fix their attention on. People can construe reality by constructing
different constructs. Hence, determining a person's system of constructs
would go a long way towards understanding them, especially the person's
essential constructs that represent their very strong and unchangeable
beliefs and their self-construal.
Kelly did not use the concept of the unconscious; instead, he proposed the notion of "levels of awareness" to explain why people did what they did. He identified "construing" as the highest level and "preverbal" as the lowest level of awareness.
Some psychologists have suggested that PCT is not a psychological theory but a metatheory because it is a theory about theories.
Therapy approach
Kelly believed in a non-invasive or non-directive approach to psychotherapy. Rather than having the therapist interpret the person's psyche,
which would amount to imposing the doctor's constructs on the patient,
the therapist should just act as a facilitator of the patient finding
his or her own constructs. The patient's behavior is then mainly
explained as ways to selectively observe the world, act upon it and
update the construct system in such a way as to increase predictability.
To help the patient find his or her constructs, Kelly developed the
repertory grid interview technique.
Kelly explicitly stated that each individual's task in
understanding their personal psychology is to put in order the facts of
his or her own experience. Then the individual, like the scientist, is
to test the accuracy of that constructed knowledge by performing those
actions the constructs suggest. If the results of their actions are in
line with what the knowledge predicted, then they have done a good job
of finding the order in their personal experience. If not, then they can
modify the construct: their interpretations or their predictions or
both. This method of discovering and correcting constructs is roughly
analogous to the general scientific method that is applied in various ways by modern sciences to discover truths about the universe.
The repertory grid serves as part of various assessment methods to
elicit and examine an individual's repertoire of personal constructs.
There are different formats such as card sorts, verbally administered
group format, and the repertory grid technique.
The repertory grid itself is a matrix
where the rows represent constructs found, the columns represent the
elements, and cells indicate with a number the position of each element
within each construct. There is software available to produce several
reports and graphs from these grids.
To build a repertory grid for a patient, Kelly might first ask
the patient to select about seven elements (although there are no fixed
rules for the number of elements) whose nature might depend on whatever the patient or therapist are trying to discover.
For instance, "Two specific friends, two work-mates, two people you
dislike, your mother and yourself", or something of that sort. Then,
three of the elements would be selected at random, and then the
therapist would ask: "In relation to ... (whatever is of interest), in
which way are two of these people alike but different from the third?"
The answer is sure to indicate one of the extreme points of one of the
patient's constructs. He might say for instance that Fred and Sarah are
very communicative whereas John isn't. Further questioning would reveal
the other end of the construct (say, introvert) and the positions of the
three characters between extremes. Repeating the procedure with
different sets of three elements ends up revealing several constructs
the patient might not have been fully aware of.
In the book Personal Construct Methodology, researchers Brian R. Gaines and Mildred L.G. Shaw noted that they "have also found concept mapping and semantic network
tools to be complementary to repertory grid tools and generally use
both in most studies" but that they "see less use of network
representations in PCP studies than is appropriate". They encouraged practitioners to use semantic network techniques in addition to the repertory grid.
Organizational applications
PCP
has always been a minority interest among psychologists. During the
last 30 years, it has gradually gained adherents in the US, Canada, the
UK, Germany, Australia, Ireland, Italy and Spain. While its chief fields
of application remain clinical and educational psychology, there is an
increasing interest in its applications to organizational development, employee training and development, job analysis, job description and evaluation. The repertory grid
is often used in the qualitative phase of market research, to identify
the ways in which consumers construe products and services.
The self-reference effect is a tendency for people to encode
information differently depending on whether they are implicated in the
information. When people are asked to remember information when it is
related in some way to themselves, the recall rate can be improved.
Research
In 1955, George Kelly published his theory about how humans create personal constructs.
This was a more general cognitive theory based on the idea that each
individual's psychological processes are influenced by the way they
anticipate events. This lays the groundwork for the ideas of personal
constructs. Attribution theory
is an explanation of the way people attribute the causes of behavior
and events, which also involved creating a construct of self, since
people can explain things related to themselves differently from the
same thing happening to someone else. Related to the attribution theory,
the fundamental attribution error
is an explanation of when an individual explains someone's given
behavior in a situation through emphasis on internal characteristics
(personality) rather than considering the situation's external factors.
Studies such as one by Jones, Sensenig, and Haley
corroborated the idea that the self has a special construct, by simply
asking experiment subjects to describe their "most significant
characteristics". The results showed that the majority of responses were
based on positive characteristics such as "sensitive", "intelligent",
and "friendly". This ties in very well with other cognitive phenomena
such as illusory superiority,
in that it is a well observed fact that people rate themselves
differently from how they rate others. In 2012, Stanley B. Klein
published an article on the self and memory and how it relates to the
self-reference effect. In recent years, studies on the self-reference
effect have shifted from identifying mechanisms to using the
self-reference as a research tool in understanding the nature of memory.
Klein discusses words encoded with respect to oneself (the
self-relevance effect) are recalled more often than words that are
unrelated to the self.
In Japan, regarding memory, people who showed higher altruism tend not to exhibit self-reference effect.
Associated brain regions
Cortical mid-line structures
In the past 20 years plus there has been an increase in cognitive neuroscience studies that focus on the concept of the self. These studies were developed in hopes of determining if there are certain brain regions that can account for the encoding
advantages involved in the self-reference effect. A great deal of
research has been focused on several regions of the brain collectively
identified as the cortical midline region. Brain imaging studies have
raised the question of whether neural activity in cortical midline
regions is self-specific. A quantitativemeta-analysis that included 87 studies, representing 1433 participants, was conducted to discuss these questions. The analysis
uncovered activity within several cortical midline structures in
activities in which participants performed tasks involving the concept
of self. Most studies that report such midline activations use tasks
that are geared towards uncovering neural processes that are related to social or psychological aspects of the self, such as self-referential judgments, self-appraisal, and judgments of personality
traits. Also, in addition to their perceived role in several forms of
self-representation, cortical midline structures are also involved in
the processing of social relationships
and recognizing personally familiar others. Studies that show midline
activations during understanding of social interactions between others
or ascribing social traits to others (impression formation) typically require subjects to reference the mental state of others.
Prefrontal cortex
There
are several areas within the cortical midline structure that are
believed to be associated with the self-reference effect. One of the
more active regions involved in the self-reference effect appears to be
the medial prefrontal cortex (mPFC). The prefrontal cortex (PFC) is the area of the brain that is believed to be involved in the planning of complex behavior
and the expression and regulation of personality characteristics in
social situations. The implication that the prefrontal cortex is
involved in the regulation of unique internal personality
characteristics illustrates how it may be an important component of the
self-reference effect. The medial prefrontal cortex in both hemispheres
has been proposed as a site of the "self model" which is a theoretical
construct made of essential features such as feelings of continuity and
unity as well as experience of agency.
The idea of the self-reference effect being linked to the medial
prefrontal cortex stems from several experiments attempting to locate
the mechanisms
involved in the self-referencing process. Experiments in which
participants were assigned tasks that required them to reflect on, or introspect about their own mental states showed activity in the medial prefrontal cortex. For example, activity in the ventromedial prefrontal cortex
has been observed in tasks in which participants report on their own
personalities or preferences, adopt a first person perspective, or
reflect on their current affective state.
Similar activity in the ventromedial prefrontal cortex is displayed in
cases where participants show the memory advantage that emerges when
items are encoded in a self-relevant manner. During various functional magnetic resonance imaging
(fMRI) tests conducted while participants were performing
self-referential tasks, there was a consistent showing of increases in blood-oxygen-level dependent (BOLD) signals in the ventral medial and dorsal medial prefrontal cortex.
Measuring BOLD signals is necessary for a sound interpretation of fMRI
signals, as BOLD fMRI reflects a complex monitoring of changes in cerebral blood flow, cerebral blood volume and blood oxygenation.
Parietal lobe
In addition to areas of the prefrontal cortex, research has suggested that there are areas within the parietal lobe
that also play a role in activating the self-reference effect. During
fMRI given during self-referential tasks there also appeared to be
increases in BOLD signals within the medial and lateral parietal cortex To further determine whether or not the medial parietal lobe plays a role in self-referencing, participants were subjected to transcranial magnetic stimulation over the region. Stimulation over this region produced a decrease in the ability of participants to retrieve previous judgments of mental self when compared to the retrieval of judgment of others.
Development over the lifespan
Childhood
The
development of a sense of self and the understanding that one is
separate and uniquely different from others is vital in the development
of the self-reference effect advantage. As young children grow, their
sense of self and understanding of the world around them is continuously
increasing. Although this occurs at different stages for each child,
research has shown rather early development of the self-reference
advantage. Research focusing on the recall abilities of children have shown the self-referencing advantage in children as young as five years old. Language development appears to play a significant role in the development and use of the self-reference effect. Verbal labeling
is among the first strategic behaviors shown by young children in order
to enhance memory, and as children progress in age and language
development, their performance on memory tasks involving
self-referencing increases.
A study done in 2011 on preschoolers found that observations on
children as young as three years old suggests that the self-reference
effect is apparent in event memory, by their ability to self-recognize.
Adulthood
Like
children, the continuous development of a self-concept is related to
the development of self-referencing in individuals. The relationships
formed with intimate others over the lifespan appear to have an effect
on self-referencing in relation to memory. The extent to which we
include others in our self-concept has been a topic of particular
interest for social psychologists. Theories of intimacy and personal
relationships might suggest that the self-reference effect is affected
by the closeness of a relationship with the other used as a target. Some
researchers define closeness as an extension of self into other and
suggest that one's cognitive processes about a close other develop in a
way so as to include that person as part of the self. Consistent with
this idea, it has been demonstrated that the memorial advantage afforded
to self-referenced material can be diminished or eliminated when the
comparison target is an intimate other such as a parent, friend, or
spouse
The capacity for utilizing the self-reference effect remains relatively
high throughout the lifespan, even well into old age. Normally
functioning older adults can benefit from self-referencing. Ageing is
marked by cognitive impairments in a number of domains including long-term memory, but older adults' memory performance is malleable.
Memory strategies and orientations that engage "deep" encoding
processes benefit older adults. For example, older adults exhibit
increased recall when using self-generated strategies that rely on
personally relevant information (e.g., important birthdates) relative to
other mnemonic strategies.
However, research has shown that there are some differences between
older adults and younger adults use of the self-reference advantage.
Like young adults, older adults exhibit superior recognition for
self-referenced items. But the amount of cognitive resources an
individual has influence on how much older adults benefit from self
referencing. Self-referencing improves older adult's memory, but its
benefits are restricted regardless of the social and personally relevant
nature of the task.
A reason for this change in self referencing may be the change in
brain activation that has been observed in older adults when studying
self-referencing. Older adults showed more activity in the medial
prefrontal cortex and along the cingulate gyrus
than young adults. Because these regions often are associated with
self-referential processing, these results suggest that older adults'
mnemonic boost for positive information may stem from an increased
tendency to process this information in relation to themselves. It has
been proposed that this "positivity shift" may occur because older
adults put more emphasis on emotion regulation goals than do young
adults, with older adults having a greater motivation to derive
emotional meaning from life and to maintain positive affect.
Effect on students
Students
are often challenged when faced with the attempt to recall memories.
It is therefore important to understand the effects of self-reference
encoding for students and beneficial ways it can increase their recall
of information. The purpose of the current study was to examine the
effects of, self-referent encoding.
Rogers, Kuiper, and Kirker (1977) performed one of the first
studies examining the self-reference effect making it a foundational
article. The focus of the study was to identify the importance of the
self and how it is implicated when processing personal information.
The self-reference effect has been considered a robust encoding
strategy and has been effective over the past 30 years (Gutches et al.,
2007). The process behind this study was to gather students and divide
them into four different task groups and they would be asked to give a
yes or no answer to a trait adjective being presented to them. The four
tasks that were used were: structural, phonemic, semantic, and
self-reference. There were some different theories that support the
study. The personality theory stressed that the observer's network when
looking at the trait adjectives is an essential part of how they
process personal information Hastorf et al. (1970). Another theory that
supports this study is the attribution theory. It is another example
where a person's organization traits fit with the self-reference effect
Jones et al. (1971). The self is visualized as a schema that is involved
with processing personal information, interpretation, and memories
which is considered a powerful and effective process (Rogers et al.,
1977).
Gutchess, Kensinger, and Schacter (2007) performed a study where
they used age as a factor when looking at the self-reference effect.
The first and second experiment looks at the young and older adults and
they are presented with encoded adjectives and they must decide if it
describes them. The third experiment is deciding if they found these
traits desirable towards themselves. The age difference was shown
effective with the self-reference effect leaving the older adults
showing superiority of recognition for self-referenced items that were
relative. Although, self-referencing the older adults did not have the
same restoring level as the younger adults. A major factor that played
in this study was the availability of cognitive resources. When there
was a greater availability of cognitive resources, the ability to
enhance memory similarly for both young and older adults diverged from
socioemotional processing (Gutches et al., 2007).
Hartlep and Forsyth (2001) performed a study using two different
approaches when studying for an exam. The first approach was the
survey, question, read, reflect, recite, and review method which is
called the SQR4. The other method was the self-reference method. The
third group was a controlled group and received no special instructions
on their studying process (Hartlep & Forsyth, 2001). This study is
considered an applied study. People who have a more elaborative
cognitive framework, the better they will be able to retrieve a memory.
The most elaborative cognitive framework someone can have is knowledge
of themselves (Hartlep & Forsyth, 2001). The self-reference effect
is viable when having strict lab conditions. When students are studying,
if they can see the material as an elaboration of what they already
remember or can relate to personal experiences, their recall would be
enhanced (Hartlep & Forsyth, 2001). Although, the self-reference
method can enhance recall of memory in certain instances, unfortunately
for this study, there were no significant differences between the two
study methods.
Serbun, Shih and Gutchess (2011) performed a study involving the
effects of general and specific memory when using the self-reference
effect. The study created a gap in research due to the experiments being
tested. The first experiment uses visuals details of objects where the
second and third experiment use verbal memory to assess the
self-reference effect. The self-reference effect enhances both general
and specific memory and can improve the accuracy and richness of a
memory (Serbun et al., 2011). We know how the self-reference effect
works, but instead of using trait adjectives to assess recall, we are
looking at trait adjectives. The results from the experiments show that
self-referencing does not function only through the increase in
familiarity or general memory for the object, but enhances memory for
details of an event. This likely draws on more recollected processes.
This information supports that self-referencing is effective of encoding
a rich, detailed memory towards not only general memory, but specific
memories.
(Nakao et al., 2012) performed a study to show the relation
between the self-reference effect and people that are highly in altruism
and low in altruism. This all starts with the medical prefrontal
cortex (MPFC). People who are high in altruism did not show the
self-reference effect compared to the participants low in altruism. The
participants who frequently chose the altruistic behavior refer to the
social desirability as a backboard (Nakoa et al., 2012). The relation
the self-reference effect and altruism is the MPFC. When using the
self-reference effect, people who are low in altruism, the same part of
the brain is being used. Whereas the same is for people who are high in
altruism when using social desirability. Social desirability ties into
the different types of memory enhancement can vary for individual
differences of past experiences. People's individual differences can
show similar effects as the self-reference effect (Nakoa et al., 2012).
The self-reference effect is a rich and powerful encoding process
that can be used multiple ways. The self-reference effect shows better
results over the semantic method when processing personal information.
Processing personal information can be distinguished and recalled
differently with age. The older the subject, the more rich and vivid
the memory can be due to the amount of information the brain has
processed. The self-reference just as effective as the SQR4 method when
study for exams, but the self-reference method is preferred. Defining
general and specific memories using objects, verbal cues, etc. can be
effective when using the self-reference effect. When using these
different method, the same part of the brain is being active resulting
in relation and better recall. It was expected that participants would
recall the most number of words from the self-referent list rather than
from the semantic or structural lists and more words from the semantic
list than from the structural list. It was also expected that for the
words encoded in the self-referent condition, fewer words would be
recalled by participants in the high altruism group than in the low
altruism group.
Evolutional Mechanism
Research suggests that the self-reference effect is connected to personal survival among the human race.
There is this survival effect which is defined as the enhancement of
memory when encoding material meant for survival, which has shown to
have a significant correlation with the self-reference effect.
The interesting thing is research has found that this memory
enhancement does not work when given by another person, in order for it
to work, it must come from the person themselves.
As this advancement of encoding incoming memories is an evolutional
mechanism that we the human race has inherited from the challenges faced
by our ancestors.
Nairne et al. (2007) noted that our advanced ability to recall past
events may be to help us as a species to solve issues, which would
relate to survival.
Weinstein et al. (2008) concluded in their study that people are able
to encode and retrieve information that is related to survival more than
information that doesn't relate to survival.
However, it is important to note that researchers theorize that there
is not just one kind of self-reference effect that people pose, rather a
group of them for different purposes other than survival.
Examples
The tendency to attribute someone else's behavior to their disposition, and to attribute one's own behavior to the situation. (The Fundamental attribution error)
When asked to remember words relating to themselves, subjects had greater recall than those receiving other instructions.
In connection with the levels-of-processing effect, more processing and more connections are made within the mind in relation to a topic connected to the self.
In the field of marketing, Asian consumers self-referenced Asian models in advertising more than White consumers.
Also Asian models advertising products that were not typically endorsed
by Asian models resulted in more self-referencing from consumers.
People are more likely to remember birthdays that are closer to their own birthday than birthdays that are more distant.
Research shows that long term memory is improved when learning occurs under self-reference conditions
Research shows that female consumers engage in self-referencing when
viewing female models of different body shapes in advertising. For
example, Martin, Veer and Pervan (2007) examined how the weight locus of
control of women (i.e., beliefs about the control of body weight)
influence how they react to female models in advertising of different
body shapes. They found that women who believe they can control their
weight ("internals"), respond most favorably to slim models in
advertising, and this favorable response is mediated by
self-referencing.
In humans, the cerebral cortex contains approximately 14–16 billion neurons, and the estimated number of neurons in the cerebellum is 55–70 billion. Each neuron is connected by synapses to several thousand other neurons, typically communicating with one another via root-like protrusions called dendrites and long fiber-like extensions called axons, which are usually myelinated and carry trains of rapid micro-electric signal pulses called action potentials to target specific recipient cells in other areas of the brain or distant parts of the body. The prefrontal cortex, which controls executive functions, is particularly well developed in humans.
Physiologically,
brains exert centralized control over a body's other organs. They act
on the rest of the body both by generating patterns of muscle activity
and by driving the secretion of chemicals called hormones. This centralized control allows rapid and coordinated responses to changes in the environment. Some basic types of responsiveness such as reflexes can be mediated by the spinal cord or peripheral ganglia,
but sophisticated purposeful control of behavior based on complex
sensory input requires the information integrating capabilities of a
centralized brain.
The operations of individual brain cells are now understood in
considerable detail but the way they cooperate in ensembles of millions
is yet to be solved. Recent models in modern neuroscience treat the brain as a biological computer, very different in mechanism from a digital computer,
but similar in the sense that it acquires information from the
surrounding world, stores it, and processes it in a variety of ways.
This article compares the properties of brains across the entire
range of animal species, with the greatest attention to vertebrates. It
deals with the human brain
insofar as it shares the properties of other brains. The ways in which
the human brain differs from other brains are covered in the human brain
article. Several topics that might be covered here are instead covered
there because much more can be said about them in a human context. The
most important that are covered in the human brain article are brain disease and the effects of brain damage.
Anatomy
Cross section of the olfactory bulb
of a rat, stained in two different ways at the same time: one stain
shows neuron cell bodies, the other shows receptors for the neurotransmitterGABA.
The shape and size of the brain varies greatly between species, and identifying common features is often difficult. Nevertheless, there are a number of principles of brain architecture that apply across a wide range of species. Some aspects of brain structure are common to almost the entire range of animal species; others distinguish "advanced" brains from more primitive ones, or distinguish vertebrates from invertebrates.
The simplest way to gain information about brain anatomy is by
visual inspection, but many more sophisticated techniques have been
developed. Brain tissue in its natural state is too soft to work with,
but it can be hardened by immersion in alcohol or other fixatives, and then sliced apart for examination of the interior. Visually, the interior of the brain consists of areas of so-called grey matter, with a dark color, separated by areas of white matter,
with a lighter color. Further information can be gained by staining
slices of brain tissue with a variety of chemicals that bring out areas
where specific types of molecules are present in high concentrations. It
is also possible to examine the microstructure of brain tissue using a microscope, and to trace the pattern of connections from one brain area to another.
Cellular structure
Neurons generate electrical signals that travel along their axons. When a pulse of electricity reaches a junction called a synapse,
it causes a neurotransmitter chemical to be released, which binds to
receptors on other cells and thereby alters their electrical activity.
The brains of all species are composed primarily of two broad classes of cells: neurons and glial cells. Glial cells (also known as glia or neuroglia)
come in several types, and perform a number of critical functions,
including structural support, metabolic support, insulation, and
guidance of development. Neurons, however, are usually considered the
most important cells in the brain.
The property that makes neurons unique is their ability to send signals to specific target cells over long distances.
They send these signals by means of an axon, which is a thin
protoplasmic fiber that extends from the cell body and projects, usually
with numerous branches, to other areas, sometimes nearby, sometimes in
distant parts of the brain or body. The length of an axon can be
extraordinary: for example, if a pyramidal cell
(an excitatory neuron) of the cerebral cortex were magnified so that
its cell body became the size of a human body, its axon, equally
magnified, would become a cable a few centimeters in diameter, extending
more than a kilometer.
These axons transmit signals in the form of electrochemical pulses
called action potentials, which last less than a thousandth of a second
and travel along the axon at speeds of 1–100 meters per second. Some
neurons emit action potentials constantly, at rates of 10–100 per
second, usually in irregular patterns; other neurons are quiet most of
the time, but occasionally emit a burst of action potentials.
Axons transmit signals to other neurons by means of specialized junctions called synapses. A single axon may make as many as several thousand synaptic connections with other cells. When an action potential, traveling along an axon, arrives at a synapse, it causes a chemical called a neurotransmitter to be released. The neurotransmitter binds to receptor molecules in the membrane of the target cell.
Synapses are the key functional elements of the brain. The essential function of the brain is cell-to-cell communication,
and synapses are the points at which communication occurs. The human
brain has been estimated to contain approximately 100 trillion synapses; even the brain of a fruit fly contains several million.
The functions of these synapses are very diverse: some are excitatory
(exciting the target cell); others are inhibitory; others work by
activating second messenger systems that change the internal chemistry of their target cells in complex ways.
A large number of synapses are dynamically modifiable; that is, they
are capable of changing strength in a way that is controlled by the
patterns of signals that pass through them. It is widely believed that activity-dependent modification of synapses is the brain's primary mechanism for learning and memory.
Most of the space in the brain is taken up by axons, which are often bundled together in what are called nerve fiber tracts. A myelinated axon is wrapped in a fatty insulating sheath of myelin,
which serves to greatly increase the speed of signal propagation.
(There are also unmyelinated axons). Myelin is white, making parts of
the brain filled exclusively with nerve fibers appear as light-colored white matter, in contrast to the darker-colored grey matter that marks areas with high densities of neuron cell bodies.
Nervous
system of a generic bilaterian animal, in the form of a nerve cord with
segmental enlargements, and a "brain" at the front
Except for a few primitive organisms such as sponges (which have no nervous system) and cnidarians (which have a diffuse nervous system consisting of a nerve net), all living multicellular animals are bilaterians, meaning animals with a bilaterally symmetricbody plan (that is, left and right sides that are approximate mirror images of each other). All bilaterians are thought to have descended from a common ancestor that appeared late in the Cryogenian
period, 700–650 million years ago, and it has been hypothesized that
this common ancestor had the shape of a simple tubeworm with a segmented
body.
At a schematic level, that basic worm-shape continues to be reflected
in the body and nervous system architecture of all modern bilaterians,
including vertebrates.
The fundamental bilateral body form is a tube with a hollow gut cavity
running from the mouth to the anus, and a nerve cord with an enlargement
(a ganglion)
for each body segment, with an especially large ganglion at the front,
called the brain. The brain is small and simple in some species, such as
nematode worms; in other species, such as vertebrates, it is a large and very complex organ. Some types of worms, such as leeches, also have an enlarged ganglion at the back end of the nerve cord, known as a "tail brain".
There are a few types of existing bilaterians that lack a recognizable brain, including echinoderms and tunicates. It has not been definitively established whether the existence of these brainless species indicates that the earliest bilaterians
lacked a brain, or whether their ancestors evolved in a way that led to
the disappearance of a previously existing brain structure.
Invertebrates
Fruit flies (Drosophila) have been extensively studied to gain insight into the role of genes in brain development.
This category includes tardigrades, arthropods, molluscs, and numerous types of worms. The diversity of invertebrate body plans is matched by an equal diversity in brain structures.
Two groups of invertebrates have notably complex brains: arthropods (insects, crustaceans, arachnids, and others), and cephalopods (octopuses, squids, and similar molluscs).
The brains of arthropods and cephalopods arise from twin parallel nerve
cords that extend through the body of the animal. Arthropods have a
central brain, the supraesophageal ganglion, with three divisions and large optical lobes behind each eye for visual processing. Cephalopods such as the octopus and squid have the largest brains of any invertebrates.
There are several invertebrate species whose brains have been
studied intensively because they have properties that make them
convenient for experimental work:
Fruit flies (Drosophila), because of the large array of techniques available for studying their genetics, have been a natural subject for studying the role of genes in brain development. In spite of the large evolutionary distance between insects and mammals, many aspects of Drosophilaneurogenetics have been shown to be relevant to humans. The first biological clock genes, for example, were identified by examining Drosophila mutants that showed disrupted daily activity cycles.
A search in the genomes of vertebrates revealed a set of analogous
genes, which were found to play similar roles in the mouse biological
clock—and therefore almost certainly in the human biological clock as
well. Studies done on Drosophila, also show that most neuropil regions of the brain are continuously reorganized throughout life in response to specific living conditions.
The nematode worm Caenorhabditis elegans, like Drosophila, has been studied largely because of its importance in genetics. In the early 1970s, Sydney Brenner chose it as a model organism
for studying the way that genes control development. One of the
advantages of working with this worm is that the body plan is very
stereotyped: the nervous system of the hermaphrodite contains exactly 302 neurons, always in the same places, making identical synaptic connections in every worm.
Brenner's team sliced worms into thousands of ultrathin sections and
photographed each one under an electron microscope, then visually
matched fibers from section to section, to map out every neuron and
synapse in the entire body. The complete neuronal wiring diagram of C.elegans – its connectome was achieved.
Nothing approaching this level of detail is available for any other
organism, and the information gained has enabled a multitude of studies
that would otherwise have not been possible.
The sea slug Aplysia californica was chosen by Nobel Prize-winning neurophysiologist Eric Kandel as a model for studying the cellular basis of learning and memory, because of the simplicity and accessibility of its nervous system, and it has been examined in hundreds of experiments.
The first vertebrates appeared over 500 million years ago (Mya), during the Cambrian period, and may have resembled the modern hagfish in form.
Jawed fish appeared by 445 Mya, amphibians by 350 Mya, reptiles by 310
Mya and mammals by 200 Mya (approximately). Each species has an equally
long evolutionary history, but the brains of modern hagfishes, lampreys, sharks,
amphibians, reptiles, and mammals show a gradient of size and
complexity that roughly follows the evolutionary sequence. All of these
brains contain the same set of basic anatomical components, but many are
rudimentary in the hagfish, whereas in mammals the foremost part (the telencephalon) is greatly elaborated and expanded.
Brains are most commonly compared in terms of their size. The relationship between brain size,
body size and other variables has been studied across a wide range of
vertebrate species. As a rule, brain size increases with body size, but
not in a simple linear proportion. In general, smaller animals tend to
have larger brains, measured as a fraction of body size. For mammals,
the relationship between brain volume and body mass essentially follows a
power law with an exponent of about 0.75.
This formula describes the central tendency, but every family of
mammals departs from it to some degree, in a way that reflects in part
the complexity of their behavior. For example, primates have brains 5 to
10 times larger than the formula predicts. Predators tend to have
larger brains than their prey, relative to body size.
The main subdivisions of the embryonic vertebrate brain (left), which later differentiate into structures of the adult brain (right)
All vertebrate brains share a common underlying form, which appears
most clearly during early stages of embryonic development. In its
earliest form, the brain appears as three swellings at the front end of
the neural tube; these swellings eventually become the forebrain, midbrain, and hindbrain (the prosencephalon, mesencephalon, and rhombencephalon,
respectively). At the earliest stages of brain development, the three
areas are roughly equal in size. In many classes of vertebrates, such as
fish and amphibians, the three parts remain similar in size in the
adult, but in mammals the forebrain becomes much larger than the other
parts, and the midbrain becomes very small.
The brains of vertebrates are made of very soft tissue.
Living brain tissue is pinkish on the outside and mostly white on the
inside, with subtle variations in color. Vertebrate brains are
surrounded by a system of connective tissuemembranes called meninges that separate the skull from the brain. Blood vessels
enter the central nervous system through holes in the meningeal layers.
The cells in the blood vessel walls are joined tightly to one another,
forming the blood–brain barrier, which blocks the passage of many toxins and pathogens (though at the same time blocking antibodies and some drugs, thereby presenting special challenges in treatment of diseases of the brain).
Neuroanatomists usually divide the vertebrate brain into six main regions: the telencephalon (cerebral hemispheres), diencephalon (thalamus and hypothalamus), mesencephalon (midbrain), cerebellum, pons, and medulla oblongata.
Each of these areas has a complex internal structure. Some parts, such
as the cerebral cortex and the cerebellar cortex, consist of layers that
are folded or convoluted to fit within the available space. Other
parts, such as the thalamus and hypothalamus, consist of clusters of
many small nuclei. Thousands of distinguishable areas can be identified
within the vertebrate brain based on fine distinctions of neural
structure, chemistry, and connectivity.
The
main anatomical regions of the vertebrate brain, shown for shark and
human. The same parts are present, but they differ greatly in size and
shape.
Although the same basic components are present in all vertebrate
brains, some branches of vertebrate evolution have led to substantial
distortions of brain geometry, especially in the forebrain area. The
brain of a shark shows the basic components in a straightforward way,
but in teleost
fishes (the great majority of existing fish species), the forebrain has
become "everted", like a sock turned inside out. In birds, there are
also major changes in forebrain structure. These distortions can make it difficult to match brain components from one species with those of another species.
Here is a list of some of the most important vertebrate brain
components, along with a brief description of their functions as
currently understood:
The medulla,
along with the spinal cord, contains many small nuclei involved in a
wide variety of sensory and involuntary motor functions such as
vomiting, heart rate and digestive processes.
The pons
lies in the brainstem directly above the medulla. Among other things,
it contains nuclei that control often voluntary but simple acts such as
sleep, respiration, swallowing, bladder function, equilibrium, eye
movement, facial expressions, and posture.
The hypothalamus
is a small region at the base of the forebrain, whose complexity and
importance belies its size. It is composed of numerous small nuclei,
each with distinct connections and neurochemistry. The hypothalamus is
engaged in additional involuntary or partially voluntary acts such as
sleep and wake cycles, eating and drinking, and the release of some
hormones.
The thalamus
is a collection of nuclei with diverse functions: some are involved in
relaying information to and from the cerebral hemispheres, while others
are involved in motivation. The subthalamic area (zona incerta)
seems to contain action-generating systems for several types of
"consummatory" behaviors such as eating, drinking, defecation, and
copulation.
The cerebellum
modulates the outputs of other brain systems, whether motor-related or
thought related, to make them certain and precise. Removal of the
cerebellum does not prevent an animal from doing anything in particular,
but it makes actions hesitant and clumsy. This precision is not
built-in but learned by trial and error. The muscle coordination learned
while riding a bicycle is an example of a type of neural plasticity that may take place largely within the cerebellum. 10% of the brain's total volume consists of the cerebellum and 50% of all neurons are held within its structure.
The optic tectum
allows actions to be directed toward points in space, most commonly in
response to visual input. In mammals, it is usually referred to as the superior colliculus,
and its best-studied function is to direct eye movements. It also
directs reaching movements and other object-directed actions. It
receives strong visual inputs, but also inputs from other senses that
are useful in directing actions, such as auditory input in owls and
input from the thermosensitive pit organs in snakes. In some primitive fishes, such as lampreys, this region is the largest part of the brain. The superior colliculus is part of the midbrain.
The pallium
is a layer of grey matter that lies on the surface of the forebrain and
is the most complex and most recent evolutionary development of the
brain as an organ. In reptiles and mammals, it is called the cerebral cortex. Multiple functions involve the pallium, including smell and spatial memory.
In mammals, where it becomes so large as to dominate the brain, it
takes over functions from many other brain areas. In many mammals, the
cerebral cortex consists of folded bulges called gyri that create deep furrows or fissures called sulci.
The folds increase the surface area of the cortex and therefore
increase the amount of gray matter and the amount of information that
can be stored and processed.
The hippocampus,
strictly speaking, is found only in mammals. However, the area it
derives from, the medial pallium, has counterparts in all vertebrates.
There is evidence that this part of the brain is involved in complex
events such as spatial memory and navigation in fishes, birds, reptiles,
and mammals.
The basal ganglia are a group of interconnected structures in the forebrain. The primary function of the basal ganglia appears to be action selection:
they send inhibitory signals to all parts of the brain that can
generate motor behaviors, and in the right circumstances can release the
inhibition, so that the action-generating systems are able to execute
their actions. Reward and punishment exert their most important neural
effects by altering connections within the basal ganglia.
The olfactory bulb
is a special structure that processes olfactory sensory signals and
sends its output to the olfactory part of the pallium. It is a major
brain component in many vertebrates, but is greatly reduced in humans
and other primates (whose senses are dominated by information acquired
by sight rather than smell).
Reptiles
Anatomical comparison between the brain of a lizard (A and C) and the brain of a turkey (B and D). Abbreviations: Olf,
olfactory lobes; Hmp, cerebral hemispheres; Pn, pineal gland ; Mb,
optic lobes of the middle brain ; Cb, cerebellum; MO, medulla oblongata;
ii, optic nerves; iv and vi, nerves for the muscles of the eye; Py,
pituitary body.Comparison of Vertebrate Brains: Mammalian, Reptilian, Amphibian, Teleost, and Ammocoetes. CB.,
cerebellum; PT., pituitary body; PN., pineal body; C. STR., corpus
striatum; G.H.R., right ganglion habenulæ. I., olfactory; II., optic
nerves.
Modern reptiles and mammals diverged from a common ancestor around 320 million years ago.
Interestingly, the number of extant reptiles far exceeds the number of
mammalian species, with 11,733 recognized species of reptiles compared to 5,884 extant mammals. Along with the species diversity, reptiles have diverged in terms of external morphology, from limbless to tetrapod gliders to armored chelonians, reflecting adaptive radiation to a diverse array of environments.
Morphological differences are reflected in the nervous system phenotype,
such as: absence of lateral motor column neurons in snakes, which
innervate limb muscles controlling limb movements; absence of motor
neurons that innervate trunk muscles in tortoises; presence of
innervation from the trigeminal nerve to pit organs responsible to infrared detection in snakes. Variation in size, weight, and shape of the brain can be found within reptiles.
For instance, crocodilians have the largest brain volume to body weight
proportion, followed by turtles, lizards, and snakes. Reptiles vary in
the investment in different brain sections. Crocodilians have the
largest telencephalon, while snakes have the smallest. Turtles have the
largest diencephalon per body weight whereas crocodilians have the
smallest. On the other hand, lizards have the largest mesencephalon.
Yet their brains share several characteristics revealed by recent anatomical, molecular, and ontogenetic studies. Vertebrates share the highest levels of similarities during embryological development, controlled by conservedtranscription factors and signaling centers, including gene expression, morphological and cell type differentiation.
In fact, high levels of transcriptional factors can be found in all
areas of the brain in reptiles and mammals, with shared neuronal
clusters enlightening brain evolution.
Conserved transcription factors elucidate that evolution acted in
different areas of the brain by either retaining similar morphology and
function, or diversifying it.
Anatomically, the reptilian brain has less subdivisions than the
mammalian brain, however it has numerous conserved aspects including the
organization of the spinal cord and cranial nerve, as well as
elaborated brain pattern of organization.
Elaborated brains are characterized by migrated neuronal cell bodies
away from the periventricular matrix, region of neuronal development,
forming organized nuclear groups. Aside from reptiles and mammals, other vertebrates with elaborated brains include hagfish, galeomorph sharks, skates, rays, teleosts, and birds. Overall elaborated brains are subdivided in forebrain, midbrain, and hindbrain.
The hindbrain coordinates and integrates sensory and motor inputs
and outputs responsible for, but not limited to, walking, swimming, or
flying. It contains input and output axons interconnecting the spinal
cord, midbrain and forebrain transmitting information from the external
and internal environments.
The midbrain links sensory, motor, and integrative components received
from the hindbrain, connecting it to the forebrain. The tectum, which
includes the optic tectum and torus semicircularis, receives auditory,
visual, and somatosensory inputs, forming integrated maps of the sensory
and visual space around the animal.
The tegmentum receives incoming sensory information and forwards motor
responses to and from the forebrain. The isthmus connects the hindbrain
with midbrain. The forebrain region is particularly well developed, is
further divided into diencephalon and telencephalon. Diencephalon is
related to regulation of eye and body movement in response to visual
stimuli, sensory information, circadian rhythms, olfactory input, and autonomic nervous system.Telencephalon
is related to control of movements, neurotransmitters and
neuromodulators responsible for integrating inputs and transmitting
outputs are present, sensory systems, and cognitive functions.
The bird brain is divided into a number of sections, each with a different function. The cerebrum or telencephalon is divided into two hemispheres, and controls higher functions. The telencephalon is dominated by a large pallium, which corresponds to the mammaliancerebral cortex and is responsible for the cognitive functions
of birds. The pallium is made up of several major structures: the
hyperpallium, a dorsal bulge of the pallium found only in birds, as well
as the nidopallium, mesopallium, and archipallium. The bird
telencephalon nuclear structure, wherein neurons are distributed in
three-dimensionally arranged clusters, with no large-scale separation of
white matter and grey matter, though there exist layer-like and column-like connections. Structures in the pallium are associated with perception, learning, and cognition. Beneath the pallium are the two components of the subpallium, the striatum and pallidum.
The subpallium connects different parts of the telencephalon and plays
major roles in a number of critical behaviours. To the rear of the
telencephalon are the thalamus, midbrain, and cerebellum. The hindbrain connects the rest of the brain to the spinal cord.
The size and structure of the avian brain enables prominent behaviours of birds such as flight and vocalization. Dedicated structures and pathways integrate the auditory and visual senses, strong in most species of birds, as well as the typically weaker olfactory and tactile senses. Social behaviour,
widespread among birds, depends on the organisation and functions of
the brain. Some birds exhibit strong abilities of cognition, enabled by
the unique structure and physiology of the avian brain.
Mammals
The most obvious difference between the brains of mammals and other
vertebrates is in terms of size. On average, a mammal has a brain
roughly twice as large as that of a bird of the same body size, and ten
times as large as that of a reptile of the same body size.
Size, however, is not the only difference: there are also
substantial differences in shape. The hindbrain and midbrain of mammals
are generally similar to those of other vertebrates, but dramatic
differences appear in the forebrain, which is greatly enlarged and also
altered in structure.
The cerebral cortex is the part of the brain that most strongly
distinguishes mammals. In non-mammalian vertebrates, the surface of the cerebrum is lined with a comparatively simple three-layered structure called the pallium. In mammals, the pallium evolves into a complex six-layered structure called neocortex or isocortex. Several areas at the edge of the neocortex, including the hippocampus and amygdala, are also much more extensively developed in mammals than in other vertebrates.
The elaboration of the cerebral cortex carries with it changes to other brain areas. The superior colliculus,
which plays a major role in visual control of behavior in most
vertebrates, shrinks to a small size in mammals, and many of its
functions are taken over by visual areas of the cerebral cortex. The cerebellum of mammals contains a large portion (the neocerebellum) dedicated to supporting the cerebral cortex, which has no counterpart in other vertebrates.
The brains of humans and other primates contain the same structures as the brains of other mammals, but are generally larger in proportion to body size. The encephalization quotient (EQ) is used to compare brain sizes across species. It takes into account the nonlinearity of the brain-to-body relationship.
Humans have an average EQ in the 7-to-8 range, while most other
primates have an EQ in the 2-to-3 range. Dolphins have values higher
than those of primates other than humans, but nearly all other mammals have EQ values that are substantially lower.
Most of the enlargement of the primate brain comes from a massive expansion of the cerebral cortex, especially the prefrontal cortex and the parts of the cortex involved in vision.
The visual processing network of primates includes at least 30
distinguishable brain areas, with a complex web of interconnections. It
has been estimated that visual processing areas occupy more than half of
the total surface of the primate neocortex. The prefrontal cortex carries out functions that include planning, working memory, motivation, attention, and executive control.
It takes up a much larger proportion of the brain for primates than for
other species, and an especially large fraction of the human brain.
Brain of a human embryo in the sixth week of development
The brain develops in an intricately orchestrated sequence of stages.
It changes in shape from a simple swelling at the front of the nerve
cord in the earliest embryonic stages, to a complex array of areas and
connections. Neurons are created in special zones that contain stem cells,
and then migrate through the tissue to reach their ultimate locations.
Once neurons have positioned themselves, their axons sprout and navigate
through the brain, branching and extending as they go, until the tips
reach their targets and form synaptic connections. In a number of parts
of the nervous system, neurons and synapses are produced in excessive
numbers during the early stages, and then the unneeded ones are pruned
away.
For vertebrates, the early stages of neural development are similar across all species. As the embryo transforms from a round blob of cells into a wormlike structure, a narrow strip of ectoderm running along the midline of the back is induced to become the neural plate, the precursor of the nervous system. The neural plate folds inward to form the neural groove, and then the lips that line the groove merge to enclose the neural tube,
a hollow cord of cells with a fluid-filled ventricle at the center. At
the front end, the ventricles and cord swell to form three vesicles that
are the precursors of the prosencephalon (forebrain), mesencephalon (midbrain), and rhombencephalon (hindbrain). At the next stage, the forebrain splits into two vesicles called the telencephalon (which will contain the cerebral cortex, basal ganglia, and related structures) and the diencephalon (which will contain the thalamus and hypothalamus). At about the same time, the hindbrain splits into the metencephalon (which will contain the cerebellum and pons) and the myelencephalon (which will contain the medulla oblongata).
Each of these areas contains proliferative zones where neurons and
glial cells are generated; the resulting cells then migrate, sometimes
for long distances, to their final positions.
Once a neuron is in place, it extends dendrites and an axon into
the area around it. Axons, because they commonly extend a great distance
from the cell body and need to reach specific targets, grow in a
particularly complex way. The tip of a growing axon consists of a blob
of protoplasm called a growth cone,
studded with chemical receptors. These receptors sense the local
environment, causing the growth cone to be attracted or repelled by
various cellular elements, and thus to be pulled in a particular
direction at each point along its path. The result of this pathfinding
process is that the growth cone navigates through the brain until it
reaches its destination area, where other chemical cues cause it to
begin generating synapses. Considering the entire brain, thousands of genes create products that influence axonal pathfinding.
The synaptic network that finally emerges is only partly
determined by genes, though. In many parts of the brain, axons initially
"overgrow", and then are "pruned" by mechanisms that depend on neural
activity.
In the projection from the eye to the midbrain, for example, the
structure in the adult contains a very precise mapping, connecting each
point on the surface of the retina
to a corresponding point in a midbrain layer. In the first stages of
development, each axon from the retina is guided to the right general
vicinity in the midbrain by chemical cues, but then branches very
profusely and makes initial contact with a wide swath of midbrain
neurons. The retina, before birth, contains special mechanisms that
cause it to generate waves of activity that originate spontaneously at a
random point and then propagate slowly across the retinal layer. These
waves are useful because they cause neighboring neurons to be active at
the same time; that is, they produce a neural activity pattern that
contains information about the spatial arrangement of the neurons. This
information is exploited in the midbrain by a mechanism that causes
synapses to weaken, and eventually vanish, if activity in an axon is not
followed by activity of the target cell. The result of this
sophisticated process is a gradual tuning and tightening of the map,
leaving it finally in its precise adult form.
Similar things happen in other brain areas: an initial synaptic
matrix is generated as a result of genetically determined chemical
guidance, but then gradually refined by activity-dependent mechanisms,
partly driven by internal dynamics, partly by external sensory inputs.
In some cases, as with the retina-midbrain system, activity patterns
depend on mechanisms that operate only in the developing brain, and
apparently exist solely to guide development.
In humans and many other mammals, new neurons are created mainly
before birth, and the infant brain contains substantially more neurons
than the adult brain. There are, however, a few areas where new neurons continue to be generated throughout life. The two areas for which adult neurogenesis is well established are the olfactory bulb, which is involved in the sense of smell, and the dentate gyrus
of the hippocampus, where there is evidence that the new neurons play a
role in storing newly acquired memories. With these exceptions,
however, the set of neurons that is present in early childhood is the
set that is present for life. Glial cells are different: as with most
types of cells in the body, they are generated throughout the lifespan.
There has long been debate about whether the qualities of mind, personality, and intelligence can be attributed to heredity or to upbringing.
Although many details remain to be settled, neuroscience shows that
both factors are important. Genes determine both the general form of the
brain and how it reacts to experience, but experience is required to
refine the matrix of synaptic connections, resulting in greatly
increased complexity. The presence or absence of experience is critical
at key periods of development. Additionally, the quantity and quality of experience are important. For example, animals raised in enriched environments
demonstrate thick cerebral cortices, indicating a high density of
synaptic connections, compared to animals with restricted levels of
stimulation.
Physiology
The functions of the brain depend on the ability of neurons to
transmit electrochemical signals to other cells, and their ability to
respond appropriately to electrochemical signals received from other
cells. The electrical properties
of neurons are controlled by a wide variety of biochemical and
metabolic processes, most notably the interactions between
neurotransmitters and receptors that take place at synapses.
Neurotransmitters and receptors
Neurotransmitters are chemicals that are released at synapses when the local membrane is depolarised and Ca2+
enters into the cell, typically when an action potential arrives at the
synapse – neurotransmitters attach themselves to receptor molecules on
the membrane of the synapse's target cell (or cells), and thereby alter
the electrical or chemical properties of the receptor molecules. With
few exceptions, each neuron in the brain releases the same chemical
neurotransmitter, or combination of neurotransmitters, at all the
synaptic connections it makes with other neurons; this rule is known as Dale's principle. Thus, a neuron can be characterized by the neurotransmitters that it releases. The great majority of psychoactive drugs exert their effects by altering specific neurotransmitter systems. This applies to drugs such as cannabinoids, nicotine, heroin, cocaine, alcohol, fluoxetine, chlorpromazine, and many others.
The two neurotransmitters that are most widely found in the vertebrate brain are glutamate, which almost always exerts excitatory effects on target neurons, and gamma-aminobutyric acid (GABA), which is almost always inhibitory. Neurons using these transmitters can be found in nearly every part of the brain. Because of their ubiquity, drugs that act on glutamate or GABA tend to have broad and powerful effects. Some general anesthetics act by reducing the effects of glutamate; most tranquilizers exert their sedative effects by enhancing the effects of GABA.
There are dozens of other chemical neurotransmitters that are
used in more limited areas of the brain, often areas dedicated to a
particular function. Serotonin, for example—the primary target of many antidepressant drugs and many dietary aids—comes exclusively from a small brainstem area called the raphe nuclei. Norepinephrine, which is involved in arousal, comes exclusively from a nearby small area called the locus coeruleus. Other neurotransmitters such as acetylcholine and dopamine have multiple sources in the brain but are not as ubiquitously distributed as glutamate and GABA.
Electrical activity
Brain electrical activity recorded from a human patient during an epileptic seizure
As a side effect of the electrochemical processes used by neurons for
signaling, brain tissue generates electric fields when it is active.
When large numbers of neurons show synchronized activity, the electric
fields that they generate can be large enough to detect outside the
skull, using electroencephalography (EEG) or magnetoencephalography
(MEG). EEG recordings, along with recordings made from electrodes
implanted inside the brains of animals such as rats, show that the brain
of a living animal is constantly active, even during sleep.
Each part of the brain shows a mixture of rhythmic and nonrhythmic
activity, which may vary according to behavioral state. In mammals, the
cerebral cortex tends to show large slow delta waves during sleep, faster alpha waves
when the animal is awake but inattentive, and chaotic-looking irregular
activity when the animal is actively engaged in a task, called beta and gamma waves. During an epileptic seizure,
the brain's inhibitory control mechanisms fail to function and
electrical activity rises to pathological levels, producing EEG traces
that show large wave and spike patterns not seen in a healthy brain.
Relating these population-level patterns to the computational functions
of individual neurons is a major focus of current research in neurophysiology.
Metabolism
All vertebrates have a blood–brain barrier that allows metabolism inside the brain to operate differently from metabolism in other parts of the body. The neurovascular unit regulates cerebral blood flow so that activated neurons can be supplied with energy. Glial cells
play a major role in brain metabolism by controlling the chemical
composition of the fluid that surrounds neurons, including levels of
ions and nutrients.
Brain tissue consumes a large amount of energy in proportion to
its volume, so large brains place severe metabolic demands on animals.
The need to limit body weight in order, for example, to fly, has
apparently led to selection for a reduction of brain size in some
species, such as bats. Most of the brain's energy consumption goes into sustaining the electric charge (membrane potential) of neurons.
Most vertebrate species devote between 2% and 8% of basal metabolism to
the brain. In primates, however, the percentage is much higher—in
humans it rises to 20–25%.
The energy consumption of the brain does not vary greatly over time,
but active regions of the cerebral cortex consume somewhat more energy
than inactive regions; this forms the basis for the functional brain
imaging methods of PET, fMRI, and NIRS. The brain typically gets most of its energy from oxygen-dependent metabolism of glucose (i.e., blood sugar), but ketones provide a major alternative source, together with contributions from medium chain fatty acids (caprylic and heptanoic acids), lactate, acetate, and possibly amino acids.
Function
Model of a neural circuit in the cerebellum, as proposed by James S. Albus
Information from the sense organs is collected in the brain. There it
is used to determine what actions the organism is to take. The brain processes
the raw data to extract information about the structure of the
environment. Next it combines the processed information with information
about the current needs of the animal and with memory of past
circumstances. Finally, on the basis of the results, it generates motor
response patterns. These signal-processing tasks require intricate
interplay between a variety of functional subsystems.
The function of the brain is to provide coherent control over the
actions of an animal. A centralized brain allows groups of muscles to
be co-activated in complex patterns; it also allows stimuli impinging on
one part of the body to evoke responses in other parts, and it can
prevent different parts of the body from acting at cross-purposes to
each other.
The human brain is provided with information about light, sound, the
chemical composition of the atmosphere, temperature, the position of the
body in space (proprioception), the chemical composition of the bloodstream, and more. In other animals additional senses are present, such as the infrared heat-sense of snakes, the magnetic field sense of some birds, or the electric field sense mainly seen in aquatic animals.
Each sensory system begins with specialized receptor cells, such as photoreceptor cells in the retina of the eye, or vibration-sensitive hair cells in the cochlea of the ear. The axons of sensory receptor cells travel into the spinal cord or brain, where they transmit their signals to a first-order sensory nucleus dedicated to one specific sensory modality.
This primary sensory nucleus sends information to higher-order sensory
areas that are dedicated to the same modality. Eventually, via a
way-station in the thalamus, the signals are sent to the cerebral cortex, where they are processed to extract the relevant features, and integrated with signals coming from other sensory systems.
Motor control
Motor systems are areas of the brain that are involved in initiating body movements,
that is, in activating muscles. Except for the muscles that control the
eye, which are driven by nuclei in the midbrain, all the voluntary
muscles in the body are directly innervated by motor neurons in the spinal cord and hindbrain.
Spinal motor neurons are controlled both by neural circuits intrinsic
to the spinal cord, and by inputs that descend from the brain. The
intrinsic spinal circuits implement many reflex responses, and contain pattern generators for rhythmic movements such as walking or swimming. The descending connections from the brain allow for more sophisticated control.
The brain contains several motor areas that project directly to
the spinal cord. At the lowest level are motor areas in the medulla and
pons, which control stereotyped movements such as walking, breathing, or swallowing. At a higher level are areas in the midbrain, such as the red nucleus, which is responsible for coordinating movements of the arms and legs. At a higher level yet is the primary motor cortex,
a strip of tissue located at the posterior edge of the frontal lobe.
The primary motor cortex sends projections to the subcortical motor
areas, but also sends a massive projection directly to the spinal cord,
through the pyramidal tract.
This direct corticospinal projection allows for precise voluntary
control of the fine details of movements. Other motor-related brain
areas exert secondary effects by projecting to the primary motor areas.
Among the most important secondary areas are the premotor cortex, supplementary motor area, basal ganglia, and cerebellum. In addition to all of the above, the brain and spinal cord contain extensive circuitry to control the autonomic nervous system which controls the movement of the smooth muscle of the body.
Many animals alternate between sleeping and waking in a daily cycle.
Arousal and alertness are also modulated on a finer time scale by a
network of brain areas. A key component of the sleep system is the suprachiasmatic nucleus (SCN), a tiny part of the hypothalamus located directly above the point at which the optic nerves
from the two eyes cross. The SCN contains the body's central biological
clock. Neurons there show activity levels that rise and fall with a
period of about 24 hours, circadian rhythms:
these activity fluctuations are driven by rhythmic changes in
expression of a set of "clock genes". The SCN continues to keep time
even if it is excised from the brain and placed in a dish of warm
nutrient solution, but it ordinarily receives input from the optic
nerves, through the retinohypothalamic tract (RHT), that allows daily light-dark cycles to calibrate the clock.
The SCN projects to a set of areas in the hypothalamus,
brainstem, and midbrain that are involved in implementing sleep-wake
cycles. An important component of the system is the reticular formation,
a group of neuron-clusters scattered diffusely through the core of the
lower brain. Reticular neurons send signals to the thalamus, which in
turn sends activity-level-controlling signals to every part of the
cortex. Damage to the reticular formation can produce a permanent state
of coma.
Sleep involves great changes in brain activity. Until the 1950s it was generally believed that the brain essentially shuts off during sleep,
but this is now known to be far from true; activity continues, but
patterns become very different. There are two types of sleep: REM sleep (with dreaming) and NREM
(non-REM, usually without dreaming) sleep, which repeat in slightly
varying patterns throughout a sleep episode. Three broad types of
distinct brain activity patterns can be measured: REM, light NREM and
deep NREM. During deep NREM sleep, also called slow wave sleep,
activity in the cortex takes the form of large synchronized waves,
whereas in the waking state it is noisy and desynchronized. Levels of
the neurotransmitters norepinephrine and serotonin drop during slow wave sleep, and fall almost to zero during REM sleep; levels of acetylcholine show the reverse pattern.
Homeostasis
Cross-section of a human head, showing location of the hypothalamus
For any animal, survival requires maintaining a variety of parameters
of bodily state within a limited range of variation: these include
temperature, water content, salt concentration in the bloodstream, blood
glucose levels, blood oxygen level, and others. The ability of an animal to regulate the internal environment of its body—the milieu intérieur, as the pioneering physiologist Claude Bernard called it—is known as homeostasis (Greek for "standing still"). Maintaining homeostasis is a crucial function of the brain. The basic principle that underlies homeostasis is negative feedback:
any time a parameter diverges from its set-point, sensors generate an
error signal that evokes a response that causes the parameter to shift
back toward its optimum value. (This principle is widely used in engineering, for example in the control of temperature using a thermostat.)
In vertebrates, the part of the brain that plays the greatest role is the hypothalamus, a small region at the base of the forebrain whose size does not reflect its complexity or the importance of its function.
The hypothalamus is a collection of small nuclei, most of which are
involved in basic biological functions. Some of these functions relate
to arousal or to social interactions such as sexuality, aggression, or
maternal behaviors; but many of them relate to homeostasis. Several
hypothalamic nuclei receive input from sensors located in the lining of
blood vessels, conveying information about temperature, sodium level,
glucose level, blood oxygen level, and other parameters. These
hypothalamic nuclei send output signals to motor areas that can generate
actions to rectify deficiencies. Some of the outputs also go to the pituitary gland,
a tiny gland attached to the brain directly underneath the
hypothalamus. The pituitary gland secretes hormones into the
bloodstream, where they circulate throughout the body and induce changes
in cellular activity.
The individual animals need to express survival-promoting behaviors, such as seeking food, water, shelter, and a mate.
The motivational system in the brain monitors the current state of
satisfaction of these goals, and activates behaviors to meet any needs
that arise. The motivational system works largely by a reward–punishment
mechanism. When a particular behavior is followed by favorable
consequences, the reward mechanism
in the brain is activated, which induces structural changes inside the
brain that cause the same behavior to be repeated later, whenever a
similar situation arises. Conversely, when a behavior is followed by
unfavorable consequences, the brain's punishment mechanism is activated,
inducing structural changes that cause the behavior to be suppressed
when similar situations arise in the future.
Most organisms studied to date use a reward–punishment mechanism:
for instance, worms and insects can alter their behavior to seek food
sources or to avoid dangers.
In vertebrates, the reward-punishment system is implemented by a
specific set of brain structures, at the heart of which lie the basal
ganglia, a set of interconnected areas at the base of the forebrain.
The basal ganglia are the central site at which decisions are made: the
basal ganglia exert a sustained inhibitory control over most of the
motor systems in the brain; when this inhibition is released, a motor
system is permitted to execute the action it is programmed to carry out.
Rewards and punishments function by altering the relationship between
the inputs that the basal ganglia receive and the decision-signals that
are emitted. The reward mechanism is better understood than the
punishment mechanism, because its role in drug abuse has caused it to be
studied very intensively. Research has shown that the neurotransmitter
dopamine plays a central role: addictive drugs such as cocaine,
amphetamine, and nicotine either cause dopamine levels to rise or cause
the effects of dopamine inside the brain to be enhanced.
Learning and memory
Almost all animals are capable of modifying their behavior as a
result of experience—even the most primitive types of worms. Because
behavior is driven by brain activity, changes in behavior must somehow
correspond to changes inside the brain. Already in the late 19th century
theorists like Santiago Ramón y Cajal
argued that the most plausible explanation is that learning and memory
are expressed as changes in the synaptic connections between neurons. Until 1970, however, experimental evidence to support the synaptic plasticity hypothesis was lacking. In 1971 Tim Bliss and Terje Lømo published a paper on a phenomenon now called long-term potentiation: the paper showed clear evidence of activity-induced synaptic changes that lasted for at least several days.
Since then technical advances have made these sorts of experiments much
easier to carry out, and thousands of studies have been made that have
clarified the mechanism of synaptic change, and uncovered other types of
activity-driven synaptic change in a variety of brain areas, including
the cerebral cortex, hippocampus, basal ganglia, and cerebellum. Brain-derived neurotrophic factor (BDNF) and physical activity appear to play a beneficial role in the process.
Neuroscientists currently distinguish several types of learning and memory that are implemented by the brain in distinct ways:
Working memory
is the ability of the brain to maintain a temporary representation of
information about the task that an animal is currently engaged in. This
sort of dynamic memory is thought to be mediated by the formation of cell assemblies—groups of activated neurons that maintain their activity by constantly stimulating one another.
Episodic memory
is the ability to remember the details of specific events. This sort of
memory can last for a lifetime. Much evidence implicates the
hippocampus in playing a crucial role: people with severe damage to the
hippocampus sometimes show amnesia, that is, inability to form new long-lasting episodic memories.
Semantic memory
is the ability to learn facts and relationships. This sort of memory is
probably stored largely in the cerebral cortex, mediated by changes in
connections between cells that represent specific types of information.
Instrumental learning
is the ability for rewards and punishments to modify behavior. It is
implemented by a network of brain areas centered on the basal ganglia.
Motor learning
is the ability to refine patterns of body movement by practicing, or
more generally by repetition. A number of brain areas are involved,
including the premotor cortex,
basal ganglia, and especially the cerebellum, which functions as a
large memory bank for microadjustments of the parameters of movement.
"Brain research" redirects here. For the scientific journal, see Brain Research.
The Human Brain Project is a large scientific research project, starting in 2013, which aims to simulate the complete human brain.
The field of neuroscience encompasses all approaches that seek to understand the brain and the rest of the nervous system. Psychology seeks to understand mind and behavior, and neurology
is the medical discipline that diagnoses and treats diseases of the
nervous system. The brain is also the most important organ studied in psychiatry, the branch of medicine that works to study, prevent, and treat mental disorders. Cognitive science seeks to unify neuroscience and psychology with other fields that concern themselves with the brain, such as computer science (artificial intelligence and similar fields) and philosophy.
The oldest method of studying the brain is anatomical,
and until the middle of the 20th century, much of the progress in
neuroscience came from the development of better cell stains and better
microscopes. Neuroanatomists study the large-scale structure of the
brain as well as the microscopic structure of neurons and their
components, especially synapses. Among other tools, they employ a
plethora of stains that reveal neural structure, chemistry, and
connectivity. In recent years, the development of immunostaining techniques has allowed investigation of neurons that express specific sets of genes. Also, functional neuroanatomy uses medical imaging techniques to correlate variations in human brain structure with differences in cognition or behavior.
Neurophysiologists study the chemical, pharmacological, and
electrical properties of the brain: their primary tools are drugs and
recording devices. Thousands of experimentally developed drugs affect
the nervous system, some in highly specific ways. Recordings of brain
activity can be made using electrodes, either glued to the scalp as in EEG studies, or implanted inside the brains of animals for extracellular recordings, which can detect action potentials generated by individual neurons.
Because the brain does not contain pain receptors, it is possible using
these techniques to record brain activity from animals that are awake
and behaving without causing distress. The same techniques have
occasionally been used to study brain activity in human patients with
intractable epilepsy, in cases where there was a medical necessity to implant electrodes to localize the brain area responsible for epileptic seizures. Functional imaging techniques such as fMRI
are also used to study brain activity; these techniques have mainly
been used with human subjects, because they require a conscious subject
to remain motionless for long periods of time, but they have the great
advantage of being noninvasive.
Design of an experiment in which brain activity from a monkey was used to control a robotic arm
Another approach to brain function is to examine the consequences of damage to specific brain areas. Even though it is protected by the skull and meninges, surrounded by cerebrospinal fluid,
and isolated from the bloodstream by the blood–brain barrier, the
delicate nature of the brain makes it vulnerable to numerous diseases
and several types of damage. In humans, the effects of strokes and other
types of brain damage have been a key source of information about brain
function. Because there is no ability to experimentally control the
nature of the damage, however, this information is often difficult to
interpret. In animal studies, most commonly involving rats, it is
possible to use electrodes or locally injected chemicals to produce
precise patterns of damage and then examine the consequences for
behavior.
Computational neuroscience
encompasses two approaches: first, the use of computers to study the
brain; second, the study of how brains perform computation. On one hand,
it is possible to write a computer program to simulate the operation of
a group of neurons by making use of systems of equations that describe
their electrochemical activity; such simulations are known as biologically realistic neural networks.
On the other hand, it is possible to study algorithms for neural
computation by simulating, or mathematically analyzing, the operations
of simplified "units" that have some of the properties of neurons but
abstract out much of their biological complexity. The computational
functions of the brain are studied both by computer scientists and
neuroscientists.
Computational neurogenetic modeling
is concerned with the study and development of dynamic neuronal models
for modeling brain functions with respect to genes and dynamic
interactions between genes.
Recent years have seen increasing applications of genetic and genomic techniques to the study of the brain and a focus on the roles of neurotrophic factors and physical activity in neuroplasticity.
The most common subjects are mice, because of the availability of
technical tools. It is now possible with relative ease to "knock out" or
mutate a wide variety of genes, and then examine the effects on brain
function. More sophisticated approaches are also being used: for
example, using Cre-Lox recombination it is possible to activate or deactivate genes in specific parts of the brain, at specific times.
Illustration by René Descartes of how the brain implements a reflex response
The oldest brain to have been discovered was in Armenia in the Areni-1 cave complex.
The brain, estimated to be over 5,000 years old, was found in the skull
of a 12 to 14-year-old girl. Although the brains were shriveled, they
were well preserved due to the climate found inside the cave.
Early philosophers were divided as to whether the seat of the soul lies in the brain or heart. Aristotle favored the heart, and thought that the function of the brain was merely to cool the blood. Democritus,
the inventor of the atomic theory of matter, argued for a three-part
soul, with intellect in the head, emotion in the heart, and lust near
the liver. The unknown author of On the Sacred Disease, a medical treatise in the Hippocratic Corpus, came down unequivocally in favor of the brain, writing:
Men ought to know that from nothing
else but the brain come joys, delights, laughter and sports, and
sorrows, griefs, despondency, and lamentations. ... And by the same
organ we become mad and delirious, and fears and terrors assail us, some
by night, and some by day, and dreams and untimely wanderings, and
cares that are not suitable, and ignorance of present circumstances,
desuetude, and unskillfulness. All these things we endure from the
brain, when it is not healthy...
— On the Sacred Disease, attributed to Hippocrates
The Roman physician Galen
also argued for the importance of the brain, and theorized in some
depth about how it might work. Galen traced out the anatomical
relationships among brain, nerves, and muscles, demonstrating that all
muscles in the body are connected to the brain through a branching
network of nerves. He postulated that nerves activate muscles
mechanically by carrying a mysterious substance he called pneumata psychikon, usually translated as "animal spirits".
Galen's ideas were widely known during the Middle Ages, but not much
further progress came until the Renaissance, when detailed anatomical
study resumed, combined with the theoretical speculations of René Descartes
and those who followed him. Descartes, like Galen, thought of the
nervous system in hydraulic terms. He believed that the highest
cognitive functions are carried out by a non-physical res cogitans, but that the majority of behaviors of humans, and all behaviors of animals, could be explained mechanistically.
The first real progress toward a modern understanding of nervous function, though, came from the investigations of Luigi Galvani
(1737–1798), who discovered that a shock of static electricity applied
to an exposed nerve of a dead frog could cause its leg to contract.
Since that time, each major advance in understanding has followed more
or less directly from the development of a new technique of
investigation. Until the early years of the 20th century, the most
important advances were derived from new methods for staining cells. Particularly critical was the invention of the Golgi stain,
which (when correctly used) stains only a small fraction of neurons,
but stains them in their entirety, including cell body, dendrites, and
axon. Without such a stain, brain tissue under a microscope appears as
an impenetrable tangle of protoplasmic fibers, in which it is impossible
to determine any structure. In the hands of Camillo Golgi, and especially of the Spanish neuroanatomist Santiago Ramón y Cajal,
the new stain revealed hundreds of distinct types of neurons, each with
its own unique dendritic structure and pattern of connectivity.
Drawing by Santiago Ramón y Cajal of two types of Golgi-stained neurons from the cerebellum of a pigeon
In the first half of the 20th century, advances in electronics
enabled investigation of the electrical properties of nerve cells,
culminating in work by Alan Hodgkin, Andrew Huxley, and others on the biophysics of the action potential, and the work of Bernard Katz and others on the electrochemistry of the synapse.
These studies complemented the anatomical picture with a conception of
the brain as a dynamic entity. Reflecting the new understanding, in 1942
Charles Sherrington visualized the workings of the brain waking from sleep:
The great topmost sheet of the
mass, that where hardly a light had twinkled or moved, becomes now a
sparkling field of rhythmic flashing points with trains of traveling
sparks hurrying hither and thither. The brain is waking and with it the
mind is returning. It is as if the Milky Way entered upon some cosmic
dance. Swiftly the head mass becomes an enchanted loom where millions of
flashing shuttles weave a dissolving pattern, always a meaningful
pattern though never an abiding one; a shifting harmony of subpatterns.
— Sherrington, 1942, Man on his Nature
The invention of electronic computers in the 1940s, along with the development of mathematical information theory,
led to a realization that brains can potentially be understood as
information processing systems. This concept formed the basis of the
field of cybernetics, and eventually gave rise to the field now known as computational neuroscience.
The earliest attempts at cybernetics were somewhat crude in that they
treated the brain as essentially a digital computer in disguise, as for
example in John von Neumann's 1958 book, The Computer and the Brain.
Over the years, though, accumulating information about the electrical
responses of brain cells recorded from behaving animals has steadily
moved theoretical concepts in the direction of increasing realism.
One of the most influential early contributions was a 1959 paper titled What the frog's eye tells the frog's brain: the paper examined the visual responses of neurons in the retina and optic tectum
of frogs, and came to the conclusion that some neurons in the tectum of
the frog are wired to combine elementary responses in a way that makes
them function as "bug perceivers". A few years later David Hubel and Torsten Wiesel
discovered cells in the primary visual cortex of monkeys that become
active when sharp edges move across specific points in the field of
view—a discovery for which they won a Nobel Prize. Follow-up studies in higher-order visual areas found cells that detect binocular disparity,
color, movement, and aspects of shape, with areas located at increasing
distances from the primary visual cortex showing increasingly complex
responses.
Other investigations of brain areas unrelated to vision have revealed
cells with a wide variety of response correlates, some related to
memory, some to abstract types of cognition such as space.
Theorists have worked to understand these response patterns by constructing mathematical models of neurons and neural networks, which can be simulated using computers.
Some useful models are abstract, focusing on the conceptual structure
of neural algorithms rather than the details of how they are implemented
in the brain; other models attempt to incorporate data about the
biophysical properties of real neurons.
No model on any level is yet considered to be a fully valid description
of brain function, though. The essential difficulty is that
sophisticated computation by neural networks requires distributed
processing in which hundreds or thousands of neurons work
cooperatively—current methods of brain activity recording are only
capable of isolating action potentials from a few dozen neurons at a
time.
Furthermore, even single neurons appear to be complex and capable of performing computations.
So, brain models that do not reflect this are too abstract to be
representative of brain operation; models that do try to capture this
are very computationally expensive and arguably intractable with present
computational resources. However, the Human Brain Project
is trying to build a realistic, detailed computational model of the
entire human brain. The wisdom of this approach has been publicly
contested, with high-profile scientists on both sides of the argument.
In the second half of the 20th century, developments in
chemistry, electron microscopy, genetics, computer science, functional
brain imaging, and other fields progressively opened new windows into
brain structure and function. In the United States, the 1990s were
officially designated as the "Decade of the Brain" to commemorate advances made in brain research, and to promote funding for such research.
In the 21st century, these trends have continued, and several new approaches have come into prominence, including multielectrode recording, which allows the activity of many brain cells to be recorded all at the same time; genetic engineering, which allows molecular components of the brain to be altered experimentally; genomics, which allows variations in brain structure to be correlated with variations in DNA properties and neuroimaging.
The Fore people of Papua New Guinea
are known to eat human brains. In funerary rituals, those close to the
dead would eat the brain of the deceased to create a sense of immortality. A prion disease called kuru has been traced to this.
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