The Dream of Human Life, by unknown artist, based on Michelangelo’s drawing The Dream, c. 1533
The dream argument is the postulation that the act of dreaming provides preliminary evidence that the senses we trust to distinguish reality from illusion
should not be fully trusted, and therefore, any state that is dependent
on our senses should at the very least be carefully examined and
rigorously tested to determine whether it is in fact reality.
Synopsis
While dreaming, one does not normally realize one is dreaming. On more rare occasions, the dream may be contained inside another dream with the very act of realizing that one is dreaming, itself, being only a dream that one is not aware of having. This has led philosophers to wonder whether it is possible for one ever to be certain,
at any given point in time, that one is not in fact dreaming, or
whether indeed it could be possible for one to remain in a perpetual
dream state and never experience the reality of wakefulness at all.
He who dreams of drinking wine may weep when morning
comes; he who dreams of weeping may in the morning go off to hunt. While
he is dreaming he does not know it is a dream, and in his dream he may
even try to interpret a dream. Only after he wakes does he know it was a
dream. And someday there will be a great awakening when we know that
this is all a great dream. Yet the stupid believe they are awake, busily
and brightly assuming they understand things, calling this man ruler,
that one herdsman—how dense! Confucius and you are both dreaming! And
when I say you are dreaming, I am dreaming, too. Words like these will
be labeled the Supreme Swindle. Yet, after ten thousand generations, a
great sage may appear who will know their meaning, and it will still be
as though he appeared with astonishing speed.
The Yogachara philosopher Vasubandhu (4th to 5th century C.E.) referenced the argument in his "Twenty verses on appearance only."
Dreaming provides a springboard for those who question whether our
own reality may be an illusion. The ability of the mind to be tricked
into believing a mentally generated world is the "real world" means at
least one variety of simulated reality is a common, even nightly event.
Those who argue that the world is not simulated must concede that
the mind—at least the sleeping mind—is not itself an entirely reliable
mechanism for attempting to differentiate reality from illusion.
Whatever I have accepted until now
as most true has come to me through my senses. But occasionally I have
found that they have deceived me, and it is unwise to trust completely
those who have deceived us even once.
— René Descartes
Critical discussion
In the past, philosophers John Locke and Thomas Hobbes
have separately attempted to refute Descartes's account of the dream
argument. Locke claimed that you cannot experience pain in dreams.
Various scientific studies conducted within the last few decades
provided evidence against Locke's claim by concluding that pain in
dreams can occur, but on very rare occasions.
Philosopher Ben Springett has said that Locke might respond to this by
stating that the agonizing pain of stepping into a fire is
non-comparable to stepping into a fire in a dream. Hobbes claimed that
dreams are susceptible to absurdity while the waking life is not.
Many contemporary philosophers have attempted to refute dream skepticism in detail (see, e.g., Stone (1984)). Ernest Sosa
(2007) devoted a chapter of a monograph to the topic, in which he
presented a new theory of dreaming and argued that his theory raises a
new argument for skepticism, which he attempted to refute. In A Virtue Epistemology: Apt Belief and Reflective Knowledge, he states: "in dreaming we do not really believe; we only make-believe."
Jonathan Ichikawa (2008) and Nathan Ballantyne & Ian Evans (2010)
have offered critiques of Sosa's proposed solution. Ichikawa argued that
as we cannot tell whether our beliefs in waking life are truly beliefs
and not imaginings, like in a dream, we are still not able to tell
whether we are awake or dreaming.
The dream hypothesis is also used to develop other philosophical concepts, such as Valberg's personal horizon: what this world would be internal to if this were all a dream.
Dream Skepticism
Norman Malcolm
in his monograph "Dreaming" (published in 1959) elaborated on
Wittgenstein's question as to whether it really mattered if people who
tell dreams "really had these images while they slept, or whether it
merely seems so to them on waking". He argues that the sentence "I am
asleep" is a senseless form of words; that dreams cannot exist
independently of the waking impression; and that skepticism based on
dreaming "comes from confusing the historical and dream telling
senses...[of]...the past tense" (page 120). In the chapter: "Do I Know I
Am Awake ?" he argues that we do not have to say: "I know that I am
awake" simply because it would be absurd to deny that one is awake.
Philosopher Daniel Dennett expanded on this idea with his cassette type hypothesis of dreaming.
He conjectured that dreams are not real conscious experiences, and are
instead pseudo-memories that emerge upon awakening from sleep. This
pseudo-memories do not correspond to any real dream experiences, and are
instead strictly fabrications of experiences that never occurred.
Philosopher Jennifer Windt has counter-argued against dream skepticism, drawing on the psychology of lucid dreaming, and has advanced a conceptual framework of dreaming as real imaginative experiences.
"Extraordinary claims require extraordinary evidence" (sometimes shortened to ECREE), also known as the Sagan standard, is an aphorism popularized by science communicator Carl Sagan. He used the phrase in his 1979 book Broca's Brain and the 1980 television program Cosmos. It has been described as fundamental to the scientific method and is regarded as encapsulating the basic principles of scientific skepticism.
The concept is similar to Occam's razor in that both heuristics
prefer simpler explanations of a phenomenon to more complicated ones.
In application, there is some ambiguity regarding when evidence is
deemed sufficiently "extraordinary". It is often invoked to challenge
data and scientific findings, or to criticize pseudoscientific claims.
Some critics have argued that the standard can suppress innovation and
affirm confirmation biases.
Philosopher David Hume characterized the principle in his 1748 essay "Of Miracles". Similar statements were made by figures such as Thomas Jefferson in 1808, Pierre-Simon Laplace in 1814, and Théodore Flournoy
in 1899. The formulation "Extraordinary claims require extraordinary
proof" was used a year prior to Sagan, by scientific skeptic Marcello Truzzi.
Critics state that it is impossible to objectively
define the term "extraordinary" and that measures of "extraordinary
evidence" are completely reliant on subjective evaluation. Ambiguity in
what constitutes "extraordinary" has led to misuse of the aphorism, and it is frequently invoked to discredit research dealing with scientific anomalies or any claim that falls outside the mainstream.
Application
An interesting debate has gone on within the [Federal Communications Commission] between those who think that all doctrines that smell of pseudoscience should be combated and those who believe that each issue should be judged on its own merits, but that the burden of proof
should fall squarely on those who make the proposals. I find myself
very much in the latter camp. I believe that the extraordinary should
certainly be pursued. But extraordinary claims require extraordinary
evidence.
The concept is related to Occam's razor as, according to such a heuristic,
simpler explanations are preferred to more complicated ones. Only in
situations where extraordinary evidence exists would an extraordinary
claim be the simplest explanation. It appears in hypothesis testing where the hypothesis that there is no evidence for the proposed phenomenon, what is known as the "null hypothesis", is preferred. The formal argument involves assigning a stronger Bayesian prior to the acceptance of the null hypothesis as opposed to its rejection.
Origin and precursors
Philosopher David Hume may have been the first to fully describe the principles of the Sagan standard.
Sagan popularized the aphorism in his 1979 book Broca's Brain, and in his 1980 television show Cosmos in reference to claims about extraterrestrials visiting Earth. Sagan had first stated the eponymous standard in a 1977 interview with The Washington Post. However, scientific skeptic Marcello Truzzi used the formulation "Extraordinary claims require extraordinary proof" in an article published by Parapsychology Review in 1975, as well as in a Zetetic Scholar article in 1978. Two 1978 articles quoted physicist Philip Abelson—then the editor of the journal Science—using the same phrasing as Truzzi.
In his 1748 essay "Of Miracles", philosopher David Hume
wrote that if "the fact ... partakes of the extraordinary and the
marvellous ... the evidence ... received a diminution, greater or less,
in proportion as the fact is more or less unusual".
Deming concluded that this was the first complete elucidation of the
standard. Unlike Sagan, Hume defined the nature of "extraordinary": he
wrote that it was a large magnitude of evidence.
Science communicator Carl Sagan
did not describe any concrete or quantitative parameters as to what
constitutes "extraordinary evidence", which raises the issue of whether
the standard can be applied objectively. Academic David Deming
notes that it would be "impossible to base all rational thought and
scientific methodology on an aphorism whose meaning is entirely
subjective". He instead argues that "extraordinary evidence" should be
regarded as a sufficient amount of evidence rather than evidence deemed
of extraordinary quality.
Tressoldi noted that the threshold of evidence is typically decided
through consensus. This problem is less apparent in clinical medicine
and psychology where statistical results can establish the strength of evidence.
Deming also noted that the standard can "suppress innovation and maintain orthodoxy". Others, like Etzel Cardeña, have noted that many scientific discoveries that spurred paradigm shifts
were initially deemed "extraordinary" and likely would not have been so
widely accepted if extraordinary evidence were required. Uniform rejection of extraordinary claims could affirm confirmation biases in subfields. Additionally, there are concerns that, when inconsistently applied, the standard exacerbates racial and gender biases. Psychologist Richard Shiffrin
has argued that the standard should not be used to bar research from
publication but to ascertain what is the best explanation for a
phenomenon. Conversely, mathematical psychologist Eric-Jan Wagenmakers stated that extraordinary claims are often false and their publication "pollutes the literature".
To qualify the publication of such claims, psychologist Suyog
Chandramouli has suggested the inclusion of peer reviewers' opinions on
their plausibility or an attached curation of post-publication peer
evaluations.
Cognitive scientist and AI researcher Ben Goertzel believes that the phrase is utilized as a "rhetorical meme" without critical thought. Philosopher Theodore Schick argued that "extraordinary claims do not require extraordinary evidence" if they provide the most adequate explanation. Moreover, theists and Christian apologists like William Lane Craig have argued that it is unfair to apply the standard to religious miracles
as other improbable claims are often accepted based on limited
testimonial evidence, such as an individual claiming that they won the
lottery.
A model is an informative representation of an object, person, or system. The term originally denoted the plans of a building in late 16th-century English, and derived via French and Italian ultimately from Latin modulus, a measure.
In scholarly research and applied science, a model should not be confused with a theory:
while a model seeks only to represent reality with the purpose of
better understanding or predicting the world, a theory is more ambitious
in that it claims to be an explanation of reality.
Model in specific contexts
As a noun, model has specific meanings in certain fields, derived from its original meaning of "structural design or layout":
Model (art), a person posing for an artist, e.g. a 15th-century criminal representing the biblical Judas in Leonardo da Vinci's painting The Last Supper
Model (person), a person who serves as a template for others to copy, as in a role model, often in the context of advertising commercial products; e.g. the first fashion model, Marie Vernet Worth in 1853, wife of designer Charles Frederick Worth.
Model (organism)
a non-human species that is studied to understand biological phenomena
in other organisms, e.g. a guinea pig starved of vitamin C to study
scurvy, an experiment that would be immoral to conduct on a person
Model (mimicry), a species that is mimicked by another species
Model (logic),
a structure (a set of items, such as natural numbers 1, 2, 3,..., along
with mathematical operations such as addition and multiplication, and
relations, such as ) that satisfies a given system of axioms (basic truisms), i.e. that satisfies the statements of a given theory
Model (CGI), a mathematical representation of any surface of an object in three dimensions via specialized software
Model (MVC), the information-representing internal component of a software, as distinct from its user interface
A physical model (most commonly referred to simply as a model but in this context distinguished from a conceptual model) is a smaller or larger physical representation of an object, person or system. The object being modelled may be small (e.g., an atom) or large (e.g., the Solar System) or life-size (e.g., a fashion model displaying clothes for similarly-built potential customers).
The geometry of the model and the object it represents are often similar in the sense that one is a rescaling of the other. However, in many cases the similarity is only approximate or even intentionally distorted. Sometimes the distortion is systematic, e.g., a fixed scale horizontally and a larger fixed scale vertically when modelling topography to enhance a region's mountains.
An architectural model permits visualization of internal
relationships within the structure or external relationships of the
structure to the environment. Another use is as a toy.
Instrumented physical models are an effective way of investigating fluid flows for engineering design. Physical models are often coupled with computational fluid dynamics
models to optimize the design of equipment and processes. This
includes external flow such as around buildings, vehicles, people, or hydraulic structures. Wind tunnel
and water tunnel testing is often used for these design efforts.
Instrumented physical models can also examine internal flows, for the
design of ductwork systems, pollution control equipment, food processing
machines, and mixing vessels. Transparent flow models are used in this
case to observe the detailed flow phenomenon. These models are scaled
in terms of both geometry and important forces, for example, using Froude number or Reynolds number scaling (see Similitude). In the pre-computer era, the UK economy was modelled with the hydraulic model MONIAC, to predict for example the effect of tax rises on employment.
Water-powered model of the UK economy – MONIAC in the Science Museum, London
Female model demonstrating brassiere for similarly-built potential buyers
NASA wind tunnel with the scale model of an aeroplane
Conceptual model
Weather models use differential equations based on the laws of physics, and a coordinate system which divides the planet into a 3D grid.
A conceptual model is a theoretical representation of a system, e.g. a set of mathematical equations attempting to describe the workings of the atmosphere for the purpose of weather forecasting. It consists of concepts used to help understand or simulate a subject the model represents.
Abstract or conceptual models are central to philosophy of science, as almost every scientific theory effectively embeds some kind of model of the physical or human sphere.
In some sense, a physical model "is always the reification of some
conceptual model; the conceptual model is conceived ahead as the
blueprint of the physical one", which is then constructed as conceived. Thus, the term refers to models that are formed after a conceptualization or generalization process.
Economic model, a theoretical construct representing economic processes
Language model a probabilistic model of a natural language, used for speech recognition, language generation, and information retrieval
Large language models are artificial neural networks used for generative artificial intelligence (AI), e.g. ChatGPT
Mathematical model, a description of a system using mathematical concepts and language
Statistical model,
a mathematical model that usually specifies the relationship between
one or more random variables and other non-random variables
Model (CGI), a mathematical representation of any surface of an object in three dimensions via specialized software
Medical model, a proposed "set of procedures in which all doctors are trained"
Mental model, in psychology, an internal representation of external reality
Model (logic),
a set along with a collection of finitary operations, and relations
that are defined on it, satisfying a given collection of axioms
Model (MVC),
information-representing component of a software, distinct from the
user interface (the "view"), both linked by the "controller" component,
in the context of the model–view–controller software design
Model act, a law drafted centrally to be disseminated and proposed for enactment in multiple independent legislatures
Properties of models, according to general model theory
According to Herbert Stachowiak, a model is characterized by at least three properties:
1. Mapping
A model always is a model of something—it is an image or representation of some natural or artificial, existing or imagined original, where this original itself could be a model.
2. Reduction
In general, a model will not include all attributes that describe
the original but only those that appear relevant to the model's creator
or user.
3. Pragmatism
A model does not relate unambiguously to its original. It is intended to work as a replacement for the original
a) for certain subjects (for whom?)
b) within a certain time range (when?)
c) restricted to certain conceptual or physical actions (what for?).
For example, a street map is a model of the actual streets in a city
(mapping), showing the course of the streets while leaving out, say,
traffic signs and road markings (reduction), made for pedestrians and
vehicle drivers for the purpose of finding one's way in the city
(pragmatism).
Additional properties have been proposed, like extension and distortion as well as validity.
The American philosopher Michael Weisberg differentiates between
concrete and mathematical models and proposes computer simulations
(computational models) as their own class of models.
Integrative neuroscience is the study of neuroscience that works to unify functional organization data to better understand complex structures and behaviors.
The relationship between structure and function, and how the regions
and functions connect to each other. Different parts of the brain
carrying out different tasks, interconnecting to come together allowing
complex behavior.
Integrative neuroscience works to fill gaps in knowledge that can
largely be accomplished with data sharing, to create understanding of
systems, currently being applied to simulation neuroscience: Computer Modeling of the brain that integrates functional groups together.
Overview
The roots of integrative neuroscience originated from the Rashevsky-Rosen school of relational biology
that characterizes functional organization mathematically by
abstracting away the structure (i.e., physics and chemistry). It was
further expanded by Chauvet who introduced hierarchical and functional integration.
Hierarchical integration is structural involving spatiotemporal
dynamic continuity in Euclidean space to bring about functional
organization, viz.
However, functional integration is relational and as such this
requires a topology not restricted to Euclidean space, but rather
occupying vector spaces
This means that for any given functional organization the methods of
functional analysis enable a relational organization to be mapped by the
functional integration, viz.
Thus hierarchical and functional integration entails a "neurobiology
of cognitive semantics" where hierarchical organization is associated
with the neurobiology and relational organization is associated with the
cognitive semantics.
Relational organization throws away the matter; "function dictates
structure", hence material aspects are entailed, while in reductionism
the causal nexus between structure and dynamics entails function that
obviates functional integration because the causal entailment in the
brain of hierarchical integration is absent from the structure.
If integrative neuroscience is studied from the viewpoint of
functional organization of hierarchical levels then it is defined as
causal entailment in the brain of hierarchical integration. If it is
studied from the viewpoint of relational organization then it is defined
as semantic entailment in the brain of functional integration.
It aims to present studies of functional organization of
particular brain systems across scale through hierarchical integration
leading to species-typical behaviors under normal and pathological
states. As such, integrative neuroscience aims for a unified
understanding of brain function across scale.
Spivey's continuity of mind thesis extends integrative neuroscience to the domain of continuity psychology.
Motivation
With data building up, it ends up in its respective specializations with very little overlap.
With the creation of a standardized integrated database of neuroscience
data, lead to statical models that would otherwise not be possible, for
example, understanding and treating psychiatric disorders.
It provides a framework for linking the great diversity of specializations within contemporary neuroscience, including
Clinical observations – evidence that can be gleaned from brain dysfunction
This diversity is inevitable, yet has arguably created a void: neglect of the primary
role of the nervous system in enabling the animal to survive and
prosper. Integrative neuroscience aims to fill this perceived void.
Experimental methods
Identifying
different brain regions through correlation and causal methods, combine
to contribute an overall brain function and location map. Using
different data collected from different methods combine to create a
better interconnected and integrative understanding of the brain.
Correlation
The relationship between brain states and behavioral states.
Observed through spatial and temporal differences. That pin point
places in the brain affected by an action or stimuli, and the timing of
the response. Tools used for this include fMRI and EEG, more information below.
Functional magnetic resonance imaging
Functional magnetic resonance imaging (fMRI) measures blood oxygen dependent response (BOLD), using magnetic resonance to observe blood oxygenated areas. Active areas are associated with increased blood flow, presenting a correlation relationship. The spatial localization of fMRI allows accurate information down to the nuclei and Brodmann areas.
Certain activities such as the visual system are so rapid lasting only
fractions of seconds, while other brain functions can take days or
months such as memory. fMRI measures in the frame of seconds, making it
difficult to measure extremely fast processes.
Electroencephalography
Electroencephalography
(EEG) allows you to see the electrical activity of the brain over time,
can only measure presented stimuli responses, stimuli the experimenter
presents. it uses electrode sensors places on the surface on the skull
to measure synchronous neuron firing. It can not be certain activity is
caused by stimuli only a correlation between a given function and brain
area. EEG measures overall changes in wide regions, lacking specificity.
Causal
Brain activity is directly caused by stimulation of a specific region, as proven through experimentation.
TMS
TMS (Transcranial magnetic stimulation)
uses a magnetic coil releasing a burst of magnetic field that activated
activity in a specific brain area. It is useful in exciting a specific
area in the cortex and recording the MEPs (Motor Evoked Potentials) that
occurs as a result.
It gives certain causal relationships, but is limited to the cortex
making it impossible to reach any deeper than the surface of the brain.
Lesions studies
When
patients have natural lesions, it is an opportunity to watch how a
lesion in a given region affects functionality. Or in non-human
experimentation, lesions can be created by removing sections of the
brain. These methods are not reversible, unlike brain studying
techniques, and does not accurately show what that section of the brain
are disabled due to the disruption of homeostasis in the brain. With a
permeate lesion, the brain chemically adjusted and restores homeostasis.
Relying on natural occurrences has little control over variables such
as location and size. And in cases with damage in multiple areas,
differentiation is not certain with lack of mass data.
Electrode stimulation
Cortical Stimulation Mapping, invasive brain surgery that probes at area of the cortex to relate different regions to function.
Typically occurs during open brain surgery where electrodes are
inserted in areas and observations are made. This method is limited by
number of patients having open brain surgery that consent to such
experimentation, and to what area of the brain is being operated on.
Also performed in mice with full range over the brain.
Applications
Human Brain Project
Since
the 'decade of the brain' there has been an explosion of insights into
the brain and their application in most areas of medicine. With this
explosion, the need for integration of data across studies, modalities
and levels of understanding is increasingly recognized. A concrete
exemplar of the value of large-scale data sharing has been provided by
the Human Brain Project.
Medical
The importance of large-scale integration of brain information for new approaches to medicine has been recognized.
Rather than relying mainly on symptom information, a combination of
brain and gene information may ultimately be required for understanding
what treatment is best suited to which individual person.
Behavioral
There
is also work studying empathy and social behavior trends to better
understand how empathy plays a role in behavioral science, and how the
brain responds to empathy, produces empathy, and develops empathy over
time. Combining these functional units and the social behavior and
impact work to create a better understanding of the complex behaviors
that create the human experience.
The scope of neuroscience has broadened over time to include
different approaches used to study the nervous system at different
scales. The techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain.
The earliest study of the nervous system dates to ancient Egypt. Trepanation, the surgical practice of either drilling or scraping a hole into the skull for the purpose of curing head injuries or mental disorders, or relieving cranial pressure, was first recorded during the Neolithic period. Manuscripts dating to 1700 BC indicate that the Egyptians had some knowledge about symptoms of brain damage.
Early views on the function of the brain regarded it to be a "cranial stuffing" of sorts. In Egypt, from the late Middle Kingdom onwards, the brain was regularly removed in preparation for mummification. It was believed at the time that the heart was the seat of intelligence. According to Herodotus,
the first step of mummification was to "take a crooked piece of iron,
and with it draw out the brain through the nostrils, thus getting rid of
a portion, while the skull is cleared of the rest by rinsing with drugs."
The view that the heart was the source of consciousness was not challenged until the time of the Greek physician Hippocrates.
He believed that the brain was not only involved with sensation—since
most specialized organs (e.g., eyes, ears, tongue) are located in the
head near the brain—but was also the seat of intelligence. Plato also speculated that the brain was the seat of the rational part of the soul. Aristotle, however, believed the heart was the center of intelligence and that the brain regulated the amount of heat from the heart. This view was generally accepted until the Roman physician Galen, a follower of Hippocrates and physician to Roman gladiators, observed that his patients lost their mental faculties when they had sustained damage to their brains.
The Golgi stain first allowed for the visualization of individual neurons.
Luigi Galvani's pioneering work in the late 1700s set the stage for studying the electrical excitability of muscles and neurons. In 1843 Emil du Bois-Reymond demonstrated the electrical nature of the nerve signal, whose speed Hermann von Helmholtz proceeded to measure, and in 1875 Richard Caton found electrical phenomena in the cerebral hemispheres of rabbits and monkeys. Adolf Beck published in 1890 similar observations of spontaneous electrical activity of the brain of rabbits and dogs. Studies of the brain became more sophisticated after the invention of the microscope and the development of a staining procedure by Camillo Golgi during the late 1890s. The procedure used a silver chromate salt to reveal the intricate structures of individual neurons. His technique was used by Santiago Ramón y Cajal and led to the formation of the neuron doctrine, the hypothesis that the functional unit of the brain is the neuron. Golgi and Ramón y Cajal shared the Nobel Prize in Physiology or Medicine in 1906 for their extensive observations, descriptions, and categorizations of neurons throughout the brain.
In parallel with this research, in 1815 Jean Pierre Flourens
induced localized lesions of the brain in living animals to observe
their effects on motricity, sensibility and behavior. Work with
brain-damaged patients by Marc Dax in 1836 and Paul Broca
in 1865 suggested that certain regions of the brain were responsible
for certain functions. At the time, these findings were seen as a
confirmation of Franz Joseph Gall's theory that language was localized and that certain psychological functions were localized in specific areas of the cerebral cortex. The localization of function hypothesis was supported by observations of epileptic patients conducted by John Hughlings Jackson, who correctly inferred the organization of the motor cortex by watching the progression of seizures through the body. Carl Wernicke
further developed the theory of the specialization of specific brain
structures in language comprehension and production. Modern research
through neuroimaging techniques, still uses the Brodmanncerebral cytoarchitectonic map (referring to the study of cell structure)
anatomical definitions from this era in continuing to show that
distinct areas of the cortex are activated in the execution of specific
tasks.
During the 20th century, neuroscience began to be recognized as a
distinct academic discipline in its own right, rather than as studies
of the nervous system within other disciplines. Eric Kandel and collaborators have cited David Rioch, Francis O. Schmitt, and Stephen Kuffler as having played critical roles in establishing the field. Rioch originated the integration of basic anatomical and physiological research with clinical psychiatry at the Walter Reed Army Institute of Research,
starting in the 1950s. During the same period, Schmitt established a
neuroscience research program within the Biology Department at the Massachusetts Institute of Technology,
bringing together biology, chemistry, physics, and mathematics. The
first freestanding neuroscience department (then called Psychobiology)
was founded in 1964 at the University of California, Irvine by James L. McGaugh. This was followed by the Department of Neurobiology at Harvard Medical School, which was founded in 1966 by Stephen Kuffler.
In the process of treating epilepsy, Wilder Penfield produced maps of the location of various functions (motor, sensory, memory, vision) in the brain. He summarized his findings in a 1950 book called The Cerebral Cortex of Man. Wilder Penfield and his co-investigators Edwin Boldrey and Theodore Rasmussen are considered to be the originators of the cortical homunculus.
The understanding of neurons and of nervous system function
became increasingly precise and molecular during the 20th century. For
example, in 1952, Alan Lloyd Hodgkin and Andrew Huxley presented a mathematical model for the transmission of electrical signals in neurons of the giant axon of a squid, which they called "action potentials", and how they are initiated and propagated, known as the Hodgkin–Huxley model. In 1961–1962, Richard FitzHugh and J. Nagumo simplified Hodgkin–Huxley, in what is called the FitzHugh–Nagumo model. In 1962, Bernard Katz modeled neurotransmission across the space between neurons known as synapses.
Beginning in 1966, Eric Kandel and collaborators examined biochemical
changes in neurons associated with learning and memory storage in Aplysia. In 1981 Catherine Morris and Harold Lecar combined these models in the Morris–Lecar model. Such increasingly quantitative work gave rise to numerous biological neuron models and models of neural computation.
Over time, brain research has gone through philosophical,
experimental, and theoretical phases, with work on neural implants and
brain simulation predicted to be important in the future.
The scientific study of the nervous system increased significantly during the second half of the twentieth century, principally due to advances in molecular biology, electrophysiology, and computational neuroscience. This has allowed neuroscientists to study the nervous system in all its aspects: how it is structured, how it works, how it develops, how it malfunctions, and how it can be changed.
For example, it has become possible to understand, in much detail, the complex processes occurring within a single neuron.
Neurons are cells specialized for communication. They are able to
communicate with neurons and other cell types through specialized
junctions called synapses,
at which electrical or electrochemical signals can be transmitted from
one cell to another. Many neurons extrude a long thin filament of axoplasm called an axon,
which may extend to distant parts of the body and are capable of
rapidly carrying electrical signals, influencing the activity of other
neurons, muscles, or glands at their termination points. A nervous system emerges from the assemblage of neurons that are connected to each other in neural circuits, and networks.
The vertebrate nervous system can be split into two parts: the central nervous system (defined as the brain and spinal cord), and the peripheral nervous system. In many species—including all vertebrates—the nervous system is the most complex organ system in the body, with most of the complexity residing in the brain. The human brain
alone contains around one hundred billion neurons and one hundred
trillion synapses; it consists of thousands of distinguishable
substructures, connected to each other in synaptic networks whose
intricacies have only begun to be unraveled. At least one out of three
of the approximately 20,000 genes belonging to the human genome is
expressed mainly in the brain.
Due to the high degree of plasticity of the human brain, the structure of its synapses and their resulting functions change throughout life.
Making sense of the nervous system's dynamic complexity is a
formidable research challenge. Ultimately, neuroscientists would like to
understand every aspect of the nervous system, including how it works,
how it develops, how it malfunctions, and how it can be altered or
repaired. Analysis of the nervous system is therefore performed at
multiple levels, ranging from the molecular and cellular levels to the
systems and cognitive levels. The specific topics that form the main
focus of research change over time, driven by an ever-expanding base of
knowledge and the availability of increasingly sophisticated technical
methods. Improvements in technology have been the primary drivers of
progress. Developments in electron microscopy, computer science, electronics, functional neuroimaging, and genetics and genomics have all been major drivers of progress.
Advances in the classification of brain cells have been enabled by electrophysiological recording, single-cell genetic sequencing, and high-quality microscopy, which have combined into a single method pipeline called patch-sequencing in which all three methods are simultaneously applied using miniature tools.
The efficiency of this method and the large amounts of data that is
generated has allowed researchers to make some general conclusions about
cell types; for example that the human and mouse brain have different
versions of fundamentally the same cell types.
Basic questions addressed in molecular neuroscience include the mechanisms by which neurons express and respond to molecular signals and how axons form complex connectivity patterns. At this level, tools from molecular biology and genetics are used to understand how neurons develop and how genetic changes affect biological functions. The morphology,
molecular identity, and physiological characteristics of neurons and
how they relate to different types of behavior are also of considerable
interest.
Questions addressed in cellular neuroscience include the mechanisms of how neurons process signals physiologically and electrochemically. These questions include how signals are processed by neurites and somas and how neurotransmitters and electrical signals are used to process information in a neuron. Neurites are thin extensions from a neuronal cell body, consisting of dendrites (specialized to receive synaptic inputs from other neurons) and axons (specialized to conduct nerve impulses called action potentials). Somas are the cell bodies of the neurons and contain the nucleus.
Computational neurogenetic modeling
is concerned with the development of dynamic neuronal models for
modeling brain functions with respect to genes and dynamic interactions
between genes, on the cellular level (Computational Neurogenetic
Modeling (CNGM) can also be used to model neural systems).
Proposed organization of motor-semantic neural circuits for action language comprehension. Adapted from Shebani et al. (2013).
Systems neuroscience research centers on the structural and functional architecture of the developing human brain, and the functions of large-scale brain networks,
or functionally-connected systems within the brain. Alongside brain
development, systems neuroscience also focuses on how the structure and
function of the brain enables or restricts the processing of sensory
information, using learned mental models of the world, to motivate behavior.
Questions in systems neuroscience include how neural circuits are formed and used anatomically and physiologically to produce functions such as reflexes, multisensory integration, motor coordination, circadian rhythms, emotional responses, learning, and memory.[52]
In other words, this area of research studies how connections are made
and morphed in the brain, and the effect it has on human sensation,
movement, attention, inhibitory control, decision-making, reasoning,
memory formation, reward, and emotion regulation.
Specific areas of interest for the field include observations of
how the structure of neural circuits effect skill acquisition, how
specialized regions of the brain develop and change (neuroplasticity), and the development of brain atlases, or wiring diagrams of individual developing brains.
Cognitive neuroscience addresses the questions of how psychological functions are produced by neural circuitry. The emergence of powerful new measurement techniques such as neuroimaging (e.g., fMRI, PET, SPECT), EEG, MEG, electrophysiology, optogenetics and human genetic analysis combined with sophisticated experimental techniques from cognitive psychology allows neuroscientists and psychologists
to address abstract questions such as how cognition and emotion are
mapped to specific neural substrates. Although many studies still hold a
reductionist stance looking for the neurobiological basis of cognitive
phenomena, recent research shows that there is an interesting interplay
between neuroscientific findings and conceptual research, soliciting and
integrating both perspectives. For example, neuroscience research on
empathy solicited an interesting interdisciplinary debate involving
philosophy, psychology and psychopathology.
Moreover, the neuroscientific identification of multiple memory systems
related to different brain areas has challenged the idea of memory as a literal reproduction of the past, supporting a view of memory as a generative, constructive and dynamic process.
Questions in computational neuroscience can span a wide range of levels of traditional analysis, such as development, structure, and cognitive functions of the brain. Research in this field utilizes mathematical models, theoretical analysis, and computer simulation to describe and verify biologically plausible neurons and nervous systems. For example, biological neuron models
are mathematical descriptions of spiking neurons which can be used to
describe both the behavior of single neurons as well as the dynamics of neural networks. Computational neuroscience is often referred to as theoretical neuroscience.
Neurology works with diseases of the central and peripheral nervous systems, such as amyotrophic lateral sclerosis (ALS) and stroke, and their medical treatment. Psychiatry focuses on affective, behavioral, cognitive, and perceptual disorders. Anesthesiology focuses on perception of pain, and pharmacologic alteration of consciousness. Neuropathology
focuses upon the classification and underlying pathogenic mechanisms of
central and peripheral nervous system and muscle diseases, with an
emphasis on morphologic, microscopic, and chemically observable
alterations. Neurosurgery and psychosurgery work primarily with surgical treatment of diseases of the central and peripheral nervous systems.
Recently, the boundaries between various specialties have blurred, as they are all influenced by basic research in neuroscience. For example, brain imaging
enables objective biological insight into mental illnesses, which can
lead to faster diagnosis, more accurate prognosis, and improved
monitoring of patient progress over time.
Integrative neuroscience
describes the effort to combine models and information from multiple
levels of research to develop a coherent model of the nervous system.
For example, brain imaging coupled with physiological numerical models
and theories of fundamental mechanisms may shed light on psychiatric
disorders.
Another important area of translational research is brain–computer interfaces
(BCIs), or machines that are able to communicate and influence the
brain. They are currently being researched for their potential to repair
neural systems and restore certain cognitive functions. However, some ethical considerations have to be dealt with before they are accepted.
Major branches
Modern
neuroscience education and research activities can be very roughly
categorized into the following major branches, based on the subject and
scale of the system in examination as well as distinct experimental or
curricular approaches. Individual neuroscientists, however, often work
on questions that span several distinct subfields.
Behavioral neuroscience (also known as biological psychology,
physiological psychology, biopsychology, or psychobiology) is the
application of the principles of biology to the study of genetic,
physiological, and developmental mechanisms of behavior in humans and
non-human animals.
Cultural neuroscience is the study of how cultural values, practices
and beliefs shape and are shaped by the mind, brain and genes across
multiple timescales.
Developmental neuroscience studies the processes that generate,
shape, and reshape the nervous system and seeks to describe the cellular
basis of neural development to address underlying mechanisms.
Neuroinformatics is a discipline within bioinformatics that conducts
the organization of neuroscience data and application of computational
models and analytical tools.
Neuro-ophthalmology is an academically oriented subspecialty that
merges the fields of neurology and ophthalmology, often dealing with
complex systemic diseases that have manifestations in the visual system.
Neurophysiology is the study of the structure and function of the
nervous system, generally using physiological techniques that include
measurement and stimulation with electrodes or optically with ion- or
voltage-sensitive dyes or light-sensitive channels.
Neuropsychology is a discipline that resides under the umbrellas of
both psychology and neuroscience, and is involved in activities in the
arenas of both basic science and applied science. In psychology, it is
most closely associated with biopsychology, clinical psychology, cognitive psychology, and developmental psychology.
In neuroscience, it is most closely associated with the cognitive,
behavioral, social, and affective neuroscience areas. In the applied and
medical domain, it is related to neurology and psychiatry.
Neuropsychopharmacology, an interdisciplinary science related to psychopharmacology and fundamental neuroscience, is the study of the neural mechanisms that drugs act upon to influence behavior.
Paleoneurobiology is a field that combines techniques used in
paleontology and archeology to study brain evolution, especially that of
the human brain.
Social neuroscience is an interdisciplinary field devoted to
understanding how biological systems implement social processes and
behavior, and to using biological concepts and methods to inform and
refine theories of social processes and behavior.
The largest professional neuroscience organization is the Society for Neuroscience
(SFN), which is based in the United States but includes many members
from other countries. Since its founding in 1969 the SFN has grown
steadily: as of 2010 it recorded 40,290 members from 83 countries.
Annual meetings, held each year in a different American city, draw
attendance from researchers, postdoctoral fellows, graduate students,
and undergraduates, as well as educational institutions, funding
agencies, publishers, and hundreds of businesses that supply products
used in research.
Other major organizations devoted to neuroscience include the International Brain Research Organization (IBRO), which holds its meetings in a country from a different part of the world each year, and the Federation of European Neuroscience Societies
(FENS), which holds a meeting in a different European city every two
years. FENS comprises a set of 32 national-level organizations,
including the British Neuroscience Association, the German Neuroscience Society (Neurowissenschaftliche Gesellschaft), and the French Société des Neurosciences. The first National Honor Society in Neuroscience, Nu Rho Psi,
was founded in 2006. Numerous youth neuroscience societies which
support undergraduates, graduates and early career researchers also
exist, such as Simply Neuroscience and Project Encephalon.
In 2013, the BRAIN Initiative was announced in the US. The International Brain Initiative was created in 2017, currently integrated by more than seven national-level brain research initiatives (US, Europe, Allen Institute, Japan, China, Australia, Canada, Korea, and Israel) spanning four continents.
Public education and outreach
In addition to conducting traditional research in laboratory settings, neuroscientists have also been involved in the promotion of awareness and knowledge
about the nervous system among the general public and government
officials. Such promotions have been done by both individual
neuroscientists and large organizations. For example, individual
neuroscientists have promoted neuroscience education among young
students by organizing the International Brain Bee, which is an academic competition for high school or secondary school students worldwide.
In the United States, large organizations such as the Society for
Neuroscience have promoted neuroscience education by developing a primer
called Brain Facts, collaborating with public school teachers to develop Neuroscience Core Concepts for K-12 teachers and students, and cosponsoring a campaign with the Dana Foundation called Brain Awareness Week to increase public awareness about the progress and benefits of brain research. In Canada, the Canadian Institutes of Health Research's (CIHR) Canadian National Brain Bee is held annually at McMaster University.
Neuroscience educators formed a Faculty for Undergraduate
Neuroscience (FUN) in 1992 to share best practices and provide travel
awards for undergraduates presenting at Society for Neuroscience
meetings.
Neuroscientists have also collaborated with other education
experts to study and refine educational techniques to optimize learning
among students, an emerging field called educational neuroscience. Federal agencies in the United States, such as the National Institute of Health (NIH) and National Science Foundation (NSF), have also funded research that pertains to best practices in teaching and learning of neuroscience concepts.
Engineering applications of neuroscience
Neuromorphic computer chips
Neuromorphic engineering is a branch of neuroscience that deals with creating functional physical models
of neurons for the purposes of useful computation. The emergent
computational properties of neuromorphic computers are fundamentally
different from conventional computers in the sense that they are complex systems, and that the computational components are interrelated with no central processor.
One example of such a computer is the SpiNNaker supercomputer.
Sensors can also be made smart with neuromorphic technology. An example of this is the Event Camera's
BrainScaleS (brain-inspired Multiscale Computation in Neuromorphic
Hybrid Systems), a hybrid analog neuromorphic supercomputer located at
Heidelberg University in Germany. It was developed as part of the Human Brain Project's
neuromorphic computing platform and is the complement to the SpiNNaker
supercomputer, which is based on digital technology. The architecture
used in BrainScaleS mimics biological neurons and their connections on a
physical level; additionally, since the components are made of silicon,
these model neurons operate on average 864 times (24 hours of real time
is 100 seconds in the machine simulation) that of their biological
counterparts.
Recent advances in neuromorphic microchip technology have led a group of scientists to create an artificial neuron that can replace real neurons in diseases.
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