Computational neuroscience
Encyclopedia
Computational neuroscience is the study of brain function in terms of the information processing
properties of the structures that make up the nervous system
. It is an interdisciplinary science that links the diverse fields of neuroscience
, cognitive science
and psychology
with electrical engineering
, computer science
, mathematics
and physics
.
Computational neuroscience is somewhat distinct from psychological connectionism
and theories of learning from disciplines such as machine learning
, neural networks
and computational learning theory
in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
, who organized a conference, held in 1985 in Carmel, California at the request of the Systems Development Foundation, to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were later published as the book "Computational Neuroscience" (1990).
The early historical roots of the field can be traced to the work of people such as Louis Lapicque
, Hodgkin & Huxley
, Hubel
& Wiesel
, and David Marr, to name but a few. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907; this model is still one of the most popular models in computational neuroscience for both cellular and neural networks
studies, as well as in mathematical neuroscience because of its simplicity (see the recent review article published recently for the centenary of the original Lapicque's 1907 paper - this review also contains an English translation of the original paper). About 40 years later, Hodgkin & Huxley developed the voltage clamp and created the first biophysical model of the action potential
. Hubel & Wiesel discovered that neurons in the primary visual cortex, the first cortical area to process information coming from the retina
, have oriented receptive fields and are organized in columns. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus
and neocortex
interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall
, with the first multicompartmental model using cable theory
.
only employed two voltage-sensitive currents, the fast-acting sodium and the inward-rectifying potassium. Though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and shunting. Scientists now believe that there are a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations and sensitivity of these currents is an important topic of computational neuroscience.
The computational functions of complex dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.
Some models are also tracking biochemical pathways at very small scales such as spines or synaptic clefts.
There are many software packages, such as GENESIS
and NEURON
, that allow rapid and systematic in silico modeling of realistic neurons. Blue Brain
, a project founded by Henry Markram
from the École Polytechnique Fédérale de Lausanne
, aims to construct a biophysically detailed simulation of a cortical column
on the Blue Gene
supercomputer
.
Theoretical investigations into the formation and patterning of synaptic connection and morphology are still nascent. One hypothesis that has recently garnered some attention is the minimal wiring hypothesis, which postulates that the formation of axons and dendrites effectively minimizes resource allocation while maintaining maximal information storage.
. Somewhat similar to the minimal wiring hypothesis described in the preceding section, Barlow understood the processing of the early sensory systems to be a form of efficient coding
, where the neurons encoded information which minimized the number of spikes. Experimental and computational work have since supported this hypothesis in one form or another.
Current research in sensory processing is divided among biophysical modelling of different subsystems and more theoretical modelling of perception. Current models of perception have suggested that the brain performs some form of Bayesian inference
and integration of different sensory information in generating our perception of the physical world.
have been developed to address the properties of associative, rather than content-addressable style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium-term and long-term memory, localizing in the hippocampus. Models of working memory, relying on theories of network oscillations and persistent activity, have been built to capture some features of the prefrontal cortex in context-related memory.
One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable synapses are easy to train but also prone to stochastic disruption. Stable synapses forget less easily, but they are also harder to consolidate. One recent computational hypothesis involves cascades of plasticity that allow synapses to function at multiple time scales. Stereochemically detailed models of the acetylcholine receptor
-based synapse with Monte Carlo method
, working at the time scale of microseconds, have been built. It is likely that computational tools will contribute greatly to our understanding of how synapses function and change in relation to external stimulus in the coming decades.
The interactions of neurons in a small network can be often reduced to simple models such as the Ising model
. The statistical mechanics
of such simple systems are well-characterized theoretically. There has been some recent evidence that suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions.(Schneidman et al., 2006; Shlens et al., 2006.) It's unknown, however, whether such descriptive dynamics impart any important computational function. With the emergence of two-photon microscopy and calcium imaging
, we now have powerful experimental methods with which to test the new theories regarding neuronal networks.
In some cases the complex interactions between inhibitory and excitatory neurons can be simplified using mean field theory
that gives rise to population model
of neural networks. While many neuro-theorists prefer such models with reduced complexity, others argue that uncovering structure function relations depends on including as much neuronal and network structure as possible. Models of this type are typically built in large simulations platforms like GENESIS
or Neuron
. There have been some attempts to provide unified methods that bridge and integrate these levels of complexity.
in primates. The frontal lobe
and parietal lobe
function as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation.
The brain
seems to be able to discriminate and adapt particularly well in certain contexts. For instance, human beings seem to have an enormous capacity for memorizing and recognizing faces. One of the key goals of computational neuroscience is to dissect how biological systems carry out these complex computations efficiently and potentially replicate these processes in building intelligent machines.
The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice. Integrative neuroscience
attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the basis for some quantitative modeling of large-scale brain activity.
and Christof Koch
made some attempts in formulating a consistent framework for future work in neural correlates of consciousness
(NCC), though much of the work in this field remains speculative.
Information processing
Information processing is the change of information in any manner detectable by an observer. As such, it is a process which describes everything which happens in the universe, from the falling of a rock to the printing of a text file from a digital computer system...
properties of the structures that make up the nervous system
Nervous system
The nervous system is an organ system containing a network of specialized cells called neurons that coordinate the actions of an animal and transmit signals between different parts of its body. In most animals the nervous system consists of two parts, central and peripheral. The central nervous...
. It is an interdisciplinary science that links the diverse fields of neuroscience
Neuroscience
Neuroscience is the scientific study of the nervous system. Traditionally, neuroscience has been seen as a branch of biology. However, it is currently an interdisciplinary science that collaborates with other fields such as chemistry, computer science, engineering, linguistics, mathematics,...
, cognitive science
Cognitive science
Cognitive science is the interdisciplinary scientific study of mind and its processes. It examines what cognition is, what it does and how it works. It includes research on how information is processed , represented, and transformed in behaviour, nervous system or machine...
and psychology
Cognitive psychology
Cognitive psychology is a subdiscipline of psychology exploring internal mental processes.It is the study of how people perceive, remember, think, speak, and solve problems.Cognitive psychology differs from previous psychological approaches in two key ways....
with electrical engineering
Electrical engineering
Electrical engineering is a field of engineering that generally deals with the study and application of electricity, electronics and electromagnetism. The field first became an identifiable occupation in the late nineteenth century after commercialization of the electric telegraph and electrical...
, computer science
Computer science
Computer science or computing science is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems...
, mathematics
Mathematics
Mathematics is the study of quantity, space, structure, and change. Mathematicians seek out patterns and formulate new conjectures. Mathematicians resolve the truth or falsity of conjectures by mathematical proofs, which are arguments sufficient to convince other mathematicians of their validity...
and physics
Physics
Physics is a natural science that involves the study of matter and its motion through spacetime, along with related concepts such as energy and force. More broadly, it is the general analysis of nature, conducted in order to understand how the universe behaves.Physics is one of the oldest academic...
.
Computational neuroscience is somewhat distinct from psychological connectionism
Connectionism
Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units...
and theories of learning from disciplines such as machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
, neural networks
Neural Networks
Neural Networks is the official journal of the three oldest societies dedicated to research in neural networks: International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, published by Elsevier...
and computational learning theory
Computational learning theory
In theoretical computer science, computational learning theory is a mathematical field related to the analysis of machine learning algorithms.-Overview:Theoretical results in machine learning mainly deal with a type of...
in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.
History
The term "computational neuroscience" was introduced by Eric L. SchwartzEric L. Schwartz
Eric L. Schwartz is Professor of Cognitive and Neural Systems, Professor of Electrical and Computer Engineering, and Professor of Anatomy and Neurobiology at Boston University....
, who organized a conference, held in 1985 in Carmel, California at the request of the Systems Development Foundation, to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were later published as the book "Computational Neuroscience" (1990).
The early historical roots of the field can be traced to the work of people such as Louis Lapicque
Louis Lapicque
Louis Lapicque was a French neuroscientist who was very influential in the early 20th century. One of his main contributions was to propose the integrate and fire model of the neuron in a seminal article published in 1907...
, Hodgkin & Huxley
Andrew Huxley
Sir Andrew Fielding Huxley, OM, FRS is an English physiologist and biophysicist, who won the 1963 Nobel Prize in Physiology or Medicine for his experimental and mathematical work with Sir Alan Lloyd Hodgkin on the basis of nerve action potentials, the electrical impulses that enable the activity...
, Hubel
David H. Hubel
David Hunter Hubel is the John Franklin Enders Professor of Neurobiology, Emeritus, at Harvard Medical School. He was co-recipient with Torsten Wiesel of the 1981 Nobel Prize in Physiology or Medicine, for their discoveries concerning information processing in the visual system; the prize was...
& Wiesel
Torsten Wiesel
Torsten Nils Wiesel was a Swedish co-recipient with David H. Hubel of the 1981 Nobel Prize in Physiology or Medicine, for their discoveries concerning information processing in the visual system; the prize was shared with Roger W...
, and David Marr, to name but a few. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907; this model is still one of the most popular models in computational neuroscience for both cellular and neural networks
Neural Networks
Neural Networks is the official journal of the three oldest societies dedicated to research in neural networks: International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, published by Elsevier...
studies, as well as in mathematical neuroscience because of its simplicity (see the recent review article published recently for the centenary of the original Lapicque's 1907 paper - this review also contains an English translation of the original paper). About 40 years later, Hodgkin & Huxley developed the voltage clamp and created the first biophysical model of the action potential
Action potential
In physiology, an action potential is a short-lasting event in which the electrical membrane potential of a cell rapidly rises and falls, following a consistent trajectory. Action potentials occur in several types of animal cells, called excitable cells, which include neurons, muscle cells, and...
. Hubel & Wiesel discovered that neurons in the primary visual cortex, the first cortical area to process information coming from the retina
Retina
The vertebrate retina is a light-sensitive tissue lining the inner surface of the eye. The optics of the eye create an image of the visual world on the retina, which serves much the same function as the film in a camera. Light striking the retina initiates a cascade of chemical and electrical...
, have oriented receptive fields and are organized in columns. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus
Hippocampus
The hippocampus is a major component of the brains of humans and other vertebrates. It belongs to the limbic system and plays important roles in the consolidation of information from short-term memory to long-term memory and spatial navigation. Humans and other mammals have two hippocampi, one in...
and neocortex
Neocortex
The neocortex , also called the neopallium and isocortex , is a part of the brain of mammals. It is the outer layer of the cerebral hemispheres, and made up of six layers, labelled I to VI...
interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall
Wilfrid Rall
Wilfrid Rall is a neuroscientist who spent most of his career at the National Institutes of Health. He is considered one of the founders of computational neuroscience, and was a pioneer in establishing the integrative functions of neuronal dendrites...
, with the first multicompartmental model using cable theory
Cable theory
Classical cable theory uses mathematical models to calculate the flow of electric current along passive neuronal fibers particularly dendrites that receive synaptic inputs at different sites and times...
.
Major topics
Research in computational neuroscience can be roughly categorized into several lines of inquiry. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena.Single-neuron modeling
Even single neurons have complex biophysical characteristics. Hodgkin and Huxley's original modelHodgkin-Huxley model
The Hodgkin–Huxley model is a mathematical model that describes how action potentials in neurons are initiated and propagated....
only employed two voltage-sensitive currents, the fast-acting sodium and the inward-rectifying potassium. Though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and shunting. Scientists now believe that there are a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations and sensitivity of these currents is an important topic of computational neuroscience.
The computational functions of complex dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.
Some models are also tracking biochemical pathways at very small scales such as spines or synaptic clefts.
There are many software packages, such as GENESIS
GENESIS (software)
GENESIS is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including subcellular processes, individual neurons, networks of neurons, and neuronal systems.GENESIS was developed in the Caltech laboratory of Dr. James M...
and NEURON
Neuron (software)
NEURON is a simulation environment for modeling individual neurons and networks of neurons.It was primarily developed by Michael Hines, John W. Moore, and Ted Carnevale at Yale and Duke....
, that allow rapid and systematic in silico modeling of realistic neurons. Blue Brain
Blue Brain
The Blue Brain Project is an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level.The aim of the project, founded in May 2005 by the Brain and Mind Institute of the École Polytechnique Fédérale de Lausanne is to study the brain's architectural...
, a project founded by Henry Markram
Henry Markram
Henry Markram is Director of the Blue Brain Project at École Polytechnique Fédérale de Lausanne .He obtained his B.Sc. from Cape Town University, South Africa under the supervision of Rodney Douglas and his Ph.D. from the Weizmann Institute of Science, Israel, under the supervision of Menahem...
from the École Polytechnique Fédérale de Lausanne
École polytechnique fédérale de Lausanne
The École polytechnique fédérale de Lausanne is one of the two Swiss Federal Institutes of Technology and is located in Lausanne, Switzerland.The school was founded by the Swiss Federal Government with the stated mission to:...
, aims to construct a biophysically detailed simulation of a cortical column
Cortical column
A cortical column, also called hypercolumn or sometimes cortical module, is a group of neurons in the brain cortex which can be successively penetrated by a probe inserted perpendicularly to the cortical surface, and which have nearly identical receptive fields...
on the Blue Gene
Blue Gene
Blue Gene is a computer architecture project to produce several supercomputers, designed to reach operating speeds in the PFLOPS range, and currently reaching sustained speeds of nearly 500 TFLOPS . It is a cooperative project among IBM Blue Gene is a computer architecture project to produce...
supercomputer
Supercomputer
A supercomputer is a computer at the frontline of current processing capacity, particularly speed of calculation.Supercomputers are used for highly calculation-intensive tasks such as problems including quantum physics, weather forecasting, climate research, molecular modeling A supercomputer is a...
.
Development, axonal patterning and guidance
How do axons and dendrites form during development? How do axons know where to target and how to reach these targets? How do neurons migrate to the proper position in the central and peripheral systems? How do synapses form? We know from molecular biology that distinct parts of the nervous system release distinct chemical cues, from growth factors to hormones that modulate and influence the growth and development of functional connections between neurons.Theoretical investigations into the formation and patterning of synaptic connection and morphology are still nascent. One hypothesis that has recently garnered some attention is the minimal wiring hypothesis, which postulates that the formation of axons and dendrites effectively minimizes resource allocation while maintaining maximal information storage.
Sensory processing
Early models of sensory processing understood within a theoretical framework is credited to Horace BarlowHorace Barlow
Horace Basil Barlow FRS is a British visual neuroscientist.Barlow is the son of the civil servant Sir Alan Barlow and his wife Lady Nora, , and thus the great-grandson of Charles Darwin . He earned an M.D...
. Somewhat similar to the minimal wiring hypothesis described in the preceding section, Barlow understood the processing of the early sensory systems to be a form of efficient coding
Efficient coding hypothesis
The efficient coding hypothesis was proposed by Horace Barlow in 1961 as a theoretical model of sensory coding in the brain. Within the brain, neurons often communicate with one another by sending electrical impulses referred to as action potentials or spikes...
, where the neurons encoded information which minimized the number of spikes. Experimental and computational work have since supported this hypothesis in one form or another.
Current research in sensory processing is divided among biophysical modelling of different subsystems and more theoretical modelling of perception. Current models of perception have suggested that the brain performs some form of Bayesian inference
Bayesian inference
In statistics, Bayesian inference is a method of statistical inference. It is often used in science and engineering to determine model parameters, make predictions about unknown variables, and to perform model selection...
and integration of different sensory information in generating our perception of the physical world.
Memory and synaptic plasticity
Earlier models of memory are primarily based on the postulates of Hebbian learning. Biologically relevant models such as Hopfield netHopfield net
A Hopfield network is a form of recurrent artificial neural network invented by John Hopfield. Hopfield nets serve as content-addressable memory systems with binary threshold units. They are guaranteed to converge to a local minimum, but convergence to one of the stored patterns is not guaranteed...
have been developed to address the properties of associative, rather than content-addressable style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium-term and long-term memory, localizing in the hippocampus. Models of working memory, relying on theories of network oscillations and persistent activity, have been built to capture some features of the prefrontal cortex in context-related memory.
One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable synapses are easy to train but also prone to stochastic disruption. Stable synapses forget less easily, but they are also harder to consolidate. One recent computational hypothesis involves cascades of plasticity that allow synapses to function at multiple time scales. Stereochemically detailed models of the acetylcholine receptor
Acetylcholine receptor
An acetylcholine receptor is an integral membrane protein that responds to the binding of acetylcholine, a neurotransmitter.-Classification:...
-based synapse with Monte Carlo method
Monte Carlo method
Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in computer simulations of physical and mathematical systems...
, working at the time scale of microseconds, have been built. It is likely that computational tools will contribute greatly to our understanding of how synapses function and change in relation to external stimulus in the coming decades.
Behaviors of networks
Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most artificial neural networks, sparse and most likely, specific. It is not known how information is transmitted through such sparsely connected networks. It is also unknown what the computational functions, if any, of these specific connectivity patterns are.The interactions of neurons in a small network can be often reduced to simple models such as the Ising model
Ising model
The Ising model is a mathematical model of ferromagnetism in statistical mechanics. The model consists of discrete variables called spins that can be in one of two states . The spins are arranged in a graph , and each spin interacts with its nearest neighbors...
. The statistical mechanics
Statistical mechanics
Statistical mechanics or statistical thermodynamicsThe terms statistical mechanics and statistical thermodynamics are used interchangeably...
of such simple systems are well-characterized theoretically. There has been some recent evidence that suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions.(Schneidman et al., 2006; Shlens et al., 2006.) It's unknown, however, whether such descriptive dynamics impart any important computational function. With the emergence of two-photon microscopy and calcium imaging
Calcium imaging
Calcium imaging is a scientific technique usually carried out in research which is designed to show the calcium status of a tissue or medium....
, we now have powerful experimental methods with which to test the new theories regarding neuronal networks.
In some cases the complex interactions between inhibitory and excitatory neurons can be simplified using mean field theory
Mean field theory
Mean field theory is a method to analyse physical systems with multiple bodies. A many-body system with interactions is generally very difficult to solve exactly, except for extremely simple cases . The n-body system is replaced by a 1-body problem with a chosen good external field...
that gives rise to population model
Wilson-Cowan model
In computational neuroscience, the Wilson-Cowan model describes the dynamics of interactions between populations of very simple excitatory and inhibitory model neurons. It was developed by Hugh R. Wilson and Jack D. Cowan and extensions of the model have been widely used in modeling neuronal...
of neural networks. While many neuro-theorists prefer such models with reduced complexity, others argue that uncovering structure function relations depends on including as much neuronal and network structure as possible. Models of this type are typically built in large simulations platforms like GENESIS
GENESIS (software)
GENESIS is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including subcellular processes, individual neurons, networks of neurons, and neuronal systems.GENESIS was developed in the Caltech laboratory of Dr. James M...
or Neuron
Neuron (software)
NEURON is a simulation environment for modeling individual neurons and networks of neurons.It was primarily developed by Michael Hines, John W. Moore, and Ted Carnevale at Yale and Duke....
. There have been some attempts to provide unified methods that bridge and integrate these levels of complexity.
Cognition, discrimination and learning
Computational modeling of higher cognitive functions has only recently begun. Experimental data comes primarily from single-unit recordingSingle-unit recording
In neurophysiology and neurology, single-unit recording is the use of an electrode to record the electrophysiological activity from a single neuron.-History:...
in primates. The frontal lobe
Frontal lobe
The frontal lobe is an area in the brain of humans and other mammals, located at the front of each cerebral hemisphere and positioned anterior to the parietal lobe and superior and anterior to the temporal lobes...
and parietal lobe
Parietal lobe
The parietal lobe is a part of the Brain positioned above the occipital lobe and behind the frontal lobe.The parietal lobe integrates sensory information from different modalities, particularly determining spatial sense and navigation. For example, it comprises somatosensory cortex and the...
function as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation.
The brain
Brain
The brain is the center of the nervous system in all vertebrate and most invertebrate animals—only a few primitive invertebrates such as sponges, jellyfish, sea squirts and starfishes do not have one. It is located in the head, usually close to primary sensory apparatus such as vision, hearing,...
seems to be able to discriminate and adapt particularly well in certain contexts. For instance, human beings seem to have an enormous capacity for memorizing and recognizing faces. One of the key goals of computational neuroscience is to dissect how biological systems carry out these complex computations efficiently and potentially replicate these processes in building intelligent machines.
The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice. Integrative neuroscience
Integrative neuroscience
Integrative neuroscience sculptures a theoretical neuroscience with amathematical neuroscience that is different from computational neuroscience...
attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the basis for some quantitative modeling of large-scale brain activity.
Consciousness
One of the ultimate goals of psychology/neuroscience is to be able to explain the everyday experience of conscious life. Francis CrickFrancis Crick
Francis Harry Compton Crick OM FRS was an English molecular biologist, biophysicist, and neuroscientist, and most noted for being one of two co-discoverers of the structure of the DNA molecule in 1953, together with James D. Watson...
and Christof Koch
Christof Koch
Christof Koch is an American neuroscientist working on the neural basis of consciousness. He is the Lois and Victor Troendle Professor of Cognitive and Behavioral Biology at California Institute of Technology, where he has been since 1986...
made some attempts in formulating a consistent framework for future work in neural correlates of consciousness
Neural correlates of consciousness
The neural correlates of consciousness constitute the minimal set of neuronal events and mechanisms sufficient for a specific conscious percept. Neuroscientists use empirical approaches to discover neural correlates of subjective phenomena...
(NCC), though much of the work in this field remains speculative.
See also
- ConnectionismConnectionismConnectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units...
- Neural networkNeural networkThe term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes...
- Biological neuron models
- Neural codingNeural codingNeural coding is a neuroscience-related field concerned with how sensory and other information is represented in the brain by networks of neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses and...
- Brain-computer interfaceBrain-computer interfaceA brain–computer interface , sometimes called a direct neural interface or a brain–machine interface , is a direct communication pathway between the brain and an external device...
- Neural engineeringNeural engineeringNeural engineering is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties of neural systems...
- NeuroinformaticsNeuroinformaticsNeuroinformatics is a research field concerned with the organization of neuroscience data by the application of computational models and analytical tools. These areas of research are important for the integration and analysis of increasingly large-volume, high-dimensional, and fine-grain...
Journals
- Network: Computation in Neural Systems
- Biological Cybernetics
- Journal of Computational Neuroscience
- Neural Computation
- Neural Networks
- Neurocomputing
- Cognitive Neurodynamics
- Frontiers in Computational Neuroscience
- PLoS Computational Biology
- Frontiers in Neuroinformatics
Software
- Emergent, neural simulation software.
- Genesis, a general neural simulation system.
- ModelDB, a large open-access database of program codes of published computational neuroscience models.
- NEST, a simulation tool for large neuronal systems.
- Neuroconstruct, software for developing biologically realistic 3D neural networks.
- NEURON, a neuron simulator also useful to simulate neural networks.
- SNNAP, a single neuron and neural network simulator tool.
- ReMoto, a web-based simulator of the spinal cord and innervated muscles of the human leg.
Conferences
- Computational and Systems Neuroscience (COSYNE)– a computational neuroscience meeting with a systems neuroscience focus.
- Annual Computational Neuroscience Meeting (CNS)– a yearly computational neuroscience meeting.
- Neural Information Processing Systems (NIPS)– a leading annual conference covering other machine learning topics as well.
- Computational Cognitive Neuroscience Conference (CCNC)– a yearly conference.
- International Conference on Cognitive Neurodynamics (ICCN)– a yearly conference.
- UK Mathematical Neurosciences Meeting– a new yearly conference, focused on mathematical aspects.
- The NeuroComp Conference– a yearly computational neuroscience conference (France).
- Bernstein Conference on Computational Neuroscience (BCCN)– a yearly conference in Germany, organized by the Bernstein Network for Computational Neuroscience.
- AREADNE Conferences– a biennial meeting that includes theoretical and experimental results, held in even years in Santorini, Greece.
Websites
- Perlewitz's computational neuroscience on the web
- Encyclopedia of Computational Neuroscience, part of ScholarpediaScholarpediaScholarpedia is an English-language online wiki-based encyclopedia that uses the same MediaWiki software as Wikipedia, but has features more commonly associated with open-access online academic journals....
, an online expert curated encyclopedia on computational neuroscience, dynamical systems and machine intelligence