530:
In order to have a more concrete specification of the mechanism underlying visual attention and the binding of features, a number of computational models have been proposed aiming to explain psychophysical findings. In general, all models postulate the existence of a saliency or priority map for registering the potentially interesting areas of the retinal input, and a gating mechanism for reducing the amount of incoming visual information, so that the limited computational resources of the brain can handle it. An example theory that is being extensively tested behaviorally and physiologically is the
330:
investigations, where many neurons need to be simulated. As a result, researchers that study large neural circuits typically represent each neuron and synapse with an artificially simple model, ignoring much of the biological detail. Hence there is a drive to produce simplified neuron models that can retain significant biological fidelity at a low computational overhead. Algorithms have been developed to produce faithful, faster running, simplified surrogate neuron models from computationally expensive, detailed neuron models.
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394:, 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. For the example of visual processing, efficient coding is manifested in the forms of efficient spatial coding, color coding, temporal/motion coding, stereo coding, and combinations of them.
147:, 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 published in 1990 as the book
492:
of such simple systems are well-characterized theoretically. Some recent evidence suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions. It is not known, however, whether such descriptive dynamics impart any important computational function. With the emergence
714:
computer such as this is that it takes the computational load of the processor (in the sense that the structural and some of the functional elements don't have to be programmed since they are in hardware). In recent times, neuromorphic technology has been used to build supercomputers which are used
529:
Visual attention can be described as a set of mechanisms that limit some processing to a subset of incoming stimuli. Attentional mechanisms shape what we see and what we can act upon. They allow for concurrent selection of some (preferably, relevant) information and inhibition of other information.
520:
of neural networks. While many neurotheorists prefer such models with reduced complexity, others argue that uncovering structural-functional relations depends on including as much neuronal and network structure as possible. Models of this type are typically built in large simulation platforms like
276:
only employed two voltage-sensitive currents (Voltage sensitive ion channels are glycoprotein molecules which extend through the lipid bilayer, allowing ions to traverse under certain conditions through the axolemma), the fast-acting sodium and the inward-rectifying potassium. Though successful in
76:
Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also used sometimes, to stress the quantitative
417:
Many models of the way the brain controls movement have been developed. This includes models of processing in the brain such as the cerebellum's role for error correction, skill learning in motor cortex and the basal ganglia, or the control of the vestibulo ocular reflex. This also includes many
651:
Predictive computational neuroscience is a recent field that combines signal processing, neuroscience, clinical data and machine learning to predict the brain during coma or anesthesia. For example, it is possible to anticipate deep brain states using the EEG signal. These states can be used to
534:
that a bottom-up saliency map is created in the primary visual cortex to guide attention exogenously. Computational neuroscience provides a mathematical framework for studying the mechanisms involved in brain function and allows complete simulation and prediction of neuropsychological syndromes.
329:
Modeling the richness of biophysical properties on the single-neuron scale can supply mechanisms that serve as the building blocks for network dynamics. However, detailed neuron descriptions are computationally expensive and this computing cost can limit the pursuit of realistic network
1950:
Pannasch, Ulrike; Freche, Dominik; Dallérac, Glenn; Ghézali, Grégory; Escartin, Carole; Ezan, Pascal; Cohen-Salmon, Martine; Benchenane, Karim; Abudara, Veronica; Dufour, Amandine; Lübke, Joachim H. R.; Déglon, Nicole; Knott, Graham; Holcman, David; Rouach, Nathalie (April 2014).
342:, so important for maintaining homeostatis and to prevent epileptic seizures. Modeling reveals the role of glial protrusions that can penetrate in some cases the synaptic cleft to interfere with the synpatic transmission and thus control synaptic communication.
257:
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.
131:, columnar and topographic architecture, nuclei, all the way up to psychological faculties like memory, learning and behavior. These computational models frame hypotheses that can be directly tested by biological or psychological experiments.
124:; although mutual inspiration exists and sometimes there is no strict limit between fields, with model abstraction in computational neuroscience depending on research scope and the granularity at which biological entities are analyzed.
464:
forget less easily, but they are also harder to consolidate. 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.
281:. 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.
404:
Current research in sensory processing is divided among a biophysical modelling of different subsystems and a more theoretical modelling of perception. Current models of perception have suggested that the brain performs some form of
559:
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.
2609:
Adaszewski, Stanisław; Dukart, Juergen; Kherif, Ferath; Frackowiak, Richard; Draganski, Bogdan; Alzheimer's
Disease Neuroimaging Initiative (2013). "How early can we predict Alzheimer's disease using computational anatomy?".
233:, 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
444:
have been developed to address the properties of associative (also known as "content-addressable") style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium- and
571:. 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.
2696:
Floyrac, Aymeric; Doumergue, Adrien; Legriel, Stéphane; Deye, Nicolas; Megarbane, Bruno; Richard, Alexandra; Meppiel, Elodie; Masmoudi, Sana; Lozeron, Pierre; Vicaut, Eric; Kubis, Nathalie; Holcman, David (2023).
578:
attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the bases for some quantitative modeling of large-scale brain activity.
358:
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
586:) is another attempt at modeling human cognition through simulated processes like acquired rule-based systems in decision making and the manipulation of visual representations in decision making.
397:
Further along the visual pathway, even the efficiently coded visual information is too much for the capacity of the information bottleneck, the visual attentional bottleneck. A subsequent theory,
127:
Models in theoretical neuroscience are aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via
4168:
374:
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
1859:
401:, has been developed on exogenous attentional selection of a fraction of visual input for further processing, guided by a bottom-up saliency map in the primary visual cortex.
3373:
is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons.
477:, sparse and usually specific. It is not known how information is transmitted through such sparsely connected networks, although specific areas of the brain, such as the
2699:"Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials"
338:
Glial cells participate significantly in the regulation of neuronal activity at both the cellular and the network level. Modeling this interaction allows to clarify the
390:. 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
3403:
567:
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
277:
predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and
3466:
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288:
are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.
159:
in 1989. The first graduate educational program in computational neuroscience was organized as the
Computational and Neural Systems Ph.D. program at the
418:
normative models, such as those of the
Bayesian or optimal control flavor which are built on the idea that the brain efficiently solves its problems.
378:, which postulates that the formation of axons and dendrites effectively minimizes resource allocation while maintaining maximal information storage.
901:
241:
interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of
2098:
2063:
312:
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17:
481:, are understood in some detail. It is also unknown what the computational functions of these specific connectivity patterns are, if any.
2102:
574:
The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice.
900:
Patricia S. Churchland; Christof Koch; Terrence J. Sejnowski (1993). "What is computational neuroscience?". In Eric L. Schwartz (ed.).
2067:
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1292:
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GENESIS or NEURON. There have been some attempts to provide unified methods that bridge and integrate these levels of complexity.
456:
One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable
1810:"Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster"
4419:
3517:
3430:
2326:
Wilson, H. R.; Cowan, J.D. (1973). "A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue".
1078:
Gutkin, Boris; Pinto, David; Ermentrout, Bard (2003-03-01). "Mathematical neuroscience: from neurons to circuits to systems".
702:
A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations (See:
3299:
3280:
3261:
3242:
3170:
3147:
3102:
2452:
Machens CK, Romo R, Brody CD (2005). "Flexible control of mutual inhibition: a neural model of two-interval discrimination".
2436:
2411:
2378:
1741:
1716:
1445:
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884:
742:
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92:) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in
3418:
1204:
Paolo, E. D., "Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop",
4398:
842:
4424:
620:
596:
One of the ultimate goals of psychology/neuroscience is to be able to explain the everyday experience of conscious life.
3460:
3321:
1759:"Intracellular Calcium Dynamics Permit a Purkinje Neuron Model to Perform Toggle and Gain Computations Upon its Inputs"
1658:"Intracellular Calcium Dynamics Permit a Purkinje Neuron Model to Perform Toggle and Gain Computations Upon its Inputs"
1470:
1055:
609:
272:
Even a single neuron has complex biophysical characteristics and can perform computations (e.g.). Hodgkin and Huxley's
160:
4025:
3121:
2371:
Neural
Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience)
1302:
969:
543:
Computational modeling of higher cognitive functions has only recently begun. Experimental data comes primarily from
1545:
Lapicque L (1907). "Recherches quantitatives sur l'excitation électrique des nerfs traitée comme une polarisation".
473:
Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most
2653:
Friston KJ, Stephan KE, Montague R, Dolan RJ (2014). "Computational psychiatry: the brain as a phantastic organ".
4373:
3485:
2758:"Combining transient statistical markers from the EEG signal to predict brain sensitivity to general anesthesia"
4296:
4276:
4161:
4141:
3866:
3352:
3346:
501:, we now have powerful experimental methods with which to test the new theories regarding neuronal networks.
151:. The first of the annual open international meetings focused on Computational Neuroscience was organized by
121:
2114:
Weiss, Yair; Simoncelli, Eero P.; Adelson, Edward H. (20 May 2002). "Motion illusions as optimal percepts".
4334:
4301:
4281:
4205:
4020:
3841:
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3189:"A quantitative description of membrane current and its application to conduction and excitation in nerve"
2094:
4225:
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1860:"Dynamics of Ion Fluxes between Neurons, Astrocytes and the Extracellular Space during Neurotransmission"
391:
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907:
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3510:
762:
474:
278:
54:
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4388:
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4311:
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3899:
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2007:
1608:
822:
772:
643:, and to train scientists and clinicians that wish to apply these models to diagnosis and treatment.
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and integration of different sensory information in generating our perception of the physical world.
195:
187:
156:
113:
2474:
2171:
4383:
4324:
4286:
3994:
3969:
3894:
3801:
3706:
3602:
3451:– a computational neuroscience meeting focusing on computational models capable of cognitive tasks.
782:
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697:
660:
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273:
267:
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that modulate and influence the growth and development of functional connections between neurons.
53:, theoretical analysis and abstractions of the brain to understand the principles that govern the
4393:
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4235:
4210:
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2864:"Computational Psychiatry Research Map (CPSYMAP): a new database for visualizing research papers"
2862:
Kato, Ayaka; Kunisato, Yoshihiko; Katahira, Kentaro; Okimura, Tsukasa; Yamashita, Yuichi (2020).
707:
484:
The interactions of neurons in a small network can be often reduced to simple models such as the
117:
105:
2157:
Ernst, Marc O.; BĂĽlthoff, Heinrich H. (April 2004). "Merging the senses into a robust percept".
1609:"Receptive fields, binocular interaction and functional architecture in the cat's visual cortex"
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4215:
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The V1 hypothesis—creating a bottom-up saliency map for preattentive selection and segmentation
703:
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66:
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Early models on sensory processing understood within a theoretical framework are credited to
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2395:
2261:"Weak pairwise correlations imply strongly correlated network states in a neural population"
868:
4243:
4110:
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3701:
3672:
3567:
3492:, an online expert curated encyclopedia on computational neuroscience and dynamical systems
2461:
2282:
2019:
1896:
1564:
Brunel N, Van Rossum MC (2007). "Lapicque's 1907 paper: from frogs to integrate-and-fire".
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8:
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Neural engineering: Representation, computation, and dynamics in neurobiological systems
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1900:
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Computational neuroscience aims to address a wide array of questions, including: How do
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1988:
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128:
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2228:
2211:
1320:"Modeling language and cognition with deep unsupervised learning: a tutorial overview"
194:
model of the neuron in a seminal article published in 1907, a model still popular for
166:
The early historical roots of the field can be traced to the work of people including
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1980:
1972:
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Theoretical neuroscience: computational and mathematical modeling of neural systems
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Theoretical neuroscience: computational and mathematical modeling of neural systems
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214:
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50:
3433:(COSYNE) – a computational neuroscience meeting with a systems neuroscience focus.
2355:
1992:
1885:"The Neuroglial Potassium Cycle during Neurotransmission: Role of Kir4.1 Channels"
4100:
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2915:"Computational psychiatry as a bridge from neuroscience to clinical applications"
1909:
1883:
Sibille, Jérémie; Duc, Khanh Dao; Holcman, David; Rouach, Nathalie (2015-03-31).
1045:
832:
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and computational modeling to quantitatively define and investigate problems in
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1953:"Connexin 30 sets synaptic strength by controlling astroglial synapse invasion"
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that distinct parts of the nervous system release distinct chemical cues, from
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1976:
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3045:"Synaptic connectivity and neuronal morphology: two sides of the same coin"
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58:
42:
3413:
3355:, web based 3D visualization tool to browse connections in the human brain
2347:
2237:
2212:"Sparse coding with an overcomplete basis set: A strategy employed by V1?"
1952:
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3309:
3131:
2808:
928:
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608:
made some attempts to formulate consistent frameworks for future work in
485:
450:
234:
101:
46:
4153:
3475:– a biennial meeting that includes theoretical and experimental results.
2294:
2097:
Trends in
Cognitive Sciences vol. 6, Pages 9-16, and Zhaoping, L. 2014,
2031:
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2127:
1228:"Turing centenary: Is the brain a good model for machine intelligence?"
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1968:
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1227:
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Computational neuroscience: realistic modeling for experimentalists
3233:; Rieke, Fred; David Warland; Rob de Ruyter van Steveninck (1999).
2563:
1734:
Biophysics of computation: information processing in single neurons
1706:
1318:
Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P. (2013-08-20).
1147:
715:
in international neuroscience collaborations. Examples include the
548:
461:
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are easy to train but also prone to stochastic disruption. Stable
457:
368:
2079:
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anticipate hypnotic concentration to administrate to the patient.
612:(NCC), though much of the work in this field remains speculative.
3393:
229:, the first cortical area to process information coming from the
81:
2977:
Calimera, Andrea; Macii, Enrico; Poncino, Massimo (2013-08-20).
2608:
3495:
3445:– a leading annual conference covering mostly machine learning.
2513:
Robinson PA, Rennie CJ, Rowe DL, O'Connor SC, Gordon E (2005).
433:
345:
230:
85:
2964:"Beyond von Neumann, Neuromorphic Computing Steadily Advances"
2512:
1294:
Neural
Network Perspectives on Cognition and Adaptive Robotics
1226:
Brooks, R.; Hassabis, D.; Bray, D.; Shashua, A. (2012-02-22).
3290:
Michael A. Arbib; Shun-ichi Amari; Prudence H. Arbib (2002).
2010:(October 2004). "Cortical rewiring and information storage".
1949:
1129:
Kriegeskorte, Nikolaus; Douglas, Pamela K. (September 2018).
564:
351:
315:, aims to construct a biophysically detailed simulation of a
2913:
Huys, Quentin J M; Maia, Tiago V; Frank, Michael J (2016).
2861:
2695:
2652:
583:
3682:
3455:
International
Conference on Cognitive Neurodynamics (ICCN)
3112:
Gerstner, W.; Kistler, W.; Naud, R.; Paninski, L. (2014).
2258:
960:
Gerstner, W.; Kistler, W.; Naud, R.; Paninski, L. (2014).
582:
538:
3467:
Bernstein
Conference on Computational Neuroscience (BCCN)
2802:
2394:
Marvin M. Chun; Jeremy M. Wolfe; E. B. Goldstein (2001).
1225:
623:
is a field that brings together experts in neuroscience,
198:
studies because of its simplicity (see a recent review).
80:
Computational neuroscience focuses on the description of
3436:
2426:
2325:
663:
is a new emerging field that brings together experts in
646:
139:
The term 'computational neuroscience' was introduced by
3463:– a yearly conference, focused on mathematical aspects.
3448:
1882:
875:. United States: Oxford University Press Inc. pp.
2113:
1206:
Dynamical
Systems Approach to Embodiment and Sociality
683:
to provide an understanding of psychiatric disorders.
615:
3442:
3130:
1317:
1077:
927:
2979:"The human brain project and neuromorphic computing"
2976:
2005:
525:
Visual attention, identification, and categorization
333:
3469:– a yearly computational neuroscience conference ].
2210:Olshausen, Bruno A.; Field, David J. (1997-12-01).
1128:
3271:Sejnowski, Terrence J.; Hemmen, J. L. van (2006).
2259:Schneidman E, Berry MJ, Segev R, Bialek W (2006).
3251:
3025:: CS1 maint: DOI inactive as of September 2024 (
2519:Philosophical Transactions of the Royal Society B
2080:https://en.wikipedia.org/Visual_spatial_attention
421:
4411:
3292:The Handbook of Brain Theory and Neural Networks
2451:
1563:
245:, with the first multicompartmental model using
3437:Annual Computational Neuroscience Meeting (CNS)
3157:Eliasmith, Chris; Anderson, Charles H. (2003).
2369:Anderson, Charles H.; Eliasmith, Chris (2004).
2068:Understanding vision: theory, models, and data
1460:
1377:Shai, Adam; Larkum, Matthew Evan (2017-12-05).
504:In some cases the complex interactions between
213:and created the first biophysical model of the
3439:– a yearly computational neuroscience meeting.
3314:Understanding vision: theory, models, and data
2756:Sun, Christophe; Holcman, David (2022-08-01).
2398:Blackwell Handbook of Sensation and Perception
2209:
2103:Understanding Vision: Theory, Models, and Data
1731:
1707:Wu, Samuel Miao-sin; Johnston, Daniel (1995).
1435:
143:, who organized a conference, held in 1985 in
4169:
3511:
3179:
3116:. Cambridge, UK: Cambridge University Press.
3042:
2912:
2156:
964:. Cambridge, UK: Cambridge University Press.
4046:Intraoperative neurophysiological monitoring
3443:Neural Information Processing Systems (NIPS)
1488:"A Brief History of Simulation Neuroscience"
987:"A Brief History of Simulation Neuroscience"
722:supercomputer and the BrainScaleS computer.
346:Development, axonal patterning, and guidance
3294:. Cambridge, Massachusetts: The MIT Press.
1606:
906:. MIT Press. pp. 46–55. Archived from
866:
655:
291:There are many software packages, such as
4176:
4162:
3518:
3504:
3486:Encyclopedia of Computational Neuroscience
3449:Cognitive Computational Neuroscience (CCN)
2755:
1807:
1756:
1655:
1544:
1485:
1376:
1047:Fundamentals of Computational Neuroscience
984:
871:Fundamentals of Computational Neuroscience
4183:
3212:
3060:
3002:
2938:
2889:
2879:
2838:
2732:
2714:
2561:
2538:
2473:
2302:
2276:
2227:
2170:
2089:
2087:
1926:
1908:
1835:
1825:
1784:
1774:
1683:
1673:
1632:
1521:
1503:
1412:
1394:
1353:
1335:
1251:
1180:
1146:
1020:
1002:
691:
436:are primarily based on the postulates of
261:
3308:
2762:Biomedical Signal Processing and Control
468:
440:. Biologically relevant models such as
313:École Polytechnique Fédérale de Lausanne
3409:Frontiers in Computational Neuroscience
3316:. Oxford, UK: Oxford University Press.
2961:
2095:A saliency map in primary visual cortex
1763:Frontiers in Computational Neuroscience
1709:Foundations of cellular neurophysiology
1662:Frontiers in Computational Neuroscience
1082:. Neurogeometry and visual perception.
539:Cognition, discrimination, and learning
284:The computational functions of complex
14:
4412:
3431:Computational and Systems Neuroscience
2803:Montague, P. Read; Dolan, Raymond J.;
2084:
1463:20 years of Computational neuroscience
1290:
1131:"Cognitive computational neuroscience"
1043:
4157:
3499:
3461:UK Mathematical Neurosciences Meeting
3394:Journal of Computational Neuroscience
3367:, a general neural simulation system.
3089:; Churchland, Patricia Smith (1992).
2402:. Blackwell Publishing Ltd. pp.
647:Predictive computational neuroscience
381:
4430:Mathematical and theoretical biology
4136:
3389:Journal of Mathematical Neuroscience
3275:. Oxford : Oxford University Press.
2624:10.1016/j.neurobiolaging.2013.06.015
2429:Computational Neuroscience of Vision
1736:. Oxford : Oxford University Press.
710:). One of the advantages of using a
3273:23 problems in systems neuroscience
2427:Edmund Rolls; Gustavo Deco (2012).
621:Computational clinical neuroscience
616:Computational clinical neuroscience
24:
2373:. Cambridge, Mass: The MIT Press.
610:neural correlates of consciousness
299:, that allow rapid and systematic
161:California Institute of Technology
25:
4441:
4026:Development of the nervous system
3377:
3235:Spikes: exploring the neural code
1486:Fan, Xue; Markram, Henry (2019).
1203:
985:Fan, Xue; Markram, Henry (2019).
860:
334:Modeling Neuron-glia interactions
4135:
4124:
4123:
3681:
3525:
1092:10.1016/j.jphysparis.2003.09.005
589:
512:neurons can be simplified using
412:
3036:
2970:
2962:Russell, John (21 March 2016).
2955:
2906:
2855:
2796:
2749:
2689:
2646:
2602:
2564:"A framework for consciousness"
2555:
2506:
2445:
2420:
2387:
2362:
2319:
2252:
2203:
2150:
2107:
2078:see visual spational attention
2072:
2064:The efficient coding principle
2056:
1999:
1943:
1876:
1852:
1801:
1750:
1725:
1700:
1649:
1600:
1557:
1538:
1479:
1454:
1429:
1370:
1311:
1284:
1219:
867:Trappenberg, Thomas P. (2010).
303:modeling of realistic neurons.
252:
225:discovered that neurons in the
3424:
3205:10.1113/jphysiol.1952.sp004764
3142:. Cambridge, Mass: MIT Press.
1711:. Cambridge, Mass: MIT Press.
1625:10.1113/jphysiol.1962.sp006837
1440:. Cambridge, Mass: MIT Press.
1197:
1122:
1071:
1037:
978:
953:
939:. Cambridge, Mass: MIT Press.
921:
893:
422:Memory and synaptic plasticity
13:
1:
4420:Computational fields of study
3867:Social cognitive neuroscience
3419:Frontiers in Neuroinformatics
3361:, neural simulation software.
3353:Budapest Reference Connectome
3331:
2667:10.1016/S2215-0366(14)70275-5
2431:. Oxford Scholarship Online.
2229:10.1016/S0042-6989(97)00169-7
2066:, chapter 3, of the textbook
1492:Frontiers in Neuroinformatics
1465:. Berlin, Germany: Springer.
991:Frontiers in Neuroinformatics
854:
686:
399:V1 Saliency Hypothesis (V1SH)
122:computational learning theory
3842:Molecular cellular cognition
3062:10.1016/j.neuron.2004.08.012
2995:10.11138/FNeur/2013.28.3.191
2818:Trends in Cognitive Sciences
2515:"Multiscale brain modelling"
2159:Trends in Cognitive Sciences
1910:10.1371/journal.pcbi.1004137
1607:Hubel DH, Wiesel TN (1962).
1044:Thomas, Trappenberg (2010).
18:Computational neuroscientist
7:
4061:Neurodevelopmental disorder
4036:Neural network (biological)
4031:Neural network (artificial)
3479:
3382:
3336:
1080:Journal of Physiology-Paris
725:
10:
4446:
4425:Computational neuroscience
3588:Computational neuroscience
3414:PLoS Computational Biology
3252:Schutter, Erik de (2001).
2831:10.1016/j.tics.2011.11.018
2813:"Computational psychiatry"
2774:10.1016/j.bspc.2022.103713
2181:10.1016/j.tics.2004.02.002
1889:PLOS Computational Biology
1438:Computational neuroscience
903:Computational Neuroscience
763:Differentiable programming
695:
516:, which gives rise to the
475:artificial neural networks
425:
265:
196:artificial neural networks
190:. Lapicque introduced the
149:Computational Neuroscience
134:
114:artificial neural networks
31:Computational neuroscience
4361:
4333:
4310:
4262:
4234:
4191:
4119:
4056:Neurodegenerative disease
4013:
3900:Evolutionary neuroscience
3875:
3815:
3690:
3679:
3551:
3533:
2881:10.3389/fpsyt.2020.578706
2716:10.3389/fnins.2023.988394
2703:Frontiers in Neuroscience
1827:10.1186/s12868-015-0162-6
1808:Forrest MD (April 2015).
1578:10.1007/s00422-007-0190-0
1291:Browne, A. (1997-01-01).
1165:10.1038/s41593-018-0210-5
1050:. OUP Oxford. p. 2.
823:Neuromimetic intelligence
376:minimal wiring hypothesis
157:San Francisco, California
39:mathematical neuroscience
4021:Brain–computer interface
3970:Neuromorphic engineering
3895:Educational neuroscience
3802:Nutritional neuroscience
3707:Clinical neurophysiology
3603:Integrative neuroscience
3237:. Cambridge, Mass: MIT.
2562:Crick F, Koch C (2003).
1776:10.3389/fncom.2014.00086
1675:10.3389/fncom.2014.00086
1505:10.3389/fninf.2019.00032
1461:Bower, James M. (2013).
1337:10.3389/fpsyg.2013.00515
1004:10.3389/fninf.2019.00032
738:Biological neuron models
698:Neuromorphic engineering
661:Computational psychiatry
656:Computational Psychiatry
576:Integrative neuroscience
268:Biological neuron models
35:theoretical neuroscience
3832:Behavioral neuroscience
3404:Cognitive Neurodynamics
3091:The computational brain
2997:(inactive 2024-09-12).
2868:Frontiers in Psychiatry
2484:10.1126/science.1104171
2006:Chklovskii DB, Mel BW,
1732:Koch, Christof (1999).
1547:J. Physiol. Pathol. Gen
1436:Schwartz, Eric (1990).
1379:"Branching into brains"
1324:Frontiers in Psychology
708:physical neural network
307:, a project founded by
118:artificial intelligence
106:quantitative psychology
4399:Transportation science
3827:Affective neuroscience
3608:Molecular neuroscience
3563:Behavioral epigenetics
3457:– a yearly conference.
3087:Sejnowski, Terrence J.
3043:Chklovskii DB (2004).
2531:10.1098/rstb.2005.1638
704:neuromorphic computing
692:Neuromorphic computing
532:V1 Saliency Hypothesis
262:Single-neuron modeling
201:About 40 years later,
41:) is a branch of
27:Branch of neuroscience
4185:Computational science
3890:Cultural neuroscience
3885:Consumer neuroscience
3727:Neurogastroenterology
3583:Cellular neuroscience
773:FitzHugh–Nagumo model
753:Computational anatomy
545:single-unit recording
495:two-photon microscopy
490:statistical mechanics
469:Behaviors of networks
227:primary visual cortex
77:nature of the field.
4244:Electronic structure
3862:Sensory neuroscience
3702:Behavioral neurology
3673:Systems neuroscience
2983:Functional Neurology
838:Systems neuroscience
783:Hodgkin–Huxley model
641:psychiatric diseases
449:, localizing in the
129:network oscillations
45: which employs
4249:Molecular mechanics
4005:Social neuroscience
3905:Global neurosurgery
3782:Neurorehabilitation
3752:Neuro-ophthalmology
3737:Neurointensive care
3568:Behavioral genetics
3473:AREADNE Conferences
3256:. Boca Raton: CRC.
3161:. Cambridge, Mass:
3093:. Cambridge, Mass:
2919:Nature Neuroscience
2525:(1457): 1043–1050.
2466:2005Sci...307.1121M
2295:10.1038/nature04701
2287:2006Natur.440.1007S
2116:Nature Neuroscience
2032:10.1038/nature03012
2024:2004Natur.431..782C
1957:Nature Neuroscience
1901:2015PLSCB..11E4137S
1757:Forrest MD (2014).
1656:Forrest MD (2014).
1396:10.7554/eLife.33066
1244:2012Natur.482..462.
1157:2018arXiv180711819K
1135:Nature Neuroscience
843:Theoretical biology
717:Human Brain Project
428:Synaptic plasticity
155:and John Miller in
67:cognitive abilities
4206:Biological systems
4081:Neuroimmune system
3975:Neurophenomenology
3915:Neural engineering
3638:Neuroendocrinology
3618:Neural engineering
3399:Neural Computation
3187:(28 August 1952).
2580:10.1038/nn0203-119
2340:10.1007/BF00288786
2128:10.1038/nn0602-858
2062:Zhaoping L. 2014,
813:Neural oscillation
798:Nonlinear dynamics
793:Mathematical model
788:Information theory
432:Earlier models of
407:Bayesian inference
382:Sensory processing
192:integrate and fire
145:Carmel, California
4407:
4406:
4374:Materials science
4254:Quantum mechanics
4151:
4150:
4000:Paleoneurobiology
3935:Neuroepistemology
3910:Neuroanthropology
3876:Interdisciplinary
3762:Neuropharmacology
3722:Neuroepidemiology
3301:978-0-262-01197-6
3282:978-0-19-514822-0
3263:978-0-8493-2068-2
3244:978-0-262-68108-7
3172:978-0-262-05071-5
3149:978-0-262-04199-7
3114:Neuronal Dynamics
3104:978-0-262-03188-2
2655:Lancet Psychiatry
2438:978-0-198-52488-5
2413:978-0-631-20684-2
2380:978-0-262-55060-4
2271:(7087): 1007–12.
2222:(23): 3311–3325.
1743:978-0-19-510491-2
1718:978-0-262-10053-3
1447:978-0-262-19291-0
1238:(7386): 462–463.
962:Neuronal Dynamics
946:978-0-262-04199-7
886:978-0-19-851582-1
768:Electrophysiology
633:decision sciences
569:recognizing faces
514:mean-field theory
361:molecular biology
16:(Redirected from
4437:
4297:Particle physics
4277:Electromagnetics
4178:
4171:
4164:
4155:
4154:
4139:
4138:
4127:
4126:
4041:Detection theory
3925:Neurocriminology
3852:Neurolinguistics
3767:Neuroprosthetics
3685:
3648:Neuroinformatics
3598:Imaging genetics
3520:
3513:
3506:
3497:
3496:
3327:
3305:
3286:
3267:
3248:
3226:
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3064:
3031:
3030:
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2953:
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2942:
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2903:
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2859:
2853:
2852:
2842:
2805:Friston, Karl J.
2800:
2794:
2793:
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2747:
2746:
2736:
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2693:
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2650:
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2460:(5712): 1121–4.
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2417:
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2250:
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2147:
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2054:
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2003:
1997:
1996:
1947:
1941:
1940:
1930:
1912:
1880:
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1873:
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1849:
1839:
1829:
1814:BMC Neuroscience
1805:
1799:
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1788:
1778:
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1748:
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1723:
1722:
1704:
1698:
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1646:
1636:
1604:
1598:
1597:
1572:(5–6): 337–339.
1561:
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1483:
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1476:
1458:
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1281:
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1141:(9): 1148–1160.
1126:
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1041:
1035:
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1024:
1006:
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957:
951:
950:
925:
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916:
915:
897:
891:
890:
874:
864:
818:Neuroinformatics
778:Goldman equation
748:Brain simulation
733:Action potential
665:machine learning
518:population model
447:long-term memory
438:Hebbian learning
392:efficient coding
215:action potential
141:Eric L. Schwartz
110:machine learning
51:computer science
21:
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4440:
4439:
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4435:
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4408:
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4329:
4306:
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4230:
4187:
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4101:Neurotechnology
4096:Neuroplasticity
4091:Neuromodulation
4086:Neuromanagement
4009:
3980:Neurophilosophy
3877:
3871:
3857:Neuropsychology
3818:
3811:
3772:Neuropsychiatry
3732:Neuroimmunology
3717:Neurocardiology
3693:
3686:
3677:
3668:Neurophysiology
3658:Neuromorphology
3613:Neural decoding
3554:
3547:
3529:
3524:
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3380:
3349:based simulator
3339:
3334:
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2856:
2811:(14 Dec 2011).
2801:
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2647:
2618:(12): 2815–26.
2612:Neurobiol Aging
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2475:10.1.1.523.4396
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2216:Vision Research
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2172:10.1.1.299.4638
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2018:(7010): 782–8.
2004:
2000:
1969:10.1038/nn.3662
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1864:cyberleninka.ru
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1253:10.1038/482462a
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833:Neurophysiology
828:Neuroplasticity
808:Neural decoding
728:
700:
694:
689:
658:
649:
618:
594:
541:
527:
499:calcium imaging
471:
430:
424:
415:
384:
348:
340:potassium cycle
336:
317:cortical column
270:
264:
255:
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33:(also known as
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23:
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4337:
4335:Social science
4331:
4330:
4328:
4327:
4322:
4316:
4314:
4308:
4307:
4305:
4304:
4302:Thermodynamics
4299:
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4289:
4284:
4282:Fluid dynamics
4279:
4274:
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4259:
4257:
4256:
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4246:
4240:
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4146:
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4111:Self-awareness
4108:
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4066:Neurodiversity
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3965:Neuromarketing
3962:
3957:
3952:
3947:
3942:
3940:Neuroesthetics
3937:
3932:
3930:Neuroeconomics
3927:
3922:
3917:
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3757:Neuropathology
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3747:Neuro-oncology
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3729:
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3714:
3709:
3704:
3698:
3696:
3688:
3687:
3680:
3678:
3676:
3675:
3670:
3665:
3660:
3655:
3650:
3645:
3640:
3635:
3633:Neurochemistry
3630:
3625:
3620:
3615:
3610:
3605:
3600:
3595:
3590:
3585:
3580:
3575:
3570:
3565:
3559:
3557:
3549:
3548:
3546:
3545:
3540:
3534:
3531:
3530:
3523:
3522:
3515:
3508:
3500:
3494:
3493:
3481:
3478:
3477:
3476:
3470:
3464:
3458:
3452:
3446:
3440:
3434:
3426:
3423:
3422:
3421:
3416:
3411:
3406:
3401:
3396:
3391:
3384:
3381:
3379:
3378:External links
3376:
3375:
3374:
3368:
3362:
3356:
3350:
3338:
3335:
3333:
3330:
3329:
3328:
3323:978-0199564668
3322:
3306:
3300:
3287:
3281:
3268:
3262:
3249:
3243:
3231:William Bialek
3227:
3177:
3171:
3154:
3148:
3128:
3122:
3109:
3103:
3083:
3038:
3035:
3033:
3032:
2989:(3): 191–196.
2969:
2954:
2925:(3): 404–413.
2905:
2854:
2795:
2748:
2688:
2645:
2601:
2554:
2505:
2444:
2437:
2419:
2412:
2386:
2379:
2361:
2318:
2251:
2202:
2165:(4): 162–169.
2149:
2122:(6): 598–604.
2106:
2083:
2071:
2055:
2053:Review article
1998:
1963:(4): 549–558.
1942:
1875:
1851:
1800:
1749:
1742:
1724:
1717:
1699:
1648:
1599:
1556:
1537:
1478:
1472:978-1461414230
1471:
1453:
1446:
1428:
1369:
1310:
1303:
1283:
1218:
1196:
1121:
1086:(2): 209–219.
1070:
1057:978-0199568413
1056:
1036:
977:
970:
952:
945:
920:
892:
885:
858:
856:
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851:
850:
845:
840:
835:
830:
825:
820:
815:
810:
805:
800:
795:
790:
785:
780:
775:
770:
765:
760:
755:
750:
745:
743:Bayesian brain
740:
735:
729:
727:
724:
712:physical model
696:Main article:
693:
690:
688:
685:
657:
654:
648:
645:
617:
614:
593:
588:
540:
537:
526:
523:
470:
467:
426:Main article:
423:
420:
414:
411:
383:
380:
365:growth factors
347:
344:
335:
332:
274:original model
266:Main article:
263:
260:
254:
251:
209:developed the
168:Louis Lapicque
153:James M. Bower
136:
133:
98:control theory
90:neural systems
71:nervous system
26:
9:
6:
4:
3:
2:
4442:
4431:
4428:
4426:
4423:
4421:
4418:
4417:
4415:
4400:
4397:
4395:
4392:
4390:
4387:
4385:
4382:
4380:
4377:
4375:
4372:
4370:
4367:
4366:
4364:
4360:
4354:
4351:
4349:
4346:
4344:
4341:
4340:
4338:
4336:
4332:
4326:
4323:
4321:
4318:
4317:
4315:
4313:
4309:
4303:
4300:
4298:
4295:
4293:
4290:
4288:
4285:
4283:
4280:
4278:
4275:
4273:
4270:
4269:
4267:
4265:
4261:
4255:
4252:
4250:
4247:
4245:
4242:
4241:
4239:
4237:
4233:
4227:
4226:Phylogenetics
4224:
4222:
4219:
4217:
4214:
4212:
4209:
4207:
4204:
4202:
4199:
4198:
4196:
4194:
4190:
4186:
4179:
4174:
4172:
4167:
4165:
4160:
4159:
4156:
4144:
4143:
4134:
4132:
4131:
4122:
4121:
4118:
4112:
4109:
4107:
4104:
4102:
4099:
4097:
4094:
4092:
4089:
4087:
4084:
4082:
4079:
4077:
4074:
4072:
4069:
4067:
4064:
4062:
4059:
4057:
4054:
4052:
4049:
4047:
4044:
4042:
4039:
4037:
4034:
4032:
4029:
4027:
4024:
4022:
4019:
4018:
4016:
4012:
4006:
4003:
4001:
3998:
3996:
3995:Neurotheology
3993:
3991:
3990:Neurorobotics
3988:
3986:
3985:Neuropolitics
3983:
3981:
3978:
3976:
3973:
3971:
3968:
3966:
3963:
3961:
3958:
3956:
3953:
3951:
3950:Neuroethology
3948:
3946:
3943:
3941:
3938:
3936:
3933:
3931:
3928:
3926:
3923:
3921:
3918:
3916:
3913:
3911:
3908:
3906:
3903:
3901:
3898:
3896:
3893:
3891:
3888:
3886:
3883:
3882:
3880:
3874:
3868:
3865:
3863:
3860:
3858:
3855:
3853:
3850:
3848:
3847:Motor control
3845:
3843:
3840:
3838:
3837:Chronobiology
3835:
3833:
3830:
3828:
3825:
3824:
3822:
3820:
3814:
3808:
3805:
3803:
3800:
3798:
3797:Neurovirology
3795:
3793:
3790:
3788:
3785:
3783:
3780:
3778:
3775:
3773:
3770:
3768:
3765:
3763:
3760:
3758:
3755:
3753:
3750:
3748:
3745:
3743:
3740:
3738:
3735:
3733:
3730:
3728:
3725:
3723:
3720:
3718:
3715:
3713:
3710:
3708:
3705:
3703:
3700:
3699:
3697:
3695:
3689:
3684:
3674:
3671:
3669:
3666:
3664:
3661:
3659:
3656:
3654:
3651:
3649:
3646:
3644:
3643:Neurogenetics
3641:
3639:
3636:
3634:
3631:
3629:
3626:
3624:
3621:
3619:
3616:
3614:
3611:
3609:
3606:
3604:
3601:
3599:
3596:
3594:
3591:
3589:
3586:
3584:
3581:
3579:
3578:Brain-reading
3576:
3574:
3573:Brain mapping
3571:
3569:
3566:
3564:
3561:
3560:
3558:
3556:
3550:
3544:
3541:
3539:
3536:
3535:
3532:
3528:
3521:
3516:
3514:
3509:
3507:
3502:
3501:
3498:
3491:
3487:
3484:
3483:
3474:
3471:
3468:
3465:
3462:
3459:
3456:
3453:
3450:
3447:
3444:
3441:
3438:
3435:
3432:
3429:
3428:
3420:
3417:
3415:
3412:
3410:
3407:
3405:
3402:
3400:
3397:
3395:
3392:
3390:
3387:
3386:
3372:
3369:
3366:
3363:
3360:
3357:
3354:
3351:
3348:
3344:
3341:
3340:
3325:
3319:
3315:
3311:
3307:
3303:
3297:
3293:
3288:
3284:
3278:
3274:
3269:
3265:
3259:
3255:
3250:
3246:
3240:
3236:
3232:
3228:
3224:
3220:
3215:
3210:
3206:
3202:
3199:(4): 500–44.
3198:
3194:
3190:
3186:
3182:
3178:
3174:
3168:
3164:
3160:
3155:
3151:
3145:
3141:
3137:
3136:Abbott, L. F.
3133:
3129:
3125:
3123:9781107447615
3119:
3115:
3110:
3106:
3100:
3096:
3092:
3088:
3084:
3080:
3076:
3072:
3068:
3063:
3058:
3055:(5): 609–17.
3054:
3050:
3046:
3041:
3040:
3028:
3022:
3014:
3010:
3005:
3000:
2996:
2992:
2988:
2984:
2980:
2973:
2965:
2958:
2950:
2946:
2941:
2936:
2932:
2928:
2924:
2920:
2916:
2909:
2901:
2897:
2892:
2887:
2882:
2877:
2873:
2869:
2865:
2858:
2850:
2846:
2841:
2836:
2832:
2828:
2824:
2820:
2819:
2814:
2810:
2806:
2799:
2791:
2787:
2783:
2779:
2775:
2771:
2767:
2763:
2759:
2752:
2744:
2740:
2735:
2730:
2726:
2722:
2717:
2712:
2708:
2704:
2700:
2692:
2684:
2680:
2676:
2672:
2668:
2664:
2661:(2): 148–58.
2660:
2656:
2649:
2641:
2637:
2633:
2629:
2625:
2621:
2617:
2613:
2605:
2597:
2593:
2589:
2585:
2581:
2577:
2574:(2): 119–26.
2573:
2569:
2568:Nat. Neurosci
2565:
2558:
2550:
2546:
2541:
2536:
2532:
2528:
2524:
2520:
2516:
2509:
2501:
2497:
2493:
2489:
2485:
2481:
2476:
2471:
2467:
2463:
2459:
2455:
2448:
2440:
2434:
2430:
2423:
2415:
2409:
2405:
2400:
2399:
2390:
2382:
2376:
2372:
2365:
2357:
2353:
2349:
2345:
2341:
2337:
2333:
2329:
2322:
2314:
2310:
2305:
2300:
2296:
2292:
2288:
2284:
2279:
2278:q-bio/0512013
2274:
2270:
2266:
2262:
2255:
2247:
2243:
2239:
2235:
2230:
2225:
2221:
2217:
2213:
2206:
2198:
2194:
2190:
2186:
2182:
2178:
2173:
2168:
2164:
2160:
2153:
2145:
2141:
2137:
2133:
2129:
2125:
2121:
2117:
2110:
2104:
2100:
2096:
2090:
2088:
2081:
2075:
2069:
2065:
2059:
2049:
2045:
2041:
2037:
2033:
2029:
2025:
2021:
2017:
2013:
2009:
2002:
1994:
1990:
1986:
1982:
1978:
1974:
1970:
1966:
1962:
1958:
1954:
1946:
1938:
1934:
1929:
1924:
1920:
1916:
1911:
1906:
1902:
1898:
1894:
1890:
1886:
1879:
1865:
1861:
1855:
1847:
1843:
1838:
1833:
1828:
1823:
1819:
1815:
1811:
1804:
1796:
1792:
1787:
1782:
1777:
1772:
1768:
1764:
1760:
1753:
1745:
1739:
1735:
1728:
1720:
1714:
1710:
1703:
1695:
1691:
1686:
1681:
1676:
1671:
1667:
1663:
1659:
1652:
1644:
1640:
1635:
1630:
1626:
1622:
1619:(1): 106–54.
1618:
1614:
1610:
1603:
1595:
1591:
1587:
1583:
1579:
1575:
1571:
1567:
1560:
1552:
1548:
1541:
1533:
1529:
1524:
1519:
1515:
1511:
1506:
1501:
1497:
1493:
1489:
1482:
1474:
1468:
1464:
1457:
1449:
1443:
1439:
1432:
1424:
1420:
1415:
1410:
1406:
1402:
1397:
1392:
1388:
1384:
1380:
1373:
1365:
1361:
1356:
1351:
1347:
1343:
1338:
1333:
1329:
1325:
1321:
1314:
1306:
1304:9780750304559
1300:
1297:. CRC Press.
1296:
1295:
1287:
1279:
1275:
1271:
1267:
1263:
1259:
1254:
1249:
1245:
1241:
1237:
1233:
1229:
1222:
1215:
1211:
1207:
1200:
1192:
1188:
1183:
1178:
1174:
1170:
1166:
1162:
1158:
1154:
1149:
1144:
1140:
1136:
1132:
1125:
1117:
1113:
1109:
1105:
1101:
1097:
1093:
1089:
1085:
1081:
1074:
1059:
1053:
1049:
1048:
1040:
1032:
1028:
1023:
1018:
1014:
1010:
1005:
1000:
996:
992:
988:
981:
973:
971:9781107447615
967:
963:
956:
948:
942:
938:
934:
933:Abbott, L. F.
930:
924:
910:on 2011-06-04
909:
905:
904:
896:
888:
882:
878:
873:
872:
863:
859:
849:
846:
844:
841:
839:
836:
834:
831:
829:
826:
824:
821:
819:
816:
814:
811:
809:
806:
804:
803:Neural coding
801:
799:
796:
794:
791:
789:
786:
784:
781:
779:
776:
774:
771:
769:
766:
764:
761:
759:
756:
754:
751:
749:
746:
744:
741:
739:
736:
734:
731:
730:
723:
721:
718:
713:
709:
705:
699:
684:
682:
678:
674:
670:
666:
662:
653:
644:
642:
638:
634:
630:
626:
622:
613:
611:
607:
606:Christof Koch
603:
602:Giulio Tononi
599:
598:Francis Crick
592:
591:Consciousness
587:
585:
580:
577:
572:
570:
566:
561:
558:
557:parietal lobe
554:
550:
546:
536:
533:
522:
519:
515:
511:
507:
502:
500:
496:
491:
487:
482:
480:
479:visual cortex
476:
466:
463:
459:
454:
452:
448:
443:
439:
435:
429:
419:
413:Motor control
410:
408:
402:
400:
395:
393:
389:
388:Horace Barlow
379:
377:
372:
370:
366:
362:
357:
353:
343:
341:
331:
327:
325:
324:supercomputer
322:
318:
314:
310:
309:Henry Markram
306:
302:
298:
294:
289:
287:
282:
280:
275:
269:
259:
250:
248:
244:
240:
236:
232:
228:
224:
220:
216:
212:
211:voltage clamp
208:
204:
199:
197:
193:
189:
185:
181:
177:
173:
169:
164:
162:
158:
154:
150:
146:
142:
132:
130:
125:
123:
119:
115:
111:
107:
103:
99:
95:
94:connectionism
91:
87:
83:
78:
74:
72:
68:
64:
60:
56:
52:
48:
44:
40:
36:
32:
19:
4272:Astrophysics
4221:Neuroscience
4220:
4140:
4128:
4076:Neuroimaging
4071:Neurogenesis
3955:Neurohistory
3920:Neurobiotics
3819:neuroscience
3787:Neurosurgery
3712:Epileptology
3694:neuroscience
3663:Neurophysics
3653:Neurometrics
3628:Neurobiology
3623:Neuroanatomy
3593:Connectomics
3587:
3527:Neuroscience
3490:Scholarpedia
3313:
3310:Zhaoping, Li
3291:
3272:
3253:
3234:
3196:
3192:
3158:
3139:
3113:
3090:
3052:
3048:
3037:Bibliography
3021:cite journal
2986:
2982:
2972:
2957:
2922:
2918:
2908:
2871:
2867:
2857:
2825:(1): 72–80.
2822:
2816:
2809:Dayan, Peter
2798:
2765:
2761:
2751:
2706:
2702:
2691:
2658:
2654:
2648:
2615:
2611:
2604:
2571:
2567:
2557:
2522:
2518:
2508:
2457:
2453:
2447:
2428:
2422:
2397:
2389:
2370:
2364:
2334:(2): 55–80.
2331:
2327:
2321:
2268:
2264:
2254:
2219:
2215:
2205:
2162:
2158:
2152:
2119:
2115:
2109:
2101:in the book
2093:Li. Z. 2002
2074:
2058:
2015:
2011:
2001:
1960:
1956:
1945:
1892:
1888:
1878:
1867:. Retrieved
1863:
1854:
1817:
1813:
1803:
1766:
1762:
1752:
1733:
1727:
1708:
1702:
1665:
1661:
1651:
1616:
1612:
1602:
1569:
1566:Biol. Cybern
1565:
1559:
1550:
1546:
1540:
1495:
1491:
1481:
1462:
1456:
1437:
1431:
1386:
1382:
1372:
1327:
1323:
1313:
1293:
1286:
1235:
1231:
1221:
1205:
1199:
1138:
1134:
1124:
1083:
1079:
1073:
1061:. Retrieved
1046:
1039:
994:
990:
980:
961:
955:
936:
923:
912:. Retrieved
908:the original
902:
895:
870:
862:
758:Connectomics
701:
669:neuroscience
659:
650:
637:neurological
619:
595:
581:
573:
562:
553:frontal lobe
542:
528:
509:
505:
503:
483:
472:
455:
442:Hopfield net
431:
416:
403:
396:
385:
375:
373:
349:
337:
328:
300:
290:
283:
271:
256:
253:Major topics
247:cable theory
243:Wilfrid Rall
200:
165:
148:
138:
126:
82:biologically
79:
75:
43:neuroscience
38:
34:
30:
29:
4389:Engineering
4379:Mathematics
4312:Linguistics
3945:Neuroethics
3792:Neurotology
3425:Conferences
848:Theta model
486:Ising model
451:hippocampus
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4353:Economics
4348:Sociology
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4236:Chemistry
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4051:Neurochip
3817:Cognitive
3742:Neurology
3185:Huxley AF
3163:MIT Press
3095:MIT Press
2790:248488365
2782:1746-8094
2725:1662-453X
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