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'''Models of neural computation''' are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.
==Introduction==
Due to the complexity of nervous system behavior, the associated experimental error bounds are ill-defined, but the relative merit of the different [[scientific model|models]] of a particular subsystem can be compared according to how closely they reproduce real-world behaviors or respond to specific input signals. In the closely related field of computational [[neuroethology]], the practice is to include the environment in the model in such a way that the [[Control theory#Closed-
In all but the simplest cases, the mathematical equations that form the basis of a model cannot be solved exactly. Nevertheless, computer technology, sometimes in the form of specialized software or hardware architectures, allow scientists to perform iterative calculations and search for plausible solutions. A computer chip or a robot that can interact with the natural environment in ways akin to the original organism is one embodiment of a useful model. The ultimate measure of success is however the ability to make testable predictions.
==General criteria for evaluating models==
===Speed of information processing===
The rate of information processing in biological neural systems are constrained by the speed at which an action potential can propagate down a nerve fibre. This conduction velocity ranges from 1 m/s to over 100 m/s, and generally increases with the diameter of the neuronal process. Slow in the timescales of biologically-relevant events dictated by the speed of sound or the force of gravity, the nervous system overwhelmingly prefers parallel computations over serial ones in time-critical applications.
===Robustness===
A model is robust if it continues to produce the same computational results under
===Gain control===
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the visual system always remain within a much narrower range of amplitudes.<ref name=Ferster>{{cite news
| title = A New Mechanism for Neuronal Gain Control
|
| year = 2002
| volume = 35
| pages = 602–604
| issue=4}}</ref><ref>Klein, S. A., Carney, T., Barghout-Stein, L., & Tyler, C. W. (1997, June). Seven models of masking. In Electronic Imaging'97 (pp. 13–24). International Society for Optics and Photonics.</ref><ref>Barghout-Stein, Lauren. On differences between peripheral and foveal pattern masking. Diss. University of California, Berkeley, 1999.</ref>
| issue=4}}</ref>.▼
===Linearity versus nonlinearity===
A '''linear''' system is one whose response in a specified unit of measure, to a set of inputs considered at once, is the sum of its responses due to the inputs considered individually.
[[Linear algebra|Linear]] systems are easier to analyze mathematically and are a persuasive assumption in many models including the McCulloch and Pitts neuron, population coding models, and the simple neurons often used in [[Artificial neural network]]s. Linearity may occur in the basic elements of a neural circuit such as the response of a postsynaptic neuron, or as an emergent property of a combination of nonlinear subcircuits.<ref name="MolnarHsueh2009">{{cite journal|last1=Molnar|first1=Alyosha|last2=Hsueh|first2=Hain-Ann|last3=Roska|first3=Botond|last4=Werblin|first4=Frank S.|title=Crossover inhibition in the retina: circuitry that compensates for nonlinear rectifying synaptic transmission|journal=Journal of Computational Neuroscience|volume=27|issue=3|year=2009|pages=569–590|issn=0929-5313|doi=10.1007/s10827-009-0170-6 | pmid = 19636690|pmc=2766457}}</ref> Though linearity is often seen as incorrect, there has been recent work suggesting it may, in fact, be biophysically plausible in some cases.<ref>{{Cite journal|last1=Singh|first1=Chandan|last2=Levy|first2=William B.|date=2017-07-13|title=A consensus layer V pyramidal neuron can sustain interpulse-interval coding|journal=PLOS ONE|volume=12|issue=7|pages=e0180839|doi=10.1371/journal.pone.0180839|pmid=28704450|pmc=5509228|arxiv=1609.08213|bibcode=2017PLoSO..1280839S|issn=1932-6203|doi-access=free}}</ref><ref>{{Cite journal|last1=Cash|first1=Sydney|last2=Yuste|first2=Rafael|date=1998-01-01|title=Input Summation by Cultured Pyramidal Neurons Is Linear and Position-Independent|journal=Journal of Neuroscience|language=en|volume=18|issue=1|pages=10–15|issn=0270-6474|pmid=9412481|doi=10.1523/JNEUROSCI.18-01-00010.1998|pmc=6793421|doi-access=free}}</ref>
==Examples==
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The Hodgkin–Huxley model, widely regarded as one of the great achievements of 20th-century biophysics, describes how [[action potential]]s in neurons are initiated and propagated in axons via [[voltage-gated ion channel]]s. It is a set of [[nonlinearity|nonlinear]] [[ordinary differential equation]]s that were introduced by [[Alan Lloyd Hodgkin]] and [[Andrew Huxley]] in 1952 to explain the results of [[voltage clamp]] experiments on the [[squid giant axon]]. Analytic solutions do not exist, but the [[Levenberg–Marquardt algorithm]], a modified [[Gauss–Newton algorithm]], is often used to [[curve fitting|fit]] these equations to voltage-clamp data.
The [[FitzHugh–Nagumo model]] is a
====Solitons====
{{main|Soliton model in neuroscience}}
The [[Soliton model in neuroscience|soliton model]] is an alternative to the [[Hodgkin–Huxley model]] that claims to explain how [[action potentials]] are initiated and conducted in the form of certain kinds of [[Solitary wave (water waves)|solitary]] [[sound]] (or [[density]]) pulses that can be modeled as [[soliton]]s along [[axon]]s, based on a thermodynamic theory of nerve pulse propagation.
====Transfer functions and linear filters====
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The accompanying taxonomy of [[linear filter]]s turns out to be useful in characterizing neural circuitry. Both [[low-pass filter|low-]] and [[high-pass filter]]s are postulated to exist in some form in sensory systems, as they act to prevent information loss in high and low contrast environments, respectively.
Indeed, measurements of the transfer functions of neurons in the [[horseshoe crab]] retina according to linear systems analysis show that they remove short-term fluctuations in input signals leaving only the long-term trends, in the manner of low-pass filters. These animals are unable to see low-contrast objects without the help of optical distortions caused by underwater currents
| title = The Neural Network of the Limulus Retina: From Computer to Behavior
|
| year = 1993
| volume = 33
| pages = 66–78
| doi=10.1093/icb/33.1.66
| doi-access = free
}}</ref><ref>{{cite journal
| doi = 10.2307/1543311
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| pmid = 11341579
| jstor = 1543311
| citeseerx = 10.1.1.116.5190
| s2cid = 18371282
===Models of computations in sensory systems ===
====Lateral inhibition in the retina: Hartline–Ratliff equations====
In the retina, an excited neural receptor can suppress the activity of surrounding neurons within an area called the inhibitory field. This effect, known as [[lateral inhibition]], increases the contrast and sharpness in visual response, but leads to the epiphenomenon of [[Mach bands]]. This is often illustrated by the [[illusion|optical illusion]] of light or dark stripes next to a sharp boundary between two regions in an image of different luminance.
The Hartline-Ratliff model describes interactions within a group of ''p'' [[photoreceptor cell]]s.<ref name=kuhnhadeler>{{cite journal
| doi = 10.1007/BF00319520
| title = Stationary States of the Hartline–Ratliff Model
|
| year = 1987
| volume = 56
| pages = 411–417
| issue = 5–6
| s2cid = 8710876
}}</ref>
<math>r_{p}=\left|\left[e_{p}-\sum_{j=1,j\ne p}^{n}k_{pj}\left|r_{j}-r_{pj}^{o}\right|\right]\right|</math>.
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====Cross-correlation in sound localization: Jeffress model====
According to [[Lloyd A. Jeffress|Jeffress]],<ref>{{cite journal | last1 = Jeffress
Following Fischer and Anderson,<ref>{{cite journal | last1 = Fischer | first1 = Brian J.
<math>y_{R}\left(t\right) - y_{L}\left(t\right)</math>
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====Cross-correlation for motion detection: Hassenstein–Reichardt model====
A motion detector needs to satisfy three general requirements: pair-inputs, asymmetry and nonlinearity.<ref>Borst A, Egelhaaf M., 1989. Principles of visual motion detection. "Trends in
The master equation for response is
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<math>R = A_1(t-\tau)B_2(t) - A_2(t - \tau)B_1(t)</math>
The HR model predicts a peaking of the response at a particular input temporal frequency. The conceptually similar Barlow–Levick model is deficient in the sense that a stimulus presented to only one receptor of the pair is sufficient to generate a response. This is unlike the HR model, which requires two correlated signals delivered in a time ordered fashion. However the HR model does not show a saturation of response at high contrasts, which is observed in experiment. Extensions of the Barlow-Levick model can provide for this discrepancy.<ref>Gonzalo G. de Polavieja, 2006. Neuronal Algorithms That Detect the Temporal Order of Events "Neural Computation" 18 (2006) 2102–2121</ref>
====
This uses a cross-correlation in both the spatial and temporal directions, and is related to the concept of [[optical flow]].<ref>Andrew B. Watson and Albert J. Ahumada, Jr., 1985. Model of human visual-motion sensing "J. Opt. Soc. Am. A" 2(2) 322–341</ref>
===Neurophysiological metronomes: neural circuits for pattern generation===▼
Mutually [[inhibitory]] processes are an unifying motif of all [[central pattern generator]]s. This has been demonstrated in the stomatogastric (STG) nervous system of crayfish and lobsters<ref>Michael P. Nusbaum and Mark P. Beenhakker, A small-systems approach to motor pattern generation, Nature 417, 343–350 (16 May 2002)</ref>. Two and three-cell oscillating networks based on the STG have been constructed which are amenable to mathematical analysis, and which depend in a simple way on on synaptic strengths and overall activity, presumably the knobs on these things.<ref>Cristina Soto-Treviño, Kurt A. Thoroughman and Eve Marder, L. F. Abbott, 2006. Activity-dependent modification of inhibitory synapses in models of rhythmic neural networks Nature Vol 4 No 3 2102–2121</ref>. The mathematics involved is the theory of [[dynamical systems]].▼
===Anti-Hebbian adaptation: spike-timing dependent plasticity===
* {{cite journal | last1 = Roberts | first1 = Patrick D. | last2 = Portfors | first2 = Christine V. | year = 2008 | title = Design principles of sensory processing in cerebellum-like structures| doi = 10.1007/s00422-008-0217-1 | pmid = 18491162 | journal = Biological Cybernetics | volume = 98 | issue = 6| pages = 491–507 | s2cid = 14393814 }}
===Models of [[sensory-motor coupling]] ===
===Feedback and control: models of flight control in the fly===▼
▲====Neurophysiological metronomes: neural circuits for pattern generation====
Flight control in the fly is believed to be mediated by inputs from the visual system and also the [[halteres]], a pair of knob-like organs which measure angular velocity. Integrated computer models of [[Drosophila]], short on neuronal circuitry but based on the general guidelines given by [[control theory]] and data from the tethered flights of flies, have been constructed to investigate the details of flight control [http://www.dickinson.caltech.edu/Research/Grand_Unified_Fly][http://www.dickinson.caltech.edu/Research/Grand_Unified_Fly?action=AttachFile&do=get&target=Dickson_Straw_Poelma_Dickinson_2006.pdf].▼
▲Mutually [[inhibitory]] processes are
▲====Feedback and control: models of flight control in the fly====
▲Flight control in the fly is believed to be mediated by inputs from the visual system and also the [[halteres]], a pair of knob-like organs which measure angular velocity. Integrated computer models of ''[[Drosophila]]'', short on neuronal circuitry but based on the general guidelines given by [[control theory]] and data from the tethered flights of flies, have been constructed to investigate the details of flight control.<ref>{{cite
====Cerebellum sensory motor control====
[[Tensor network theory]] is a theory of [[cerebellum|cerebellar]] function that provides a mathematical model of the [[transformation geometry|transformation]] of sensory [[space-time]] coordinates into motor coordinates and vice versa by cerebellar [[neuronal networks]]. The theory was developed by Andras Pellionisz and [[Rodolfo Llinas]] in the 1980s as a [[geometrization]] of brain function (especially of the [[central nervous system]]) using [[tensor]]s.<ref name="Neuroscience1980-Pellionisz">{{Cite journal| author =Pellionisz, A., Llinás, R. | year =1980 | title =Tensorial Approach to the Geometry of Brain Function: Cerebellar Coordination Via A Metric Tensor | journal = Neuroscience | volume =5 | issue = 7| pages = 1125––1136 | url= https://www.academia.edu/download/31409354/pellionisz_1980_cerebellar_coordination_via_a_metric_tensor_fullpaper.pdf | doi = 10.1016/0306-4522(80)90191-8 | pmid=6967569| s2cid =17303132 }}{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}</ref><ref name="Neuroscience1985-Pellionisz">{{Cite journal| author = Pellionisz, A., Llinás, R. | year =1985 | title= Tensor Network Theory of the Metaorganization of Functional Geometries in the Central Nervous System | journal = Neuroscience | volume =16 | issue =2 | pages = 245–273| doi = 10.1016/0306-4522(85)90001-6 | pmid = 4080158| s2cid =10747593 }}</ref>
==Software modelling approaches and tools==
===Neural networks===
{{main|neural network}}
In this approach the strength and type, excitatory or inhibitory, of synaptic connections are represented by the magnitude and sign of weights, that is, numerical [[coefficients]] <math>w'</math> in front of the inputs
<math>f_{j}=\sum_{i}g\left(w_{ji}'x_{i}+b_{j}\right)</math>.
This response is then fed as input into other neurons and so on. The goal is to
===Genetic algorithms===
[[Genetic algorithms]] are used to evolve neural (and sometimes body) properties in a model brain-body-environment system so as to exhibit some desired behavioral performance. The evolved agents can then be subjected to a detailed analysis to uncover their principles of operation. Evolutionary approaches are particularly useful for exploring spaces of possible solutions to a given behavioral task because these approaches minimize a priori assumptions about how a given behavior ought to be instantiated. They can also be useful for exploring different ways to complete a computational neuroethology model when only partial neural circuitry is available for a biological system of interest.<ref>{{cite
===NEURON===
The [[Neuron (software)|NEURON]] software, developed at Duke University, is a simulation environment for modeling individual neurons and networks of neurons.<ref>{{cite
Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci. 2017; 42(1):1–10.</ref>
==Embodiment in electronic hardware==
===Conductance-based silicon neurons===
Nervous systems differ from the majority of silicon-based computing devices in that they
The most realistic circuits to date make use of [[analogue electronics|analog]] properties of existing [[digital electronics]] (operated under non-standard conditions) to realize Hodgkin–Huxley-type models ''in silico''.<ref>L. Alvadoa, J. Tomasa, S. Saghia, S. Renauda, T. Balb, A. Destexheb, G. Le Masson, 2004. Hardware computation of conductance-based neuron models. Neurocomputing 58–60 (2004)
===Retinomorphic chips===
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==See also==
{{div col|colwidth=22em}}
*[[Computational neuroscience]]▼
* [[
* [[
▲* [[Computational neuroscience]]
*[[Neuroinformatics]]▼
* [[
* [[Neural coding]]
* [[Neural correlate]]
* [[Neural decoding]]
* [[Neuroethology]]
▲* [[Neuroinformatics]]
* [[Quantitative models of the action potential]]
* [[Spiking neural network]]
* [[Systems neuroscience]]
{{div col end}}
==References==
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==External links==
* [http://
* [http://www.
{{Neuroethology}}
{{animal cognition}}
{{DEFAULTSORT:Models Of Neural Computation}}
[[Category:Ethology]]
[[Category:
[[Category:Neuroethology]]
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