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====Watson–Ahumada model for motion estimation in humans====
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 a 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 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===
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* {{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 }}
===Models of [[sensory-motor coupling]] ===
===Feedback and control: models of flight control in the fly===▼
▲====Neurophysiological metronomes: neural circuits for pattern generation====
▲Mutually [[inhibitory]] processes are a 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 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]].
▲====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 web|url=http://strawlab.org/2011/03/23/grand-unified-fly/|title=the Grand Unified Fly (GUF) model|publisher=}}</ref><ref>http://www.mendeley.com/download/public/2464051/3652638122/d3bd7957efd2c8a011afb0687dfb6943731cb6d0/dl.pdf</ref>
====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 | id = | 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}}</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| url = https://s3.amazonaws.com/academia.edu.documents/31409352/pellionisz_llinas_tensor_tnt_of_metaorganization_1985.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1540567963&Signature=7oENMoA9yBITrBLNf0orulY1uOA%3D&response-content-disposition=inline%3B%20filename%3DTensor_Network_Theory_of_Metaorganizatio.pdf | doi = 10.1016/0306-4522(85)90001-6 | pmid = 4080158}}</ref>
==Software modelling approaches and tools==
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