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====Cross-correlation in sound localization: Jeffress model====
According to [[Lloyd A. Jeffress|Jeffress]],<ref>{{cite journal | last1 = Jeffress | first1 = L.A. | year = 1948 | title = A place theory of sound localization | url = | journal = Journal of Comparative and Physiological Psychology | volume = 41 | issue = | pages = 35–39 | doi=10.1037/h0061495 | pmid=18904764}}</ref> in order to compute the ___location of a sound source in space from [[interaural time difference]]s, an auditory system relies on [[Analog delay line|delay lines]]: the induced signal from an [[ipsilateral]] auditory receptor to a particular neuron is delayed for the same time as it takes for the original sound to go in space from that ear to the other. Each postsynaptic cell is differently delayed and thus specific for a particular inter-aural time difference. This theory is equivalent to the mathematical procedure of [[cross-correlation]].
Following Fischer and Anderson,<ref>Brian J. Fischer and Charles H. Anderson, 2004. A computational model of sound localization in the barn owl ''Neurocomputing" 58–60 (2004) 1007–1012</ref> the response of the postsynaptic neuron to the signals from the left and right ears is given by
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===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 journal|url=http://www.scholarpedia.org/article/Computational_neuroethology|title=Computational neuroethology|first1=Randall|last1=Beer|first2=Hillel|last2=Chiel|date=4 March 2008|publisher=|volume=3|issue=3|doi=10.4249/scholarpedia.5307|journal=Scholarpedia|pages=5307}}</ref>
===NEURON===
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Nervous systems differ from the majority of silicon-based computing devices in that they resemble [[analog computer]]s (not [[digital data]] processors) and massively [[parallel computing|parallel]] processors, not [[von Neumann architecture|sequential]] processors. To model nervous systems accurately, in real-time, alternative hardware is required.
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) 109 – 115</ref><ref>{{cite journal|url=http://www.scholarpedia.org/article/Silicon_neurons|title=Silicon neurons|first1=Giacomo|last1=Indiveri|first2=Rodney|last2=Douglas|first3=Leslie|last3=Smith|date=29 March 2008|publisher=|volume=3|issue=3|doi=10.4249/scholarpedia.1887|journal=Scholarpedia|pages=1887}}</ref>
===Retinomorphic chips===
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