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| 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.
===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
==Examples==
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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
<math>y_{R}\left(t\right) - y_{L}\left(t\right)</math>
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===NEURON===
The [[Neuron (software)|NEURON]] software, developed at Duke University, is a simulation environment for modeling individual neurons and networks of neurons.<ref>{{cite web|url=http://www.neuron.yale.edu/neuron/|title=NEURON - for empirically-based simulations of neurons and networks of neurons|publisher=}}</ref> The NEURON environment is a self-contained environment allowing interface through its [[Graphical user interface|GUI]] or via scripting with [[hoc (programming language)|hoc]] or [[Python (programming language)|python]]. The NEURON simulation engine is based on a Hodgkin–Huxley type model using a Borg–Graham formulation. Several examples of models written in NEURON are available from the online database ModelDB. <ref>McDougal RA, Morse TM, Carnevale T, Marenco L, Wang R, Migliore M, Miller PL, Shepherd GM, Hines ML.
Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci. 2017; 42(1):
==Embodiment in electronic hardware==
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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)
===Retinomorphic chips===
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