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'''Temporal coding''' is a model of [[neural coding]] in which a neuron encodes information through the precise timing of [[action potential]]s, or spikes, on a millisecond time scale. There is no precise or universal definition of temporal coding; almost any coding scheme that is not rate coding may be referred to as a temporal code. However, distinctions have been made specifically to differentiate the coding of temporal information, such as phase-locked responses in the auditory system, from the precise timing of spikes in a single neuron that encode information about a stimulus. The term temporal coding is also used to refer to relative timing of spikes from separate neurons, but this is better termed [[correlation coding]].<ref> Dayan P, Abbott LF. ''Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems''. Cambridge, Massachusetts: The MIT Press; 2001. p. 35. ISBN 0-262-04199-5</ref> The model of temporal coding is evidenced in the mammalian gustatory system. It should not be confused with the coding of temporal information.
==A candidate for the neural code==
Simply put, a neural code can be defined as the minimal number of symbols necessary to express all biologically significant information.<ref> Theunissen F, Miller JP. ''Temporal Encoding in Nervous Systems: A Rigorous Definition''. Journal of Computational Neuroscience, 2, 149—162; 1995.</ref> Many systems of the body utilize a more complex coding system than could be considered feasible for a rate code. Neurons exhibit high-frequency fluctuations of firing-rates which are either noise or actually carry information. Rate coding models suggest that these irregularities are noise, but this is perhaps inadequate. If the nervous system used only rate codes to convey information, evolution should have selected for a more consistent, regular firing rate.<ref> J. Leo van Hemmen, TJ Sejnowski. 23 Problems in Systems Neuroscience. Oxford Univ. Press, 2006. p.143-158. </ref> The theory of temporal coding offers another solution to the "noise" problem by suggesting that the seeming randomness of spikes is not indeed random, but encodes information. This solution supplies an explanation for the “noise” and allows for a more information rich code. Binary symbols can be used to mark the spikes, 1 for spike, 0 for no spike. Temporal coding allows sequences like 000111000111 to mean something different than 001100110011, even though the mean rate of firing is the same for both sequences: there are 6spikes/10msec.<ref>Theunissen F, Miller JP. ''Temporal Encoding in Nervous Systems: A Rigorous Definition''. Journal of Computational Neuroscience, 2, 149—162; 1995.</ref>
Until recently, scientists had put the most emphasis on rate encoding, or using the mean frequency of spikes to convey information about the stimulus. However, functions of the brain are more temporally precise than mere rate encoding would seem to allow; in other words, essential information would be lost due to the inability of the rate code to capture all of the information of the spike train. In addition, responses are stochastic enough between identical stimuli to suggest that the different patterns of spikes contain a higher volume of information than is possible to include in a rate code; that is, there is 'extra' information. However, scientists have little certainty of the implications of this additional dimensionality of the temporal code.<ref>{{cite web|last=Zador, Stevens|first=Charles, Anthony|title=The enigma of the brain|url=https://docs.google.com/a/stolaf.edu/viewer?a=v&pid=gmail&attid=0.1&thid=1369b5e1cdf273f9&mt=application/pdf&url=https://mail.google.com/mail/u/0/?ui%3D2%26ik%3D0a436eb2a7%26view%3Datt%26th%3D1369b5e1cdf273f9%26attid%3D0.1%26disp%3Dsafe%26realattid%3Df_h0ty13ea0%26zw&sig=AHIEtbQB4vngr9nDZaMTLUOcrk5DzePKqA|work=© Current Biology 1995, Vol 5 No 12|accessdate=4/08/12}}</ref>
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