Temporal coding: Difference between revisions

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'''Temporal coding''' is a type 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 universal definition of temporal coding, though efforts have been made to differentiate the precise timing of spikes in a single neuron which encodes information about a stimulus from synchronized firing of neurons within a localized area. The latter is sometimes referred to as [[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>

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==Finding meaning in patterns==

Simply put, a neural code can be defined as the minimum number of symbols necessary to express all biologically significant information.<ref name="Theunissen F 1995">Theunissen F, Miller JP. ''Temporal Encoding in Nervous Systems: A Rigorous Definition''. Journal of Computational Neuroscience, 2, 149—162; 1995.</ref> There are many hypotheses about an encoding method, two of which are [[neural coding#Rate coding|rate coding]] and temporal coding. Many systems of the body utilize a more complex and information rich coding system than could be encoded in a rate code alone.<ref name="van Hemmen 2006">J. Leo van Hemmen, TJ Sejnowski. 23 Problems in Systems Neuroscience. Oxford Univ. Press, 2006. p.143-158.</ref> The two are often thought to work in conjunction, as in the gustatory system.<ref name="Carleton A 2010">Carleton, Alan, Riccardo Accolla, and Sidney A. Simon. (2010). [http://dx.doi.org/10.1016/j.tins.2010.04.002 "Coding in the mammalian gustatory system"]. ''Trends in Neurosciences'', 33(7):326–334.</ref> </b>

Neurons exhibit high-frequency fluctuations of firing-rates which could be noise or could carry information. Rate coding models suggest that these irregularities are noise, while temporal coding models suggest that they encode information. If the nervous system only used rate codes to convey information, a more consistent, regular firing rate would have been evolutionarily advantageous and likely would have been selected for over time.<ref name="van Hemmen 2006"/> Temporal coding supplies an alternate explanation for the “noise," suggesting that it actually encodes information and affects neural processing. To model this idea, binary symbols can be used to mark the spikes: 1 for a spike, 0 for no spike. Temporal coding allows the sequence 000111000111 to mean something different than 001100110011, even though the mean firing rate is the same for both sequences, at 6 spikes/10 msec.<ref name="Theunissen F 1995"/>

Until recently, scientists had put the most emphasis on rate encoding alone. However, functions of the brain are more temporally precise than the use of only rate encoding would seem to allow. In other words, essential information could be lost due to the inability of the rate code to capture all the available information of the spike train. In addition, responses are different enough between similar (but not identical) stimuli to suggest that the distinct patterns of spikes contain a higher volume of information than is possible to include in a rate code.<ref name="Zador, Stevens">{{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>

==Evidence==

Because it is unclear when a neuron begins encoding a stimulus, neuroscientists must choose a point of reference to compare different spike trains, and may form different conclusions based on the same spike train pattern. Even so, by observing trends between the stimuli and the response, it is possible to find different patterns which are more likely to be elicited by a certain type of stimulus.<ref name="Theunissen F 1995"/> Each stimulus can elicit a variety of responses, and there does not seem to be a one-to-one, stimulus-to-response pattern. Despite this, scientists have found that there is a higher likelihood of certain response trends with specific stimuli,<ref>{{cite book|last=Reike, Warland, de Ruter van Steveninck, Bialek|first=Fred, David Rob, William|title=Spikes: Exploring the Neural Code|year=1997|publisher=Massachusetts Institute of Technology}}</ref> but once patterns have been identified, they must be decoded by cells into synaptic neurotransmitter release and resulting postsynaptic potentials.

For very brief stimuli, a neuron's maximum firing rate may not be fast enough to produce more than a single spike. Due to the density of information about the abbreviated stimulus contained in this single spike, it would seem that the timing of the spike itself would have to convey more information than simply the average frequency of action potentials over a given period of time. This model is especially important for [[sound localization]], which occurs within the brain on the order of milliseconds. The brain must obtain a large quantity of information based on a relatively short neural response. Additionally, if low firing rates on the order of ten spikes per second must be distinguished from arbitrarily close rate coding for different stimuli, then a neuron trying to discriminate these two stimuli may need to wait for a second or more to accumulate enough information. This is not consistent with numerous organisms which are able to discriminate between stimuli in the time frame of milliseconds, suggesting that a rate code is not the only model at work.<ref name="Theunissen F 1995"/>

==Sensory systems==

===The gustatory system===

The mammalian [[gustatory system]] is useful for studying temporal coding because of its fairly distinct stimuli and the easily discernible responses of the organism.<ref>Hallock, Robert M. and Patricia M. Di Lorenzo. (2006). [http://dx.doi.org/10.1016/j.neubiorev.2006.07.005 "Temporal coding in the gustatory system"]. ''Neuroscience & Biobehavioral Reviews'', 30(8):1145–1160.</ref> Temporally encoded information may help an organism discriminate between different tastants of the same category (sweet, bitter, sour, salty, umami) that elicit very similar responses in terms of spike count. The temporal component of the pattern elicited by each tastant may be used to determine its identity (e.g., the difference between two bitter tastants, such as quinine and denatonium). In this way, both rate coding and temporal coding may be used in the gustatory system – rate for basic tastant type, temporal for more specific differentiation.<ref name="Carleton A 2010" />

Research on mammalian gustatory system has shown that there is an abundance of information present in temporal patterns across populations of neurons, and this information is different than that which is determined by rate coding schemes. Groups of neurons may synchronize in response to a stimulus. In studies dealing with the front cortical portion of the brain in primates, precise patterns with short time scales only a few milliseconds in length were found across small populations of neurons which correlated with certain information processing behaviors. However, little information could be determined from the patterns; one possible theory is they represented the higher-order processing taking place in the brain.<ref name="Zador, Stevens"/>

===The visual system===

In the [[Visual cortex#Primary visual cortex (V1)|primary visual cortex]] of macaques, the timing of the first spike relative to the start of the stimulus was found to provide more information than the interval between spikes. However, the interspike interval could be used to encode additional information, which is especially important when the spike rate reaches its limit, as in high-contrast situations. For this reason, temporal coding may play a part in coding defined edges rather than gradual transitions.<ref>Victor, Johnathan D. (2005). [http://dx.doi.org/10.1016/j.conb.2005.08.002 "Spike train metrics"]. ''Current Opinion in Neurobiology'', 15(5):585–592.</ref>

===The olfactory system===

As with the visual system, in [[mitral cell|mitral/tufted cells]] in the [[olfactory bulb]] of mice, first-spike latency relative to the start of a sniffing action seemed to encode much of the information about an odor. This strategy of using spike latency allows for rapid identification of and reaction to an odorant. In addition, some mitral/tufted cells have specific firing patterns for given odorants. This type of extra information could help in recognizing a certain odor, but is not completely necessary, as average spike count over the course of the animal's sniffing was also a good identifier.<ref>Wilson, Rachel I. (2008). [http://www.sciencedirect.com/science/article/pii/S0959438808000883 "Neural and behavioral mechanisms of olfactory perception"]. ''Current Opinion in Neurobiology'', 18(4):408–412.</ref> Along the same lines, experiments done with the olfactory system of rabbits showed distinct patterns which correlated with different subsets of odorants, and a similar result was obtained in experiments with the locust olfactory system.<ref name="Theunissen F 1995"/>

==Applications==

The specificity of temporal coding requires highly refined technology to measure informative, reliable, experimental data. Advances made in [[optogenetics]] allow neurologists to control spikes in individual neurons, offering electrical and spatial single-cell resolution. For example, blue light causes the light-gated ion channel [[channelrhodopsin]] to open, depolarizing the cell and producing a spike. When blue light is not sensed by the cell, the channel closes, and the neuron ceases to spike. The pattern of the spikes matches the pattern of the blue light stimuli. By inserting channelrhodopsin gene sequences into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (e.g., making the mouse turn left).<ref name="youtube.com">Karl Diesseroth, Lecture. “Personal Growth Series: Karl Diesseroth on Cracking the Neural Code.” Google Tech Talks. November 21, 2008. http://www.youtube.com/watch?v=5SLdSbp6VjM</ref> Researchers, through optogenetics, have the tools to effect different temporal codes in a neuron while maintaining the same mean firing rate, and thereby can test whether or not temporal coding occurs in specific neural circuits.<ref name="Han X 2009">Han X, Qian X, Stern P, Chuong AS, Boyden ES. “Informational lesions: optical perturbations of spike timing and neural synchrony via microbial opsin gene fusions.” Cambridge, MA: MIT Media Lad, 2009. PubMed.</ref>

Optogenetic technology also has the potential to enable the correction of spike abnormalities at the root of several neurological and psychological disorders.<ref name="Han X 2009"/> If neurons do encode information in individual spike timing patterns, key signals could be missed by attempting to crack the code while looking only at mean firing rates.<ref name="Theunissen F 1995"/> Understanding any temporally encoded aspects of the neural code and replicating these sequences in neurons could allow for greater control and treatment of neurological disorders such as [[depression (mood)|depression]], [[schizophrenia]], and [[Parkinson’s disease]]. Regulation of spike intervals in single cells more precisely controls brain activity than the addition of pharmacological agents intravenously.<ref name="youtube.com"/>

==See also==

*[[Sparse coding]]

*[[Independent-spike coding]]

*[[Correlation coding]]

*[[Population coding]]

*[[Neural synchronization]]

==References==

{{reflist}}

==Further Reading==

* Rullen, R. V. and Thorpe, S. J. (2001). Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex. ''Neural Computation'', 13(6):1255—1283.

* Vanrullen, R., Guyonneau, R., and Thorpe, S. (2005). Spike times make sense. ''Trends in Neurosciences'', 28(1):1--4.

[[Category:Neural coding]]

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