Temporal coding: Difference between revisions

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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.

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==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>

==Evidence==

Studying neural coding is a complex process. Because it is unclear when a neuron begins encoding a stimulus, neurologists must choose a point of reference to compare different spike trains and may make different conclusions regarding the same encoded message. 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>Theunissen F, Miller JP. ''Temporal Encoding in Nervous Systems: A Rigorous Definition''. Journal of Computational Neuroscience, 2, 149—162; 1995.</ref> Each stimulus can elicit a variety of responses, and unfortunately there is no one-to-one, stimulus-to-response pattern. However, scientists have found that there is a higher likelihood of certain response trends with specific stimuli. Once a pattern has been established, it is another matter altogether to be able to assign meaning to the spike trains.

In addition, the temporally precise nature of neuronal interactions should be considered when attempting to establish a probable code. [[Spike-timing-dependent plasticity]] is one canonical example in which the synchronicity between two neural codes is vital for synapse strengthening or weakening. In the hippocampus, when two [[EPSP]]s arrive simultaneously, the likelihood of their producing an action potential is much higher than the smaller, temporally dispersed EPSPs. An ''in vivo'' study done by intracellular analysis of pyramidal cells in the monkey motor cortex and simultaneous recordings from connected neurons along the thalamocortical and intracortical transmission chain showed that the EPSPs from the cortical cells had a much more drastic impact on surrounding neurons when they were synchronized within two milliseconds of each other. The importance of spike-timing-dependent plasticity in the learning and evolution process is another strong indicator that the neural code is temporal in nature. <ref name="Current Opinion in Neurobiology">{{cite web|last=Singer|first=Wolf|title=Time as coding space?|url=www.biomednet.com/elecref/0959438800900189|publisher=Elsevier Science Ltd|accessdate=4/08/12}}</ref>

If a neuron is capable of firing at a maximum rate of one hundred spikes per second, then a stimulus of less than ten milliseconds would likely elicit only 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 a lot more information than 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, where 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.

===Sensory systems===

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The mammalian gustatory system is useful for studying temporal coding because the stimuli are fairly distinct and it is easy to judge whether or not the coding was successful by looking at an organism's responses.<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"]. <i>Neuroscience & Biobehavioral Reviews</i>, 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, telling quinine from 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>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"]. <i>Trends in Neurosciences</i>, 33(7):326–334.</ref>

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 regarding 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>{{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>

In the primary visual cortex of macaques, the timing of the first spike relative to the start of the stimulus was found to be more important than the interval between spikes. However, the interspike interval could be used to encode more 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"]. <i>Current Opinion in Neurobiology</i>, 15(5):585–592.</ref>

Similarly, in 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"]. <i>Current Opinion in Neurobiology</i>, 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>Theunissen F, Miller JP. ''Temporal Encoding in Nervous Systems: A Rigorous Definition''. Journal of Computational Neuroscience, 2, 149—162; 1995.</ref>

==Implications==

The specificity of temporal coding requires highly refined technology to create informative, reliable experimental data. In 2009, advances made in [[optogenetics]] allowed neurologists to control spikes in individual neurons, offering electrical and spatial single-cell resolution. For example, a [[channelrhodopsin]] in pond scum opens when it senses blue light, depolarizes the cell, and produces a spike. When blue light is not sensed, 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 DNA into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (i.e., making the mouse turn left).<ref> 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>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>

This optogenetic technology has the potential to help researchers crack the neural code and enable the correction of spike abnormalities at the root of several neurological and psychological disorders.<ref>Han X, Qian X, Stern P, Chuong AS, Boyden ES. “Informational lesions: optical pertubatons of spike timing and neural synchrony via microbial opsin gene fusions.” Cambridge, MA: MIT Media Lad, 2009. PubMed.</ref> Researchers must not neglect the possibility that the neuron encodes information in individual spike timing, as key signals could be missed in attempting to crack the code looking only at mean firing-rates. Understanding any temporally encoded aspects of the neural code and being able to replicate these sequences in neurons could allow for greater control and treatment of depression and Parkinson’s.<ref> 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> Controlling the precise spikes intervals in single cells is much more effective in controlling brain activity than dumping chemicals and neurotransmitters intravenously. Such medical possibilities require scientists and communities to address the ethics of such tight control over the brain. While the benefits could be enormous, so could the abuses. However, understanding where the brain uses a temporal coding system is important and valuable for neuroscientists and patients alike.

==See also==

*[[Sparse coding]]

*[[Independent-spike coding]]

*[[Correlation coding]]

*[[Population coding]]

*[[Neural synchronization]]

==References==

{{reflist}}

* Dayan P, Abbott LF. ''Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems''. Cambridge, Massachusetts: The MIT Press; 2001. ISBN 0-262-04199-5

* Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. ''Spikes: Exploring the Neural Code''. Cambridge, Massachusetts: The MIT Press; 1999. ISBN 0-262-68108-0

* 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.

* Theunissen F, Miller JP. ''Temporal Encoding in Nervous Systems: A Rigorous Definition''. Journal of Computational Neuroscience, 2, 149—162; 1995.

* 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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