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;, almost any coding scheme that is not [[neural coding#Rate coding|rate coding]] may be referred to as a temporal code. However,though distinctionsefforts 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>

==Finding meaning in patterns==

==A candidate for the neural code==

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;, the main proponentstwo of these include thewhich idea ofare [[neural coding#Rate coding|rate coding]] and temporal coding. However, manyMany systems of the body utilize a more complex coding system than could be considered reasonable for a rate code alone.<ref Recentname="van researchHemmen suggests2006">J. thatLeo 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 moregustatory informationsystem.<ref densename="Carleton temporalA code2010">Carleton, couldAlan, solveRiccardo thisAccolla, problemand 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 either be noise or could carry information. Rate coding models suggest that these irregularities are noise, butwhile thistemporal seemscoding tomodels besuggest anthat inadequatethey explanationencode for a common occurrenceinformation. If the nervous system only used only rate codes to convey information, evolution should have developed a more consistent, regular firing rate.<ref>J. Leowould vanhave Hemmen,been TJevolutionarily Sejnowskiadvantageous.<ref 23name="van Problems in Systems Neuroscience. Oxford Univ. Press,Hemmen 2006. p.143-158.<"/ref> The theory of temporalTemporal coding offers another solution to the problem of noise by suggesting that the apparent randomness of spikes is not completely arbitrary, but instead encodes information. This solution supplies an alternate explanation for the “noise” and allows for a more information-rich code. 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 of firing 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, or using the mean frequency of spikes to convey information about the stimulusalone. However, functions of the brain are more temporally precise than merethe use of only 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 the available information of the spike train. In addition, responses are different enough between similar, (but not identical) stimuli to suggest that the differentdistinct patterns of spikes contain a higher volume of information than is possible to include in a rate code. However, scientists are not confident about the implications of this additional dimensionality of the temporal 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==

Studying neural coding is a complex process. Because it is unclear when a neuron begins encoding a stimulus, neurologistsneuroscientists must choose a point of reference to compare different spike trains, and may makeform different conclusions regardingbased on the same encodedspike messagetrain 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 unfortunately there isdoes nonot seem to be a one-to-one, stimulus-to-response pattern. HoweverDespite 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> However,but once patterns have been established, there is still the problem of decoding the messages whichthat lie within.

IfFor very brief stimuli, a neuron's is capable ofmaximum firing at a maximum rate ofmay onenot hundredbe spikesfast perenough second,to thenproduce a stimulus of lessmore 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 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,. whereIn this case, 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.

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 name="Theunissen F 1995"/> 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.<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> However, once patterns have been established, there is still the problem of decoding the messages which lie within.

==Sensory systems==

The temporally precise nature of inter-neuronal interactions should also be considered when attempting to establish a probable code. [[Spike-timing-dependent plasticity]] is one canonical example in which the synchronicity between two neural responses 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 arrived 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=http://www.biomednet.com/elecref/0959438800900189|publisher=Elsevier Science Ltd|accessdate=4/08/12}}</ref>

===The gustatory system===

The mammalian [[gustatory system]] is useful for studying temporal coding because of theits 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>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>

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 regardingdealing 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"/>

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. This is not consistent with numerous organisms which are able to discriminate between stimuli in the time frame of milliseconds.

===SensoryThe systemsvisual 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 beprovide more importantinformation than the interval between spikes. However, the interspike interval could be used to encode moreadditional 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 mammalian gustatory system is useful for studying temporal coding because of the 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>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>

The specificity of temporal coding requires highly refined technology to create informative, reliable, experimental data. In 2009, advancesAdvances made in [[optogenetics]] allowed neurologists to control spikes in individual neurons, offering electrical and spatial single-cell resolution. For example, when blue light is perceived, a [[channelrhodopsin]] in pond scum opens, depolarizesdepolarizing the cell, and producesproducing 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 gene sequences into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (i.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>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>

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 name="Zador, Stevens"/>

===The olfactory system===

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"]. ''Current Opinion in Neurobiology'', 15(5):585–592.</ref>

SimilarlyAs 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"/>

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"]. ''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"/>

==Implications==

This optogeneticOptogenetic 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 perturbations of spike timing and neural synchrony via microbial opsin gene fusions.” Cambridge, MA: MIT Media Lad, 2009. PubMed.</ref> ResearchersThere mustis not neglect thea possibility that the neuron encodes information in individual spike timing, asand key signals could be missed inby attempting to crack the code while 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 Disease]].<ref name="youtube.com"/> Controlling the precise spikesspike intervals in single cells is much more effective in controlling brain activity than dumpingadding chemicals and neurotransmitters intravenously.{{Citation needed}} 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 systemcode is important and valuable for neuroscientists and patients alike.

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, when blue light is perceived, a [[channelrhodopsin]] in pond scum opens, 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 gene sequences into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (i.e., 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>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 perturbations 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 name="youtube.com"/> 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==

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==References==

{{reflist}}

==Further Reading==

* 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.