Neural coding: Difference between revisions

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{{short description|Method by which information is represented in the brain}}

'''Neural coding''' is the transduction of environmental signals and internal signals of the body into neural activity patterns as representations forming a model of reality suitable for purposeful actions and adaptation, preserving the integrity and normal functioning of the body. Deciphering the neural code as a key to understanding the inner workings of the mind in normal and pathological states is the central task in neuroscience since the course of all vital processes of our body depends on the brain's work.

'''Neural coding''' (or '''Neural representation''') is a [[neuroscience]] field concerned with characterising the hypothetical relationship between the [[Stimulus (physiology)|stimulus]] and the individual or ensemble neuronal responses and the relationship among the [[Electrophysiology|electrical activity]] of the neurons in the ensemble.<ref name="Brown">{{cite journal |vauthors=Brown EN, Kass RE, Mitra PP |title=Multiple neural spike train data analysis: state-of-the-art and future challenges |journal=Nat. Neurosci. |volume=7 |issue=5 |pages=456–61 |date=May 2004 |pmid=15114358 |doi=10.1038/nn1228 |s2cid=562815 }}</ref><ref>{{Cite journal|last=Johnson|first=K. O.|date=June 2000|title=Neural coding|journal=Neuron|volume=26|issue=3|pages=563–566|issn=0896-6273|pmid=10896153|doi=10.1016/S0896-6273(00)81193-9|doi-access=free}}</ref> Based on the theory that

sensory and other information is represented in the [[brain]] by [[Biological neural network|networks of neurons]], it is thought that [[neuron]]s can encode both [[Digital data|digital]] and [[analog signal|analog]] information.<ref name="thorpe">{{cite book |first=S.J. |last=Thorpe |chapter=Spike arrival times: A highly efficient coding scheme for neural networks |chapter-url=https://www.researchgate.net/publication/247621744 |format=PDF |pages=91–94 |editor1-first=R. |editor1-last=Eckmiller |editor2-first=G. |editor2-last=Hartmann |editor3-first=G. |editor3-last=Hauske | editor3-link = Gert Hauske |title=Parallel processing in neural systems and computers |url=https://books.google.com/books?id=b9gmAAAAMAAJ |year=1990 |publisher=North-Holland |isbn=978-0-444-88390-2}}</ref>

== Overview ==

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<!-- Image with unknown copyright status removed: [[File:Firing rate.PNG|thumb|400px|'''Figure 2. Time-dependent firinig rates for different stimulus parameters.''' The rasters show multiple trias during which an MT neuron responded to the same moving, random-dot stimulus. (Adapted from Bair and Koch, 1996)]] -->

When precise spike timing or high-frequency firing-rate [[Statistical fluctuations|fluctuations]] are found to carry information, the neural code is often identified as a temporal code.<ref name=":0" /><ref name="Dayan">{{cite book |first1=Peter |last1=Dayan |first2=L. F. |last2=Abbott |title=Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems |url=https://books.google.com/books?id=5GSKQgAACAAJ |year=2001 |publisher=Massachusetts Institute of Technology Press |isbn=978-0-262-04199-7}}</ref> A number of studies have found that the temporal resolution of the neural code is on a millisecond time scale, indicating that precise spike timing is a significant element in neural coding.<ref name="thorpe">{{cite book|last=Thorpe|first=S.J.|url=https://books.google.com/books?id=b9gmAAAAMAAJ|title=Parallel processing in neural systems and computers|publisher=North-Holland|year=1990|isbn=978-0-444-88390-2|editor1-last=Eckmiller|editor1-first=R.|pages=91–94|chapter=Spike arrival times: A highly efficient coding scheme for neural networks|format=PDF|editor2-last=Hartmann|editor2-first=G.|editor3-last=Hauske|editor3-first=G.|editor3-link=Gert Hauske|chapter-url=https://www.researchgate.net/publication/247621744}}</ref><ref name="Daniel">{{cite journal |vauthors=Butts DA, Weng C, Jin J, etal |title=Temporal precision in the neural code and the timescales of natural vision |journal=Nature |volume=449 |issue=7158 |pages=92–5 |date=September 2007 |pmid=17805296 |doi=10.1038/nature06105 |bibcode = 2007Natur.449...92B |s2cid=4402057 }}</ref><ref name=":1" /> Such codes, that communicate via the time between spikes are also referred to as interpulse interval codes, and have been supported by recent studies.<ref>Singh & Levy, [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180839 "A consensus layer V pyramidal neuron can sustain interpulse-interval coding "], ''PLoS ONE'', 2017</ref>

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 neurons would have utilized this code over other less robust options.<ref name="van Hemmen 2006">J. Leo van Hemmen, TJ Sejnowski. 23 Problems in Systems Neuroscience. Oxford Univ. Press, 2006. p.143-158.</ref> 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 from 001100110011, even though the mean firing rate is the same for both sequences, at 6 spikes/10&nbsp;ms.<ref name="Theunissen F 1995"/> Until recently, scientists had put the most emphasis on rate encoding as an explanation for [[post-synaptic potential]] patterns. However, functions of the brain are more temporally precise than the use of only rate encoding seems to allow.<ref name=":1" /> 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|access-date=August 4, 2012}}</ref>