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Neurons are remarkable among the [[cells (biology)|cells]] of the body in their ability to process signals (i.e., [[light]], [[sound]], [[taste]], [[Olfaction|smell]], [[touch|touch,]] and others) rapidly and transmit information about them over large distances and among vast neural populations. The brain is the highest achievement in the evolution of natural information technologies in terms of speed and efficiency. It follows that, of all coding schemes, the most likely candidate for neural code is the one that produces information (code patterns) most efficiently.
Neurons generate voltage oscillations called [[action potentials]]. All models consider the action potential as a fundamental element of the brain's language. However, the critical issue is the approach to this phenomenon. Physically action potentials are continuous oscillatory processes that differ in duration, amplitude and shape. Neurons demonstrate [[Graded_potential|graded potentials]] that can provide high capacity and efficiency of the code. <ref>Sengupta B, Laughlin SB, Niven JE (2014) Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency. PLOS Computational Biology 10(1): e1003439. https://doi.org/10.1371/journal.pcbi.1003439</ref> Nevertheless, most models regard neural activity as identical discrete events (spikes). If the internal parameters of an action potential are ignored, a spike train can be characterised simply by a series of [[all-or-none law|all-or-none]] point events in time.<ref name="Gerstner">{{cite book|author-link1=Wulfram Gerstner |first1=Wulfram |last1=Gerstner |first2=Werner M. |last2=Kistler |title=Spiking Neuron Models: Single Neurons, Populations, Plasticity |url=https://books.google.com/books?id=Rs4oc7HfxIUC |year=2002 |publisher=Cambridge University Press |isbn=978-0-521-89079-3}}</ref> The lengths of interspike intervals
Such theories assume that the information is contained in the number of spikes in a particular time window (rate code) or their precise timing (temporal code). Whether neurons use rate coding or temporal coding is a topic of intense debate within the neuroscience community, even though there is no clear definition of what these terms mean. Anyway, all these theories are variations of a spiking neuron model.<ref name=":0">{{Cite book|last=Gerstner, Wulfram.|url=https://www.worldcat.org/oclc/57417395|title=Spiking neuron models : single neurons, populations, plasticity|date=2002|publisher=Cambridge University Press|others=Kistler, Werner M., 1969-|isbn=0-511-07817-X|___location=Cambridge, U.K.|oclc=57417395}}</ref> [[Statistical methods]] and methods of [[probability theory]] and stochastic [[point process]]es are widely applied to describe and analyse neuronal firing. Some studies claim that they cracked the neural code <ref>The Memory Code. http://www.scientificamerican.com/article/the-memory-code/</ref><ref>{{cite journal | last1 = Chen | first1 = G | last2 = Wang | first2 = LP | last3 = Tsien | first3 = JZ | year = 2009 | title = Neural population-level memory traces in the mouse hippocampus | journal = PLOS ONE | volume = 4 | issue = 12| page = e8256 | doi = 10.1371/journal.pone.0008256 | pmid = 20016843 | pmc=2788416| bibcode = 2009PLoSO...4.8256C | doi-access = free }}</ref><ref>{{cite journal | last1 = Zhang | first1 = H | last2 = Chen | first2 = G | last3 = Kuang | first3 = H | last4 = Tsien | first4 = JZ | date = Nov 2013 | title = Mapping and deciphering neural codes of NMDA receptor-dependent fear memory engrams in the hippocampus | journal = PLOS ONE | volume = 8 | issue = 11| page = e79454 | doi = 10.1371/journal.pone.0079454 | pmid = 24302990 | pmc=3841182| bibcode = 2013PLoSO...879454Z | doi-access = free }}</ref> and there are several large-scale brain decoding projects.<ref>Brain Decoding Project. http://braindecodingproject.org/</ref><ref>The Simons Collaboration on the Global Brain. https://www.simonsfoundation.org/life-sciences/simons-collaboration-global-brain/</ref> But the actual reading and writing of the neural code remain a challenge facing neuroscience. The problem is that the spiking neuron models run counter to the actual efficiency and speed of the brain. At best, they cover only a part of the observed phenomena and cannot explain others. Perhaps it is time to change the approach to the neural coding process. Recently, models have appeared that answer questions that are unsolvable within the framework of paradigms that consider the action potentials as similar spikes.
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