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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 vary in duration, amplitude and shape. Neurons demonstrate [[graded potential]]s that can provide high capacity and efficiency of the code.<ref>{{cite journal |last1=Sengupta |first1=Biswa |last2=Laughlin |first2=Simon Barry |last3=Niven |first3=Jeremy Edward |title=Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency |journal=[[PLOS Computational Biology]] |date=23 January 2014 |volume=10 |issue=1 |pages=e1003439 |doi=10.1371/journal.pcbi.1003439|pmid=24465197 |pmc=3900385 |bibcode=2014PLSCB..10E3439S }}</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 characterized simply by a series of [[All-or-none law|all-or-none]] point events in time.<ref name="Gerstner2">{{cite book |last1=Gerstner |first1=Wulfram |url=https://books.google.com/books?id=Rs4oc7HfxIUC |title=Spiking Neuron Models: Single Neurons, Populations, Plasticity |last2=Kistler |first2=Werner M. |publisher=Cambridge University Press |year=2002 |isbn=978-0-521-89079-3 |author-link1=Wulfram Gerstner}}</ref> The lengths of interspike intervals can also vary.<ref name="Stein2">{{cite journal |vauthors=Stein RB, Gossen ER, Jones KE |date=May 2005 |title=Neuronal variability: noise or part of the signal? |journal=Nat. Rev. Neurosci. |volume=6 |issue=5 |pages=389–97 |doi=10.1038/nrn1668 |pmid=15861181 |s2cid=205500218}}</ref> But they are usually ignored in the currently prevailing models of the neural code.
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=":02">{{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 analyze neuronal firing. Some studies claim that they cracked the neural code <ref>{{cite journal |last1=Ghandour |first1=K. |last2=Ohkawa |first2=N. |last3=Fung |first3=C. C. A. |last4=Asai |first4=H. |last5=Saitoh |first5=Y. |last6=Takekawa |first6=T. |last7=Okubo-Suzuki |first7=R. |last8=Soya |first8=S. |last9=Nishizono |first9=H. |last10=Matsuo |first10=M. |last11=Osanai |first11=M. |last12=Sato |first12=M. |last13=Ohkura |first13=M. |last14=Nakai |first14=J. |last15=Hayashi |first15=Y. |last16=Sakurai |first16=T. |last17=Kitamura |first17=T. |last18=Fukai |first18=T. |last19=Inokuchi |first19=K. |date=2019 |title=Orchestrated ensemble activities constitute a hippocampal memory engram |url=https://doi.org/10.1038/s41467-019-10683-2 |journal=Nature Communications |volume=10 |pages= | pmid=31201332 | doi=10.1038/s41467-019-10683-2| doi-access=free}}</ref><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 |bibcode=2009PLoSO...4.8256C |doi=10.1371/journal.pone.0008256 |pmc=2788416 |pmid=20016843 |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 |bibcode=2013PLoSO...879454Z |doi=10.1371/journal.pone.0079454 |pmc=3841182 |pmid=24302990 |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. Recently, models have appeared that answer questions that are unsolvable within the framework of paradigms that consider the action potentials as similar spikes.{{citation needed|date=January 2022}}. As technology has advanced, new architecture has been proposed which consist of neurons that can potentially carry a larger number of synapses. These synapses have not only make connections but they are capable of computing their excitations level themselves and adjust those connections.<ref>{{Cite journal |last1=Fernando |first1=Subha |last2=Yamada |first2=Koichi |last3=Marasinghe |first3=Ashu |date=July 2011 |title=Observed Stent's anti-Hebbian postulate on dynamic stochastic computational synapses |url=http://dx.doi.org/10.1109/ijcnn.2011.6033379 |journal=The 2011 International Joint Conference on Neural Networks |pages=1336–1343 |publisher=IEEE |doi=10.1109/ijcnn.2011.6033379|isbn=978-1-4244-9635-8 |s2cid=14983385 }}</ref>
== Encoding and decoding ==
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