=== Neural Self-Information Theory for Neural Coding of Real-Time Cognition ===
Traditionally,The themost rateprevalent code, which firing spike rasters were averaged over multiple trialsidea to overcomeexplain firinghow variability,information wasis proposedencoded asby aneurons wayhas for scientists to anaylyzebeen the tuningrate propertiescoding of(Adrian aand givenZotterman, neuron1926). Over However,the itpast is100 obviousyears, theresearchers ratehave coderealized isthat notneurons howin the brain actually uses to represent real-time cognitions because neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability has mystified the brain scientists and posed one of the greatest intellectual challenges as how neural information or cognition is actually encoded in real-time. As such, the variability of neuronal firing has been widely regarded as a noise which iswas often deliberately averaged out during data analyses by the rate-coding method. (seeObviously, belowit section).is clear that the rate code is usful for researcher to anaylyze the data, but is not how the brain actually uses to represent real-time cognitions.
To solveovercome this fundamentalconceptual problemchallenge, [[Joe Z. Tsien]] has recently proposed the ''Neural Self-Information Theory'' which states that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of “positive” or “negative surprisals,” signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. <ref>{{cite journal|last1=Li|first1=M|last2=Tsien|first2=JZ|year=2017|title=Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability|journal=Front Cell Neurosci. |volume=11|page=article 236|doi=10.3389/fncel.2017.00236|pmid=28912685|doi-access=free}}</ref>
Accordingly, Tsien devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. His team revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the “Pareto Principle” that specifies, for many events—including communication—roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural codes arise from the temporal assembly of neural-clique members via ISI variability-based self-information principle. <ref>{{cite journal|last1=Li|first1=M|last2=Xie|first2=K|last3=Kuang|first3=H|last4=Liu|first4=J|last5=Wang|first5=D|last6=Fox |first6=GE|last7=Shi|first7=Z|last8=Chen|first8=L|last9=Zhao|first9=F|last10=Mao|first10=Y|last11=Tsien|first11=JZ|title=Neural Coding of Cell Assemblies via Spike-Timing Self-Information|journal=Cereb Cortex|year=2018|volume=28(7)|page=2563-2576|doi=10.1093/cercor/bhy081|pmid=29688285|doi-access=free}}</ref>
