Neural coding: Difference between revisions

Revision as of 22:34, 11 January 2024

Revision as of 06:34, 15 January 2024

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Traditionally, the rate code, which firing spike rasters were averaged over multiple trials to overcome firing variability, was proposed as a way for scientists to anaylyze the tuning properties of a given neuron. However, it is obvious the rate code is not how 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 is widely regarded as a noise which is often deliberately averaged out during data analyses by the rate-coding method (see below section).

To solve this fundamental problem, [[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|pmc=5582596}}</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|pmc=5998964}}</ref>

Another major benefit of the neural self-information coding principle is that such cognition-level information can be naturally coupled with and extended to the basic principles underlying intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies.