Neural coding: Difference between revisions

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=== Neural Self-Information Theory for Neural Coding of Real-Time Cognition ===

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 anaylyzeanalyze the tuning properties of a given neuron. However, it is obvious the rate code is not what 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>