Neural Coding of Cell Assemblies via Spike-Timing Self-Information.
Cereb Cortex
; 28(7): 2563-2576, 2018 07 01.
Article
em En
| MEDLINE
| ID: mdl-29688285
ABSTRACT
Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the "Neural Self-Information Theory" 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. Accordingly, we 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. We further 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 coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Percepção do Tempo
/
Encéfalo
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Potenciais de Ação
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Teoria da Informação
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Modelos Neurológicos
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Neurônios
Limite:
Animals
Idioma:
En
Revista:
Cereb Cortex
Assunto da revista:
CEREBRO
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Estados Unidos