Towards Neuro-inspired Symbolic Models of Cognition: Linking Neural Dynamics to Behaviors Through Asynchronous Communications

Overview

Journal Cogn Neurodyn

Publisher Springer

Specialty Neurology

Date 2017 Aug 2

PMID 28761554

Citations 12

Authors

Pierre Bonzon

Affiliations

Soon will be listed here.

Abstract

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in "Appendix".

Citing Articles

Grounding Mental Representations in a Virtual Multi-Level Functional Framework.

Bonzon P J Cogn. 2023; 6(1):6.

PMID: 36698786 PMC: 9838229. DOI: 10.5334/joc.249.

Primal-size neural circuits in meta-periodic interaction.

Baram Y Cogn Neurodyn. 2021; 15(2):359-367.

PMID: 33854649 PMC: 7969687. DOI: 10.1007/s11571-020-09613-6.

Modeling the Synchronization of Multimodal Perceptions as a Basis for the Emergence of Deterministic Behaviors.

Bonzon P Front Neurorobot. 2021; 14:570358.

PMID: 33424574 PMC: 7793961. DOI: 10.3389/fnbot.2020.570358.

Probabilistically segregated neural circuits and subcritical linguistics.

Baram Y Cogn Neurodyn. 2020; 14(6):837-848.

PMID: 33101535 PMC: 7568757. DOI: 10.1007/s11571-020-09602-9.

Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights.

Nobukawa S, Wagatsuma N, Nishimura H Cogn Neurodyn. 2020; 14(6):829-836.

PMID: 33101534 PMC: 7568752. DOI: 10.1007/s11571-020-09605-6.

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