A Hebbian Approach to Non-Spatial Prelinguistic Reasoning.

Brain Sci

Computational Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, Mexico.

Published: February 2022

This research integrates key concepts of Computational Neuroscience, including the Bienestock-CooperMunro (BCM) rule, Spike Timing-Dependent Plasticity Rules (STDP), and the Temporal Difference Learning algorithm, with an important structure of Deep Learning (Convolutional Networks) to create an architecture with the potential of replicating observations of some cognitive experiments (particularly, those that provided some basis for sequential reasoning) while sharing the advantages already achieved by the previous proposals. In particular, we present Ring Model B, which is capable of associating visual with auditory stimulus, performing sequential predictions, and predicting reward from experience. Despite its simplicity, we considered such abilities to be a first step towards the formulation of more general models of prelinguistic reasoning.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870645PMC
http://dx.doi.org/10.3390/brainsci12020281DOI Listing

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A Hebbian Approach to Non-Spatial Prelinguistic Reasoning.

Brain Sci

February 2022

Computational Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, Mexico.

This research integrates key concepts of Computational Neuroscience, including the Bienestock-CooperMunro (BCM) rule, Spike Timing-Dependent Plasticity Rules (STDP), and the Temporal Difference Learning algorithm, with an important structure of Deep Learning (Convolutional Networks) to create an architecture with the potential of replicating observations of some cognitive experiments (particularly, those that provided some basis for sequential reasoning) while sharing the advantages already achieved by the previous proposals. In particular, we present Ring Model B, which is capable of associating visual with auditory stimulus, performing sequential predictions, and predicting reward from experience. Despite its simplicity, we considered such abilities to be a first step towards the formulation of more general models of prelinguistic reasoning.

View Article and Find Full Text PDF

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