A Hebbian Approach to Non-Spatial Prelinguistic Reasoning

Fernando Aguilar-Canto, Hiram Calvo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number281
JournalBrain Sciences
Volume12
Issue number2
DOIs
StatePublished - Feb 2022

Keywords

  • BCM theory
  • Convolutional Neural Networks
  • Hebbian learning
  • Spike Timing-Dependent Plasticity
  • Temporal Difference Learning

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