A minimal dynamical system and analog circuit for non-associative learning

TL;DR

This paper explores the simplest dynamical systems and analog circuits capable of modeling non-associative learning, such as habituation, aiming to inform neuromorphic computing with minimalistic biological-inspired structures.

Contribution

It introduces a minimal dynamical system and circuit model that implement non-associative learning, highlighting the simplest possible structures for such processes.

Findings

Proposes a minimal dynamical system for habituation

Designs an analog circuit implementing non-associative learning

Provides insights into simple biological-inspired learning mechanisms

Abstract

Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.