Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Dániel Czégel,Márton Csillag,Bálint Futó,Eörs Szathmáry,Hamza Giaffar

doi:10.1038/s41598-021-91489-5

Abstract

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.

Highlights

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence
The endeavor of Darwinian neurodynamics (DN) explores the possible ways in which (i) Darwinian dynamics might emerge as an effective high-level algorithmic mechanism from plasticity and activity dynamics of neural populations[4,5] and (ii) this high level algorithmic mechanism fits into cognition
Having described the essential ingredients, we focus on exploring the characteristics of recurrent Darwinian neurodynamics as a coherent computational framework

Summary

Introduction

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. A network composed of the same exact neurons and synapses might produce different activity patterns at different time instances and give rise to different replicators in the evolutionary sense Another fundamental difference between neural Darwinism and Darwinian neurodynamics is that the latter performs bona fide evolutionary search, in which multiple rounds of selection acts on heritable variation (i) combinatorial replicators copy a sequence of low-informational states of neural activity (represented by e.g. bistable neurons), akin to template r eplication[33,34], and( ii) holistic replicators[35] copy one high-informational state, such as memory traces of autoassociative attractor networks[5]. When searching for these algorithmic ingredients of an emergent Darwinian process, three important conceptual issues arise recurrently

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