Breeding novel solutions in the brain: a model of Darwinian neurodynamics.

András Szilágyi,Harold P De Vladar,István Zachar,Anna Fedor,Eörs Szathmáry

doi:10.12688/f1000research.9630.2

Abstract

Background: The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain. Methods: We combine known components of the brain - recurrent neural networks (acting as attractors), the action selection loop and implicit working memory - to provide the appropriate Darwinian architecture. We employ a population of attractor networks with palimpsest memory. The action selection loop is employed with winners-share-all dynamics to select for candidate solutions that are transiently stored in implicit working memory. Results: We document two processes: selection of stored solutions and evolutionary search for novel solutions. During the replication of candidate solutions attractor networks occasionally produce recombinant patterns, increasing variation on which selection can act. Combinatorial search acts on multiplying units (activity patterns) with hereditary variation and novel variants appear due to (i)noisy recall of patterns from the attractor networks, (ii)noise during transmission of candidate solutions as messages between networks, and, (iii)spontaneously generated, untrained patterns in spurious attractors. Conclusions: Attractor dynamics of recurrent neural networks can be used to model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants.

Highlights

The idea that functional selection on a large set of neurons and their connections takes place in the brain during development1–3 is experimentally validated4–7
As a proof of principle, we showed that attractor networks find the global optimum in a purely selectionist model if they are pre-trained with attractors that overlap in their basins and lead to the optimum
If learning is allowed during search, the relative frequency of good patterns can be increased by re-training networks with such patterns, so that they are stored in the long-term memory in more copies (Figure 3)

Summary

Introduction

The idea that functional selection on a large set of neurons and their connections takes place in the brain during development is experimentally validated. The idea that functional selection on a large set of neurons and their connections takes place in the brain during development is experimentally validated4–7 As originally portrayed, this process is only one round of variation generation and selection, even if it requires several years. The fact that surplus connections and neurons are pruned during development is well established We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. Methods: We combine known components of the brain – recurrent neural networks (acting as attractors), the action selection loop and implicit working memory – to provide the appropriate Darwinian architecture. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants

Methods

Results

Conclusion