Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain

Fernando, Chrisantha, Vasas, Vera, Szathmáry, Eörs and Husbands, Phil (2011) Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain. PLoS ONE, 6 (8). e23534.

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Abstract

We propose a previously unrecognized kind of informational entity in the brain that is capable of acting as the basis for unlimited hereditary variation in neuronal networks. This unit is a path of activity through a network of neurons, analogous to a path taken through a hidden Markov model. To prove in principle the capabilities of this new kind of informational substrate, we show how a population of paths can be used as the hereditary material for a neuronally implemented genetic algorithm, (the swiss-army knife of black-box optimization techniques) which we have proposed elsewhere could operate at somatic timescales in the brain. We compare this to the same genetic algorithm that uses a standard 'genetic' informational substrate, i.e. non-overlapping discrete genotypes, on a range of optimization problems. A path evolution algorithm (PEA) is defined as any algorithm that implements natural selection of paths in a network substrate. A PEA is a previously unrecognized type of natural selection that is well suited for implementation by biological neuronal networks with structural plasticity. The important similarities and differences between a standard genetic algorithm and a PEA are considered. Whilst most experiments are conducted on an abstract network model, at the conclusion of the paper a slightly more realistic neuronal implementation of a PEA is outlined based on Izhikevich spiking neurons. Finally, experimental predictions are made for the identification of such informational paths in the brain.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Chrisantha Fernando
Date Deposited: 06 Feb 2012 20:00
Last Modified: 07 Mar 2017 05:41
URI: http://sro.sussex.ac.uk/id/eprint/23488

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