Evolution of connections in SHRUTI networks
Townsend, J; Keedwell, EC; Galton, A
Date: 3 August 2013
Conference paper
Publisher
Neural-Symbolic Learning and Reasoning
Related links
Abstract
SHRUTI is a model of how predicate relations can
be represented and reasoned upon using a network
of spiking neurons, attempting to model the brain’s
ability to perform reasoning using as biologically
plausible a means as possible. This paper extends
the biological plausibility of the SHRUTI model
by presenting a genotype ...
SHRUTI is a model of how predicate relations can
be represented and reasoned upon using a network
of spiking neurons, attempting to model the brain’s
ability to perform reasoning using as biologically
plausible a means as possible. This paper extends
the biological plausibility of the SHRUTI model
by presenting a genotype representation of connections
in a SHRUTI network using indirect encoding
and showing that working networks represented
in this way can be produced through an evolutionary
process. A multi-objective algorithm is used
to minimise the error and the number of weight
changes that take place as a network learns
Computer Science
Faculty of Environment, Science and Economy
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