activity
20072023
most citedEvolution of Plastic Learning in Spiking Networks via Memristive Connections

74 citations · 446 across the 50 of their papers we have counts for

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Showing 2012Show all

12 papers · 1 filter

cs.NE2012★ 6 cited

Towards the Evolution of Novel Vertical-Axis Wind Turbines

Richard J. Preen, Larry Bull

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but st…

cs.ET2012★ 74 cited

Evolution of Plastic Learning in Spiking Networks via Memristive Connections

Gerard Howard, Ella Gale, Larry Bull +2

This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design pro…

cs.ET2012★ 9 cited

Coevolving Cellular Automata with Memory for Chemical Computing: Boolean Logic Gates in the B-Z Reaction

Christopher Stone, Rita Toth, Ben de Lacy Costello +2

We propose that the behaviour of non-linear media can be controlled automatically through coevolutionary systems. By extension, forms of unconventional computing, i.e., massively p…

nlin.PS2012★ 4 cited

Initiation of waves in BZ encapsulated vesicles using light - towards design of computing architectures

Ben de Lacy Costello, Ishrat Jahan, Matt Ahearn +3

A gas free analogue of the Belousov-Zhabotinsky reaction catalysed by ferroin and encapsulated in phospholipid stabilised vesicles is reported. A reaction mixture which exhibits sp…

cs.ET2012

Toward Turing's A-type Unorganised Machines in an Unconventional Substrate: a Dynamic Representation in Compartmentalised Excitable Chemical Media

Larry Bull, Julian Holley, Ben De Lacy Costello +1

Turing presented a general representation scheme by which to achieve artificial intelligence - unorganised machines. Significantly, these were a form of discrete dynamical system a…

cs.AI2012★ 3 cited

Fuzzy Dynamical Genetic Programming in XCSF

Richard J. Preen, Larry Bull

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Gen…