Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Simulating Neurons in Reaction-Diffusion Chemistry
AU - Stovold, James
AU - O’Keefe, Simon
PY - 2012/3/22
Y1 - 2012/3/22
N2 - Diffusive Computation is a method of using diffusing particles as a representation of data. The work presented attempts to show that through simulating spiking neurons, diffusive computation has at least the same computational power as spiking neural networks. We demonstrate (by simulation) that wavefronts in a Reaction-Diffusion system have a cumulative effect on concentration of reaction components when they arrive at the same point in the reactor, and that a catalyst-free region acts as a threshold on the initiation of an outgoing wave. Spiking neuron models can be mapped onto this system, and therefore RD systems can be used for computation using the same models as are applied to spiking neurons.
AB - Diffusive Computation is a method of using diffusing particles as a representation of data. The work presented attempts to show that through simulating spiking neurons, diffusive computation has at least the same computational power as spiking neural networks. We demonstrate (by simulation) that wavefronts in a Reaction-Diffusion system have a cumulative effect on concentration of reaction components when they arrive at the same point in the reactor, and that a catalyst-free region acts as a threshold on the initiation of an outgoing wave. Spiking neuron models can be mapped onto this system, and therefore RD systems can be used for computation using the same models as are applied to spiking neurons.
U2 - 10.1007/978-3-642-28792-3_19
DO - 10.1007/978-3-642-28792-3_19
M3 - Conference contribution/Paper
SN - 978-3-642-28791-6
VL - 7223
BT - IPCAT 2012. Lecture Notes in Computer Science
PB - Springer
ER -