Name
Abstract | ||
|---|---|---|
A real-time, large scale, leaky-integrate-and-fire neural network processor realized using FPGA is presented. This has been designed, as part of a collaborative project, to investigate and implement biologically plausible models of the rodent vibrissae based somatosensory system to control a robot. An emphasis has been made on hard real-time performance of the processor, as it is to be used as part of a feedback control system. This has led to a revision of some of the established modelling protocols used in other hardware spiking neural network processors. The underlying neuron model has the ability to model synaptic noise and inter-neural propagation delays to provide a greater degree of biological plausibility. The processor has been demonstrated modelling real neural circuitry in real-time, independent of the underlying neural network activity. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_161 | ICANN (2) |
Keywords | Field | DocType |
feedback control,propagation delay,real time,somatosensory system,neural network,spiking neural network | Nervous system network models,Computer architecture,Biological neuron model,Physical neural network,Computer science,Time delay neural network,Artificial intelligence,Control system,Artificial neural network,Spiking neural network,Biological neural network,Distributed computing | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
10 | 1.22 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin J. Pearson | 1 | 215 | 26.34 |
| Ian Gilhespy | 2 | 75 | 10.01 |
| Kevin N. Gurney | 3 | 445 | 53.49 |
| Chris Melhuish | 4 | 747 | 87.61 |
| Benjamin Mitchinson | 5 | 86 | 7.90 |
| Mokhtar Nibouche | 6 | 50 | 11.87 |
| Anthony G. Pipe | 7 | 255 | 39.08 |