Name
Title | ||
|---|---|---|
A biologically plausible real-time spiking neuron simulation environment based on a multiple-FPGA platform |
Abstract | ||
|---|---|---|
Neurological research has revealed that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of such neuronal systems. The spiking neuron models which are used in simulations can be described mathematically, but the continuous time involved in mathematical models needs to be replaced by discrete time steps. An alternative approach, hardware implementation, provides the possibility of generating independent spikes precisely and simultaneously output spike waves in real biological time, under the premise that the spiking neural network implemented in hardware can take full advantage of hardware-timed speed and reliability. In this work we propose a multi-layered biologically plausible real-time spiking neural network simulation platform that can be used to emulate the operation of biological neural systems (such as elements of the visual cortex) and computational models of such systems. The implementation of a layered spiking neural network using the Xilinx Virtex-4 family of Field Programmable Gate Array (FPGA) is presented. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2082156.2082176 | SIGARCH Computer Architecture News |
Keywords | Field | DocType |
discrete time step,layered spiking neural network,biological neural system,network simulation platform,plausible real-time spiking neuron,simulation environment,multiple-fpga platform,real biological time,neural network simulation,plausible real-time spiking neural,spiking neural network,spiking neuron model,continuous time,computer model,discrete time,mathematical model,real time,power method,field programmable gate array | ENCODE,Random neural network,Computer science,Cyclic redundancy check,Field-programmable gate array,Real-time computing,Computational model,Discrete time and continuous time,Spiking neural network,Mathematical model | Journal |
Volume | Issue | Citations |
39 | 4 | 2 |
PageRank | References | Authors |
0.36 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shufan Yang | 1 | 109 | 15.18 |
| T. Martin Mcginnity | 2 | 518 | 66.30 |