A case for spiking neural network simulation based on configurable multiple-FPGA systems |
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Authors: | Shufan Yang Qiang Wu Renfa Li |
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Institution: | (1) Embedded System and Networking Laboratory, School of Computer and Communication, Hunan University, Changsha, 410082, Hunan, China |
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Abstract: | Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural
network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of
the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative
approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously
output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent
parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work.
We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might
allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network
is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual
cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the
circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual
system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual
cortex, leading to more detailed predictions and insights into visual perception phenomenon. |
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Keywords: | Spiking neural network Visual cortex FPGA Configurable |
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