“School of Cognitive Sciences”
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Paper IPM / Cognitive Sciences / 13857  


Abstract:  
A set of techniques for efficient implementation of HodgkinHuxleybased (HH) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is HH model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and stepbystep integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two minicolumns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGAbased network models to investigate the effect of different neurophysiological mechanisms, like voltagegated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and reconfigurability, our approach makes the FPGAbased system a proper candidate for study on neural control of cognitive robots and systems as well.
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