Nepteron Processor for Real-Time Computation of Conductance-Based Neuronal Networks

The simulation of conductance-based neuronal networks according to the well known Hodgkin-Huxley approach is very computing-intensive, so that in spite of GHz-processors only networks with a few hundred neurons can be calculated in realtime. From a physiological point of view model simplifications are permitted when they have no negative effect on neuronal properties like modulation of the fire rate and impulse groups. Starting with such a model, this paper proposes a new processor architecture with high accuracy and parallelism, which is implemented on a Xilinx-FPGA. It has a significantly increased performance per clock rate coupled with much lower hardware resources than universal processors and is therefore well-suited for the computation of large conductance-based networks.

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