Neurons are complex biological entities which form the basis of nervous systems. Insight can be gained into neuron behavior through the use of computer models and as a result many such models have been developed. However, there exists a trade-off between biological accuracy and simulation time with the most realistic results requiring extensive computation. To address this issue, a novel approach is described in this paper that allows complex models of real biological systems to be simulated at a speed greater than real time and with excellent accuracy. The approach is based on a specially developed neuron model VHDL library which allows complex neuron systems to be implemented on Field Programmable Gate Array (FPGA) hardware. The locomotion system of the nematode C. elegans is used as a case study and the measured results show that the real time FPGA based implementation performs 288 times faster than traditional ModelSim simulations for the same accuracy.

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ABSTRACT

A major problem in computational neuroscience is that large scale biologically realistic neuron simulations require massive amounts of computing resources, which in turn requires large amounts of power. This poses a significant problem when we look toward a potential future where machines have "silicon brains". In this paper we build on previous VHDL neuron work by building a programmable neuron device housing 116 neurons and 200 synapses to perform realistic, real-time simulations of neuron networks in hardware. This flexible architecture is loaded with the C. elegans locomotion system which demonstrates that the behavior of the programmable architecture is the same as the behavior of the design from previous work.