Cable theory for dendritic neurons
Methods in neuronal modeling
The NEURON simulation environment
Neural Computation
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 01
Biologically-Inspired Massively-Parallel Architectures - Computing Beyond a Million Processors
ACSD '09 Proceedings of the 2009 Ninth International Conference on Application of Concurrency to System Design
Simple model of spiking neurons
IEEE Transactions on Neural Networks
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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 Caenorhabditis 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.