Methodology and design flow for assisted neural-model implementations in FPGAs.
Field programmable gate arrays (FPGAs) have previously been shown as high-performance platforms for neural-modeling applications. Implementations have traditionally been time-consuming and error-prone, requiring the neural modeler to work outside of their expert domain. This paper demonstrates a new approach to the development of neural models using an auto-generation toolkit. This design tool enables model construction-level alterations (e.g., adjustment of model population size or insertion/deletion of an ionic conductance) within hours and parameter changes on-the-fly. The approach is validated on a 40-neuron pre-Bötzinger complex population model consisting of Hodgkin-Huxley style conductances and fully interconnected synapses. A total of 1880 parameters are on-the-fly user tunable on a free-running model. The resulting implemented model performs at a theoretical 8.7 x real-time utilizing 90% of logic elements within a Xilinx Virtex-4 XC4VSX35-fg676-10 FPGA.
Weinstein
Randall K
RK
Georgia Institute of Technology, Atlanta, GA 30332, USA. rweinstein@simatratechnologies.com
Reid
Michael S
MS
Lee
Robert H
RH
D000465:Algorithms; D032701:Biomimetics / Q000295:instrumentation* / Q000379:methods; D003198:Computer Simulation; D017076:Computer-Aided Design; D004867:Equipment Design; D019544:Equipment Failure Analysis; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*