title: FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.
abstract: A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H 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 step-by-step 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 mini-columns 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 FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated 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 re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.
authors.affiliation: Cognitive Robotic Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran.
authors.forename: Safa
authors.initials: S
authors.lastname: Yaghini Bonabi
authors.affiliation: Research Center of Information Technology, Department of Computer Engineering, Iran University of Science and Technology Tehran, Iran.
authors.forename: Hassan
authors.initials: H
authors.lastname: Asgharian
authors.affiliation: High Performance Embedded Computing Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Safari
authors.affiliation: Cognitive Robotic Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran ; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM Tehran, Iran.
authors.forename: Majid
authors.initials: M
authors.lastname: Nili Ahmadabadi
keywords: FPGA; Hodgkin-Huxley; digital hardware implementation; neural network; neural pool