## Neural Computation

September 2018, Vol. 30, No. 9, Pages 2472-2499
(doi: 10.1162/neco_a_01107)
© 2018 Massachusetts Institute of Technology
ASIC Implementation of a Nonlinear Dynamical Model for Hippocampal Prosthesis
Article PDF (4.06 MB)
Abstract
A hippocampal prosthesis is a very large scale integration (VLSI) biochip that needs to be implanted in the biological brain to solve a cognitive dysfunction. In this letter, we propose a novel low-complexity, small-area, and low-power programmable hippocampal neural network application-specific integrated circuit (ASIC) for a hippocampal prosthesis. It is based on the nonlinear dynamical model of the hippocampus: namely multi-input, multi-output (MIMO)–generalized Laguerre-Volterra model (GLVM). It can realize the real-time prediction of hippocampal neural activity. New hardware architecture, a storage space configuration scheme, low-power convolution, and gaussian random number generator modules are proposed. The ASIC is fabricated in 40 nm technology with a core area of 0.122 mm${}^{2}$ and test power of 84.4 $\mu$W. Compared with the design based on the traditional architecture, experimental results show that the core area of the chip is reduced by 84.94% and the core power is reduced by 24.30%.