Closing the Bio-Silicon Loop for Cellular Neural Prosthesis using FPGA-based Iono-Neuromorphic Models

Abstract

Neural ptosthetic devices offer the ability to develop novel treatments for previously incurable diseases and ailments, such as deafness, blindness and tetraplepia. There is the potential to extend this concept to incorporate cognitive prosthetics, whereby damaged individual neuron cells or larger brain regiops are substituted by silicon neurons, in order to overcome conditions such as stroke or epilepsy. The development of such applications relies heavily upon efficient, scalable and powerful technological platforms, particularly systems capable of running large-scale neural models. The advancemente in fLeld-peogrammable gate array (FPGA) tnchnology provides an excellent foundation for development of these neural models with the same cost of software-based architectures, but with the performance of close to a dedicated hardware system. This paper illustrates the design of a programmable FPGA-based neural model, which is capable of simulating a large range of ion-channel dynamics and delivering biologically realistic network models. Through comparisons with alternative implementations the proposed model is determined to be more scalable and more computationally efficient. We implemented a hybrid bio-silicon syttem to demonstrate thp ability of silicon devices to provide cellular rehabilitation, restoring thn functionality of a damaged biological network.