Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron simulators are, however, capable of simulating at most few tens/hundreds of biophysically accurate neurons in real-time due to the exponential growth in the interneuron communication costs with the number of simulated neurons. In this paper, we propose a real-time, reconfigurable, multichip system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. All parts of the system are generated automatically, based on the neuron connectivity scheme. Experimental results indicate that the proposed system architecture allows the capacity of over 3000 to 19 200 (depending on the connectivity scheme) biophysically accurate neurons over multiple chips.

For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately reproduce and mimic the behaviour of such neurons in real-time. The proposed design in this thesis models an Inferior Olivary Nucleus network on an FPGA device, with a maximised amount of simulated neurons for the given FPGA family type. This has been achieved by the usage of a highly pipelined hybrid neuron network, which executes optimally scheduled floating-point operations that, together with open source IP, has resulted in a cost-effective solutions, capable of simulating responses faster or on par with their biological counterparts.