Abstract:
Simulating Spiking Neural Networks (SNN) models is a research field attracting the interest
of researchers from various fields, from biology to computer science. The final objective is
understanding the mechanisms defining the human brain working. Multiple neural models have
been proposed, each with their peculiarities, from the very complex and biologically realistic
Hodgkin-Huxley neuron model to the very simple leaky integrate-and-fire neuron. Researchers
can, depending on the objective, choose which model to use in their simulation. For an efficient
simulation of large population of neurons using these models, there need to be a real parallel
system architecture and biologically realistic simulator. This research work revolves around
using a universally accepted biologically accurate NEST (software simulator) and SpiNNaker
(hardware based simulator). During this research, Hodgkin Huxley model has been implemented
for the first time on SpiNNaker using fixed point notation and its results have been verified with
those from NEST. Similarly, a newly proposed AJ neural model has been implemented for the
first time over NEST and SpiNNaker and we successfully verified its results with those from
MATLAB. The research contributed in devising implementation libraries for these two models
for researchers interested to simulate neural populations over SpiNNaker and NEST using these
models.
