In research areas such as mobile robotics and computer vision, energy
and computational efficiency have become critical. This has greatly
increased interest in high-efficiency neuromorphic hardware and spiking
neural networks. Because neuromorphic hardware is not yet widely
available, spiking neural network studies are conducted by simulations.
There are numerous simulators available today, each designed for a
specific purpose. In this paper, a novel and open source package (SPAYK)
for simulating spiking neural networks is presented. SPAYK has been
proposed to speed up spiking neural network research. In the majority of
simulators, networks are expressed with differential equations and
require advanced neuroscience knowledge since such simulators are
generally designed for brain and neuroscience research. SPAYK, on the
other hand, is specifically designed as a framework to easily design
spiking neural networks for practical problems. SPAYK is an easy-to-use
Python package. There are three fundamental classes in the core: the
model class for creating neuron groups, the organization class for
simulating tissues, and the learning class for synaptic plasticity.
While developing and testing the SPAYK environment, various experiments
were carried out. This study includes three of these experiments. In the
first experiment, we investigated the behavior of a group of Izhikevich
neurons for visual stimuli. Also, a single Izhikevich neuron has been
trained to respond to a particular label in a supervised manner with
synaptic plasticity. In the second experiment, a well-known experiment
was repeated to validate SPAYK. In this experiment, a neuron trained by
synaptic plasticity can recognize repetitive patterns in a spike train.
In the third experiment, a similar neuron was simulated with stimuli
with multiple labels adapted from the MNIST dataset. It has been shown
that the neuron can classify a particular label by synaptic plasticity.
All these experiments and the SPAYK environment are presented as