The biophysically-meaningful neuron models can be used to simulate human brain behavior. The understanding of neuron behaviours is expected to have prominent role in the fields such as artificial intelligence, treatments of damaged brain, etc. Mostly, the high level of realism of spiking neuron networks and their complexity require a considerable computational resources limiting the size of the...

Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages Spatio-temporal dynamics, diverse coding mechanisms, event-driven properties. This paper is a review SNN order help from other areas know became familiar with the field or even interested SNN. Neuron models, methods, training algorithms, platforms wi...

Spintronic artificial spiking neurons are promising due to their ability closely mimic the leaky integrate-and-fire (LIF) dynamics of biological LIF neuron. However, neuron needs be reset after firing. Few spintronic that have been proposed in literature discuss process detail. In this article, we various schemes achieve a magnetic domain wall (DW)-based which position DW represents membrane po...

We demonstrate neuron-like spiking dynamics in the asymmetrically driven dissipative photonic Bose-Hubbard dimmer model which describes two coupled nonlinear passive Kerr cavities. Spiking appear due to excitable nature of system. In this context, excursions phase space correspond spikes temporal evolution field variables. our case, excitability is mediated by destruction an oscillatory state a...

We consider the problem of feedback control when the controller is constructed solely of deterministic spiking neurons. Although spiking neurons and networks have been the subject of several previous studies, analysis has primarily been restricted to a firing rate model. In contrast, we construct a spike timing based deterministic spiking neuron controller whose control output is one or multipl...

while classical neural networks take a position of a leading method in the machine learning community, spiking neuromorphic systems bring attention and large projects in neuroscience. Spiking neural networks were shown to be able to substitute networks of classical neurons in applied tasks. This work explores recent hardware designs focusing on perspective applications (like convolutional neura...

Spiking neural networks (SNNs) are quickly gaining traction as a viable alternative to deep (DNNs). Compared DNNs, SNNs computationally more powerful and energy efficient. The design metrics (synaptic weights, membrane threshold, etc.) chosen for such SNN architectures often proprietary constitute confidential intellectual property (IP). Our study indicates that implemented using conventional a...

Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity.

Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computational efficiency by mimicking human brain. It is desired to incorporate biological neuronal dynamics, including complex spiking patterns which represent diverse brain activities within neural networks. Earlier hardware ...