An integrate-and-fire-type spiking feedback network is discussed in this paper. In our spiking neuron model, analog information expressing processing results is given by the relative relation of spike firing. Therefore, for spiking feedback networks, all neurons should fire (pseudo-)periodically. However, an integrate-and-fire-type neuron generates no spike unless its internal potential exceeds...

In spiking neural networks, signals are transferred by action potentials. The information is encoded in the patterns of neuron activities or spikes. These features create significant differences between spiking neural networks and classical neural networks. Since spiking neural networks are based on spiking neuron models that are very close to the biological neuron model, many of the principles...

Spiking neural P systems are a class of distributed parallel computing models inspired from the way the neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we consider a restricted variant of spiking neural P systems, called homogeneous spiking neural P systems, where each neuron has the same set of rules. The universality of homogeneous spiking...

A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at criticality. How operating near this network state impacts the variability of neuronal spiking is largely unknown. Here we show i...

Neuronal simulations fall in two broad classes: ones that use spiking neurons and ones that don’t. While spiking models match biology better than rate-based systems, computationally they can be quite expensive. The literature offers some attempts to find and use rate-based neuron models that capture important properties of spiking units. One of the most rigorous approaches [1] approximates the ...

This paper highlights and discusses the current challenges in the implementation of large scale Spiking Neural Networks (SNNs) in hardware. A mixed-mode approach to realising scalable SNNs on a reconfigurable hardware platform is presented. The approach uses compact low power analogue spiking neuron cells, with a weight storage capability, interconnected using Network on Chip (NoC) routers. Res...

Abstract Large-scale brain simulations require the investigation of large networks realistic neuron models, usually represented by sets differential equations. Here we report a detailed fine-scale study dynamical response over extended parameter ranges computationally inexpensive model, two-dimensional Rulkov map, which reproduces well spiking and spiking-bursting activity real biological neuro...

A digital spiking neuron is a wired system of shift registers and can generate various spike-trains by adjusting the wiring pattern. In this paper we analyze the basic relations between the wiring pattern and characteristics of the spike-train. Based on the relations, we present a learning algorithm which utilizes successive changes of the wiring pattern. It is shown that the neuron can reprodu...

In the present overview, our wish is to demystify some aspects of coding with spike-timing, through a simple review of well-understood technical facts regarding spike coding. Our goal is a better understanding of the extent to which computing and modeling with spiking neuron networks might be biologically plausible and computationally efficient. We intentionally restrict ourselves to a determin...