This paper studies theoretically the use of a Q-switch laser with side light injection as spiking all-optical neuron for photonic neural networks (PSNN). Ordinary differential equations multi-section are presented, including terms gain quenching and saturable absorption. The behaviour mimics that ultrafast pico-second scale response low power control signals.

We consider extended variants of spiking neural P systems with decaying spikes (i.e., the spikes have a limited lifetime) and/or total spiking (i.e., the whole contents of a neuron is erased when it spikes). Although we use the extended model of spiking neural P systems, these restrictions of decaying spikes and/or total spiking do not allow for the generation or the acceptance of more than reg...

Spiking neural P systems with anti-spikes (shortly named ASN P systems) are a class of distributed and parallel neural-like computing systems. Besides spikes, neurons in ASN P systems can also contain a number of anti-spikes. Whenever spikes and anti-spikes meet in a neuron, they annihilate each other immediately in a maximal manner, that is, the annihilation has priority over neuron’s spiking....

BACKGROUND A fundamental process underlying all brain functions is the propagation of spiking activity in networks of excitatory and inhibitory neurons. In the neocortex, although functional connections between pairs of neurons have been studied extensively in brain slices, they remain poorly characterized in vivo, where the high background activity, global brain states, and neuromodulation can...

In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an ar...

Spiking neural networks are characterised by the spiking neuron models they use and how these spiking neurons process information communicated through spikes – the neural code. We demonstrate a plausible spiking neural network based on Spike Response Models and predictive spike-coding. When combined with a plausible reinforcement learning strategy – Attention Gated REinforcement Learning (AGREL...

Studies of cortical neurons in monkeys performing short-term memory tasks have shown that information about a stimulus can be maintained by persistent neuron firing for periods of many seconds after removal of the stimulus. The mechanism by which this sustained activity is initiated and maintained is unknown. In this article we present a spiking neural network model of short-term memory and use...

�-machines can be used to study the dynamics of neural spike trains and reveal spiking patterns. By constructing �-machines we quantify the randomness and structure of three dynamical neuron models: The Linear Integrate and Fire neuron, the Quadratic Integrate and Fire neuron and the Izhikevich neuron.

SUMMARY In this paper, we propose an analog CMOS circuit which achieves spiking neural networks with spike-timing dependent synaptic plasticity (STDP). In particular, we propose a STDP circuit with symmetric function for the first time, and also we demonstrate associative memory operation in a Hopfield-type feedback network with STDP learning. In our spiking neuron model, analog information exp...