Two new theorems show that small amounts of additive white noise can improve the bit count or mutual information of several popular models of spiking retinal neurons and spiking sensory neurons. The first theorem gives necessary and sufficient conditions for this noise benefit or stochastic resonance (SR) effect for subthreshold signals in a standard family of Poisson spiking models of retinal ...

The imitative modelling of processes in the brain living beings is an ambitious task. However, advances complexity existing hardware models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics neuronal membrane channels involved operation sodium-potassium pump. dynamic such a system characterised duration picoseconds level...

Computational tasks in biological systems that require short response times can be implemented in a straightforward way by networks of spiking neurons that encode analogue values in temporal coding. We investigate the question how spiking neurons can learn on the basis of diierences between ring times. In particular, we provide learning rules of the Hebbian type in terms of single spiking event...

In this paper, a novel digital modulation technique based on spiking neuron models is proposed. Thanks to the extremely simple and low-power spiking neuron circuits, this modulation scheme allows a less complex receiver at the cost of a more complicated transmitter. Accordingly, it is a good candidate for downlink transmission. Since the transmitted signal is generated based on Shannon’s interp...

This paper proposes a Hopfield-type spiking neural network with modulation of resting membrane potential, and reports retrieval data transition phenomena in its associative memory operation. Spiking neuron models express analog information by the timing of neuronal spike firing events. Since these models operate asynchronously, it is expected that the spiking network operates faster than the co...

In recent years there has been an increasing amount of interest in spiking neural networks, which are more biologically realistic than rate based models. Whilst a lot of simulation work has been carried out on spiking neural networks, relatively little research has been done on the use of spiking neural networks to control robots in an environment. The main obstacle to the development of embodi...