The effect of environmental temperature on neuronal spiking behaviours is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin-Huxley neuron subject to synaptic stimulus. We find that the spiking threshold exhibits a global minimum in a “comfortable temperature” range where spike initiation needs weakest synaptic strength, indicating the occurren...

We developed spiking neural network control for a modular robotic system. The modular robotic system can be easily assembled by a user who is allowed to make overall behaviors by assembling the physical structure made up of a number of modules. The control of each module (building block) is implemented as a spiking neuron and action potentials are sent through the communication channels of the ...

We present the architecture of a processor chip to be fabricated in digital VLSI-technology which computes the function of a configurable spiking neuron model. The chip is a submodule of the MASPINN-System (Memory Optimized Accelerator for Spiking Neural Networks). The MASPINN-System is designed as a PCI-accelerator-board for real-time simulation of very complex networks of spiking neurons in t...

Learning rules for spiking neural networks have emerged that can classify spatio-temporal spiking patterns as precise target spike trains, although there remains uncertainty in which rule to select that offers the greatest performance. Here, we quantify the performance of a stochastic neuron model in learning to classify input patterns by precise target responses as outputs, and compare its per...

The fast simulation of large networks of spiking neurons is a major task for the examination of biology-inspired vision systems. Networks of this type label features by synchronization of spikes and there is strong demand to simulate these e ects in real world environments. As the calculations for one model neuron are complex, the digital simulation of large networks is not e cient using existi...

We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7−spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of con...

While it is generally agreed that neurons transmit information about their synaptic inputs through spike trains, the code by which this information is transmitted is not well understood. An upper bound on the information encoded is obtained by hypothesizing that the precise timing of each spike conveys information. Here we develop a general approach to quantifying the information carried by spi...

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in...

Spiking Neuron Networks (SNNs) are often referred to as the 3 generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open ...