One of the most well established forms of attentional modulation is an increase in firing rate when attention is directed into the receptive field of a neuron. The degree of rate modulation, however, can vary considerably across individual neurons, especially among broad spiking neurons (putative pyramids). We asked whether this heterogeneity might be correlated with a neuronal response propert...

Hough Transform has been widely used to detect lines in images captured by conventional cameras. In this paper, we develop an event-based Hough transform and apply it to a new type of camera, namely Dynamic Vision Sensor (DVS). DVS outputs an asynchronous stream of binary events representing illumination change in the scene. We implement the proposed algorithm in a spiking neural network to det...

“Spikes are the neural code”: this claim is about 15 years old (Shadlen & Newsome, 1994; Rieke, Warland, Steveninck, & Bialek, 1996), preceded by theoretical studies on the underlying mathematical processes (e.g., (Gerstein & Mandelbrot, 1964)), and followed by many developments regarding biological modelling or computational paradigms, or both (e.g., (Thorpe, Delorme, & VanRullen, 2001)). Howe...

Spiking neurons model a type of biological neural system where information is encoded with spike times. In this paper, a new method for decoding input spikes according to their absolute arrival times is proposed. The output times, which are responses to different input patterns, can differentiate these input patterns uniquely. Features of Spiking Neural Networks (SNN) such as actual spike input...

Axonal conduction delays should not be ignored in simulations of spiking neural networks. Here it is shown that by using axonal conduction delays, neurons can display sensitivity to a specific spatiotemporal spike pattern. By using delays that complement the firing times in a pattern, spikes can arrive simultaneously at an output neuron, giving it a high chance of firing in response to that pat...

In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS methods grow almost linearly with the size of the network, wh...