Abstract—We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Volterra system. The RFN model is a simple spiking neuron model that exhibits dynamic behavior observed in biological neurons, such as fast subthreshold oscillation, post-inhibitory rebound, and frequency preference. The RFN circuit was derived from the Volterra system to mimic s...

page 2 Implementation of a Line Attractor-Based Model of the Gaze Holding Integrator Using Nonlinear Spiking Neuron Models

The developed method extracts bathymetry distributions from multiple satellite image bands. automated remote sensing function is sparsely coded and combines spiking neural net anomaly filtration, spline, multi-band fittings. Survey data were used to identify an activation threshold, decay rate, spline fittings, weighting factors. Errors computed for remotely sensed Landsat images. Multi-band fi...

Based on spiking neuron models and different receptive field models, hierarchical networks are proposed to process visual stimuli, in which multiple overlapped objects are represented by different orientation bars. The main purpose of this paper is to show that hierarchical spiking neural networks are able to segment the objects and bind their pixels to form shapes of objects using local excita...

Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reductio...

We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple input spike trains. Spike trains are transformed into continuous functions through appropriate kernels and then Delta rule is applied. The main advantage of the method is it...