How to solve it: modern heuristics
How to solve it: modern heuristics
Pulsed Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Toward Replacement Parts for the Brain: Implantable Biomimetic Electronics as Neural Prostheses (Bradford Books)
Basic Characteristics and Learning Potential of a Digital Spiking Neuron
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Global exponential stability of impulsive neural networks with variable delay: an LMI approach
IEEE Transactions on Circuits and Systems Part I: Regular Papers
An energy-detector for noncoherent impulse-radio UWB receivers
IEEE Transactions on Circuits and Systems Part I: Regular Papers - Special issue on ISCAS2008
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel hybrid spiking neuron: response analysis and learning potential
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Sequence designs for ultra-wideband impulse radio with optimal correlation properties
IEEE Transactions on Information Theory
New upper bounds for impulse radio sequences
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
Theoretical analysis of various synchronizations in pulse-coupled digital spiking neurons
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Self-organizing digital spike interval maps
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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We present a novel hybrid spiking neuron that is a wired system of shift registers and behaves like an analog spiking neuron model. The presented neuron exhibits various bifurcation phenomena and response characteristics to an input spike train. We derive continuous discrete hybrid maps that can describe the neuron dynamics analytically. By using these maps, the typical mechanisms of bifurcations and responses are clarified. We also present a novel field-programmable gate-array-friendly online learning algorithm for the neuron. It is shown that the algorithm enables the neuron to reconstruct the response characteristics of another neuron with unknown parameter values. Typical learning functions are also validated by experimental measurements.