Associative neural memories
Spikes: exploring the neural code
Spikes: exploring the neural code
Shift Register Sequences
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates
Neural Computation
Optimization methods for spiking neurons and networks
IEEE Transactions on Neural Networks
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We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes only the neuron characteristics and the other of which describes only the signal characteristics. The approximation shows particularly clearly that signal autocorrelations and cross-correlations arise as natural features of the interspike-interval density and are particularly clear for small signals and moderate noise. We show that this model simplifies the design of spiking neuron cross-correlation systems and describe a four-neuron mutual inhibition network that generates a cross-correlation output for two input signals.