The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Parabolic bursting in an excitable system coupled with a slow oscillation
SIAM Journal on Applied Mathematics
Spikes: exploring the neural code
Spikes: exploring the neural code
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Computation in a single Neuron: Hodgkin and Huxley revisited
Neural Computation
What causes a Neuron to spike?
Neural Computation
Firing rate of the noisy quadratic integrate-and-fire neuron
Neural Computation
Identification of Nonlinear Physiological Systems
Identification of Nonlinear Physiological Systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Single neuron computation: From dynamical system to feature detector
Neural Computation
Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
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The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes and a nonlinear function that relates stimulus to firing probability. In many sensory systems, these two components of the coding strategy are found to adapt to changes in the statistics of the inputs in such a way as to improve information transmission. Here, we show for two simple neuron models how feature selectivity as captured by the spike-triggered average depends on both the parameters of the model and the statistical characteristics of the input.