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
Feature selection in simple neurons: How coding depends on spiking dynamics
Neural Computation
Learning quadratic receptive fields from neural responses to natural stimuli
Neural Computation
Identifying functional bases for multidimensional neural computations
Neural Computation
Functional identification of spike-processing neural circuits
Neural Computation
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White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input-output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular, the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.