Noise adaptation in integrate-and-fire neurons
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Computation in a single Neuron: Hodgkin and Huxley revisited
Neural Computation
Neural Computation
Computation in a single Neuron: Hodgkin and Huxley revisited
Neural Computation
Feature selection in simple neurons: How coding depends on spiking dynamics
Neural Computation
System identification of Drosophila olfactory sensory neurons
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Learning quadratic receptive fields from neural responses to natural stimuli
Neural Computation
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The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimental neuroscience to "ask" neurons which dimensions in stimulus space they are sensitive to and to characterize the nonlinearity of the response. In this article, we apply reverse correlation to the simplest model neuron with temporal dynamics--the leaky integrate-and-fire model--and find that for even this simple case, standard techniques do not recover the known neural computation. To overcome this, we develop novel reverse-correlation techniques by selectively analyzing only "isolated" spikes and taking explicit account of the extended silences that precede these isolated spikes. We discuss the implications of our methods to the characterization of neural adaptation. Although these methods are developed in the context of the leaky integrate-and-fire model, our findings are relevant for the analysis of spike trains from real neurons.