Spikes: exploring the neural code
Spikes: exploring the neural code
Efficient Coding of Time-Relative Structure Using Spikes
Neural Computation
Neural Computation
Multichannel time encoding with integrate-and-fire neurons
Neurocomputing
Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Consistent recovery of sensory stimuli encoded with MIMO neural circuits
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Population encoding with Hodgkin-Huxley neurons
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Synaptic information transfer in computer models of neocortical columns
Journal of Computational Neuroscience
Information theory in neuroscience
Journal of Computational Neuroscience
Channel identification machines
Computational Intelligence and Neuroscience
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We consider a formal model of stimulus encoding with a circuit consisting of a bank of filters and an ensemble of integrate-and-fire neurons. Such models arise in olfactory systems, vision, and hearing. We demonstrate that bandlimited stimuli can be faithfully represented with spike trains generated by the ensemble of neurons. We provide a stimulus reconstruction scheme based on the spike times of the ensemble of neurons and derive conditions for perfect recovery. The key result calls for the spike density of the neural population to be above the Nyquist rate. We also show that recovery is perfect if the number of neurons in the population is larger than a threshold value. Increasing the number of neurons to achieve a faithful representation of the sensory world is consistent with basic neurobiological thought. Finally we demonstrate that in general, the problem of faithful recovery of stimuli from the spike train of single neurons is ill posed. The stimulus can be recovered, however, from the information contained in the spike train of a population of neurons.