Mutual information, Fisher information, and population coding
Neural Computation
Neuronal tuning: to sharpen or broaden
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Representational accuracy of stochastic neural populations
Neural Computation
Optimal short-term population coding: when fisher information fails
Neural Computation
Mean instantaneous firing frequency is always higher than the firing rate
Neural Computation
Difficulty of Singularity in Population Coding
Neural Computation
Optimal Signal Estimation in Neuronal Models
Neural Computation
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To study sensory neurons, the neuron response is plotted versus stimulus level. The aim of the present contribution is to determine how well two different levels of the incoming stimulation can be distinguished on the basis of their evoked responses. Two generic models of response function are presented and studied under the influence of noise. We show in these noisy cases that the most suitable signal, from the point of view of its identification, is not unique. To obtain the best identification we propose to use measures based on Fisher information. For these measures, we show that the most identifiable signal may differ from that derived when the noise is neglected.