The inverse gaussian distribution: theory, methodology, and applications
The inverse gaussian distribution: theory, methodology, and applications
Mutual information, Fisher information, and population coding
Neural Computation
Neuronal tuning: to sharpen or broaden
Neural Computation
The effect of correlated variability on the accuracy of a population code
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
Predictability, Complexity, and Learning
Neural Computation
Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates
Neural Computation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Steady-state properties of coding of odor intensity in olfactory sensory neurons
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Input identification in the Ornstein-Uhlenbeck neuronal model with signal dependent noise
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Likelihood methods for point processes with refractoriness
Neural Computation
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We study optimal estimation of a signal in parametric neuronal models on the basis of interspike interval data. Fisher information is the inverse asymptotic variance of the best estimator. Its dependence on the parameter value indicates accuracy of estimation. Our models assume that the input signal is estimated from neuronal output interspike interval data where the frequency transfer function is sigmoidal. If the coefficient of variation of the interspike interval is constant with respect to the signal, the Fisher information is unimodal, and its maximum for the most estimable signal can be found. We obtain a general result and compare the signal producing maximal Fisher information with the inflection point of the sigmoidal transfer function in several basic neuronal models.