Neuronal tuning: to sharpen or broaden
Neural Computation
The effect of correlated variability on the accuracy of a population code
Neural Computation
The effect of correlations on the Fisher information of population codes
Proceedings of the 1998 conference on Advances in neural information processing systems II
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Population coding and decoding in a neural field: a computational study
Neural Computation
Representational accuracy of stochastic neural populations
Neural Computation
Neural Computation
Population Coding with Correlation and an Unfaithful Model
Neural Computation
Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates
Neural Computation
Optimal neuronal tuning for finite stimulus spaces
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
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A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., information-maximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.