Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Elements of information theory
Elements of information theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Discrimination thresholds for channel-coded systems
Biological Cybernetics
Mutual information, Fisher information, and population coding
Neural Computation
Neuronal tuning: to sharpen or broaden
Neural Computation
Narrow versus wide turning curves: what's best for a population code?
Neural Computation
The effect of correlated variability on the accuracy of a population code
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
On decoding the responses of a population of neurons from short time windows
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
Multidimensional Encoding Strategy of Spiking Neurons
Neural Computation
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
Neural Computation
Learning population codes by minimizing description length
Neural Computation
Population coding and decoding in a neural field: a computational study
Neural Computation
Optimal Signal Estimation in Neuronal Models
Neural Computation
Optimal neuronal tuning for finite stimulus spaces
Neural Computation
Optimal tuning widths in population coding of periodic variables
Neural Computation
Stimulus-dependent correlations and population codes
Neural Computation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Neuronal coding strategies for two-alternative forced choice tasks
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
The effect of neural adaptation on population coding accuracy
Journal of Computational Neuroscience
Optimal population codes for space: Grid cells outperform place cells
Neural Computation
Hi-index | 0.00 |
Fisher information is used to analyze the accuracy with which a neural population encodes D stimulus features. It turns out that the form of response variability has a major impact on the encoding capacity and therefore plays an important role in the selection of an appropriate neural model. In particular, in the presence of baseline firing, the reconstruction error rapidly increases with D in the case of Poissonian noise but not for additive noise. The existence of limited-range correlations of the type found in cortical tissue yields a saturation of the Fisher information content as a function of the population size only for an additive noise model. We also show that random variability in the correlation coefficient within a neural population, as found empirically, considerably improves the average encoding quality. Finally, the representational accuracy of populations with inhomogeneous tuning properties, either with variability in the tuning widths or fragmented into specialized subpopulations, is superior to the case of identical and radially symmetric tuning curves usually considered in the literature.