Statistically efficient estimation using population coding
Neural Computation
Probabilistic interpretation of population codes
Neural Computation
Mutual information, Fisher information, and population coding
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
The effect of correlations on the Fisher information of population codes
Proceedings of the 1998 conference on Advances in neural information processing systems II
Parameter extraction from population codes: A critical assessment
Neural Computation
Population coding and decoding in a neural field: a computational study
Neural Computation
Sequential Bayesian decoding with a population of neurons
Neural Computation
Difficulty of Singularity in Population Coding
Neural Computation
Computing with Continuous Attractors: Stability and Online Aspects
Neural Computation
Singularities Affect Dynamics of Learning in Neuromanifolds
Neural Computation
Correlation and independence in the neural code
Neural Computation
Emergence of attention within a neural population
Neural Networks
Temporal coding of time-varying stimuli
Neural Computation
Population coding with motion energy filters: The impact of correlations
Neural Computation
Dynamics and computation of continuous attractors
Neural Computation
Encoding and decoding spikes for dynamic stimuli
Neural Computation
Top-Down Control of Learning in Biological Self-Organizing Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Stimulus-dependent correlations and population codes
Neural Computation
Change-based inference in attractor nets: Linear analysis
Neural Computation
Population coding, bayesian inference and information geometry
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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This study investigates a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model that neglects the pair-wise correlation between neuronal activities and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited range. The performance of UMLI is compared with that of the maximum likelihood inference based on the faithful model and that of the center-of-mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkably and maintaining high-level decoding accuracy. Moreover, it can be implemented by a biologically feasible recurrent network (Pouget, Zhang, Deneve, & Latham, 1998). The effect of correlation on the decoding accuracy is also discussed.