Population Coding and Decoding in a Neural Field: A Computational Study

@article{Wu2002PopulationCA,
  title={Population Coding and Decoding in a Neural Field: A Computational Study},
  author={Si Wu and Shun‐ichi Amari and Hiroyuki Nakahara},
  journal={Neural Computation},
  year={2002},
  volume={14},
  pages={999-1026}
}
This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the… 

Implications of Neuronal Diversity on Population Coding

TLDR
It is shown that information capacity of a heterogeneous network is not limited by the correlated noise, but scales linearly with the number of cells in the population, and an optimal linear readout that takes into account the neuronal heterogeneity can extract most of this information.

Population Coding with Motion Energy Filters: The Impact of Correlations

TLDR
It is shown that for all sets of images, correlations convey a large fraction of the information: 40% to 90% of the total information, and ignoring those correlations when decoding leads to considerable information loss from 50% to 93%, depending on the image type.

Neural population codes

  • T. Sanger
  • Computer Science, Biology
    Current Opinion in Neurobiology
  • 2003

On the condition for fast neural computation

  • Si WuS. Amari
  • Computer Science
    Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference
  • 2009
TLDR
Noise has the ‘best’ effect of accelerating computation, in the sense that the strength of external inputs is linearly encoded by the number of neurons firing in a short-time window, and that the neural system can use a simple strategy to decode the input rapidly and accurately.

Unconscious Biases in Neural Populations Coding Multiple Stimuli

TLDR
A novel general framework based on gaussian processes is developed that allows an accurate calculation of the estimate distributions of maximum likelihood decoders, and reveals that the distribution of estimates is bimodal for overlapping stimuli.

Information processing in a neuron ensemble with the multiplicative correlation structure

Sequential Bayesian Decoding with a Population of Neurons

TLDR
This study investigates the performance and implementation of a sequential Bayesian decoding paradigm in the framework of population coding, obtaining the optimal form of prior knowledge that achieves the best estimation result, and investigates its possible biological realization, in the sense that all operations are performed by the dynamics of a recurrent network.

Transmission of Population-Coded Information

TLDR
The results explain how optimal communication of population codes requires the center-surround architectures found in the nervous system and provide explicit predictions on the connectivity parameters.

Accuracy of rate coding: When shorter time window and higher spontaneous activity help.

TLDR
An analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, shows that the Fisher information is nonmonotonic with respect to the length of the observation period, and observes that the signal can be enhanced by noise.
...

References

SHOWING 1-10 OF 44 REFERENCES

Simple models for reading neuronal population codes.

  • H. SeungH. Sompolinsky
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 1993
TLDR
It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic, and although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle.

Neural population codes

  • T. Sanger
  • Computer Science, Biology
    Current Opinion in Neurobiology
  • 2003

Representational Accuracy of Stochastic Neural Populations

TLDR
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.

Sequential Bayesian Decoding with a Population of Neurons

TLDR
This study investigates the performance and implementation of a sequential Bayesian decoding paradigm in the framework of population coding, obtaining the optimal form of prior knowledge that achieves the best estimation result, and investigates its possible biological realization, in the sense that all operations are performed by the dynamics of a recurrent network.

Population Decoding Based on an Unfaithful Model

TLDR
It is proved that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited-range and has advantages of decreasing the computational complexity remarkablely and maintaining a high-level decoding accuracy at the same time.

Statistically Efficient Estimation Using Population Coding

TLDR
This work shows how a nonlinear recurrent network can be used to perform estimation in a near-optimal way while keeping the estimate in a coarse code format, and suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

Statistically Efficient Estimations Using Cortical Lateral Connections

TLDR
It is shown how a non-linear recurrent network can be used to perform these estimation in an optimal way while keeping the estimate in a coarse code format, and suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

The Effect of Correlations on the Fisher Information of Population Codes

TLDR
It is shown that in the biologically relevant regime of parameters positive correlations decrease the estimation capability of the network relative to the uncorrelated population, and negative correlations substantially increase the information capacity of the neuronal population.

Population coding of visual stimuli by cortical neurons tuned to more than one dimension

TLDR
It is shown that a multi-dimensional stimulus may be coded reliably by an ensemble of neurons, using a weighted average population coding model, and this result suggests that neurons may be selective for only 3 to 5 dimensions.