期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 107, 期 32, 页码 14419-14424出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1004906107
关键词
adaptation; neural networks; ising model; attractor states
资金
- National Science Foundation [IBN-0344678, EF-0928048]
- National Institutes of Health (NIH) [R01 EY08124, T32-07035]
- IAS, Princeton
- Israel Science Foundation [1525/08]
- Center for Complexity Science, Minerva Foundation
- Clore Center for Biological Physics
- Gruber Foundation
In retina and in cortical slice the collective response of spiking neural populations is well described by maximum-entropy models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete-stimulus decorrelation, error correction, and independent encoding. These functions reflected a trade-off between efficient consumption of finite neural bandwidth and the use of redundancy to mitigate noise. Spontaneous activity in the optimal network reflected stimulus-induced activity patterns, and single-neuron response variability overestimated network noise. Our analysis suggests that rather than having a single coding principle hardwired in their architecture, networks in the brain should adapt their function to changing noise and stimulus correlations.
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