Neural Comput - Characterization of minimum error linear coding with sensory and neural noise.

Tópicos

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Resumo

Robust coding has been proposed as a solution to the problem of minimizing decoding error in the presence of neural noise. Many real-world problems, however, have degradation in the input signal, not just in neural representations. This generalized problem is more relevant to biological sensory coding where internal noise arises from limited neural precision and external noise from distortion of sensory signal such as blurring and phototransduction noise. In this note, we show that the optimal linear encoder for this problem can be decomposed exactly into two serial processes that can be optimized separately. One is Wiener filtering, which optimally compensates for input degradation. The other is robust coding, which best uses the available representational capacity for signal transmission with a noisy population of linear neurons. We also present spectral analysis of the decomposition that characterizes how the reconstruction error is minimized under different input signal spectra, types and amounts of degradation, degrees of neural precision, and neural population sizes.

Resumo Limpo

robust code propos solut problem minim decod error presenc neural nois mani realworld problem howev degrad input signal just neural represent general problem relev biolog sensori code intern nois aris limit neural precis extern nois distort sensori signal blur phototransduct nois note show optim linear encod problem can decompos exact two serial process can optim separ one wiener filter optim compens input degrad robust code best use avail represent capac signal transmiss noisi popul linear neuron also present spectral analysi decomposit character reconstruct error minim differ input signal spectra type amount degrad degre neural precis neural popul size

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