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Fast population coding| old_uid | 93 |
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| title | Fast population coding |
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| start_date | 2005/10/21 |
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| schedule | 15h |
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| online | no |
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| summary | Uncertainty arises in neural computations from noisy processing elements and the formally ill-posed nature of many tasks. Taking appropriate decisions requires that uncertainty be represented and manipulated in a self-consistent manner, likely in standard cortical structures such as population codes. There is a rich literature on the capability of populations of neurons to support computations in the face of the two types of uncertainty. However, one major facet of uncertainty has received rather little attention, namely time, as in a dynamic, rapidly changing world, data is only temporarily relevant. Here, we analyse the computational consequences of encoding stimulus trajectories in the activity of populations of neurons. For a simple, instantaneous, analytically tractable encoder, we show how the correlations induced by natural, smooth stimuli lead to a decoding problem that can only be resolved by access to information that is non-local both in time and across neurons. Such encodings are computationally ruinous; we show that there is an alternative, computationally and representationally powerful, code in which each spike contributes independent information, {\em ie} is independently decodeable. |
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| responsibles | Gutkin |
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