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- W3186720087 abstract "A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a new theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture the realistic variability or fluctuations of neural dynamics not only in time but also in space; such spatial fluctuations, however, are ignored in traditional models of neural computations. These activity patterns implement a new type of probabilistic computations that we call Fractional Neural Sampling (FNS). We further develop a normative model to reveal the algorithmic nature of FNS and its powerful computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We show that FNS accounts for recent experimental observations of perceptual inference and makes experimentally testable predictions." @default.
- W3186720087 created "2021-08-02" @default.
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- W3186720087 date "2021-01-01" @default.
- W3186720087 modified "2023-09-23" @default.
- W3186720087 title "Fractional Neural Sampling: A Theory of Spatiotemporal Probabilistic Computations in Neural Circuits" @default.
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- W3186720087 doi "https://doi.org/10.2139/ssrn.3863569" @default.
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