Learning to Estimate Dynamical State with Probabilistic Population Codes
نویسندگان
چکیده
منابع مشابه
Learning to Estimate Dynamical State with Probabilistic Population Codes
Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear wi...
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We study the problem of statistically correct inference in networks whose basic representations are population codes. Population codes are ubiquitous in the brain, and involve the simultaneous act ivi ty of many units coding for some low dimensional quantity. A classic example are place cells in the rat hippocampus: these fire when the animal is at a particular place in an environment, so the u...
متن کاملProbabilistic Population Codes
Currently there are two main working hypotheses that purport to answer the first of these questions: what do neural populations represent? The first (standard model) claims that populations encode the value of a stimulus. Whilst the second, more recent perspective, claims they encode a probability distribution over the possible values of a stimulus. The standard model can be caricatured in the ...
متن کاملProbabilistic Interpretation of Population Codes
We present a general encoding-decoding framework for interpreting the activity of a population of units. A standard population code interpretation method, the Poisson model, starts from a description as to how a single value of an underlying quantity can generate the activities of each unit in the population. In casting it in the encoding-decoding framework, we find that this model is too restr...
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When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of in...
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ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2015
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1004554