B . J . Richmond , Model based decoding of spike trains . Page 1 07 / 26 / 02 Model based decoding of spike trains
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چکیده
Reliably decoding neuronal responses requires knowing what aspects of neuronal responses are stimulus related, and which aspects act as noise. Recent work shows that spike trains can be viewed as stochastic samples from the rate variation function, as estimated by the time dependent spike density function (or normalized peristimulus time histogram). Such spike trains are exactly described by order statistics, and can be decoded millisecond-by-millisecond by iterative application of order statistics.
منابع مشابه
Model based decoding of spike trains.
Reliably decoding neuronal responses requires knowing what aspects of neuronal responses are stimulus related, and which aspects act as noise. Recent work shows that spike trains can be viewed as stochastic samples from the rate variation function, as estimated by the time dependent spike density function (or normalized peristimulus time histogram). Such spike trains are exactly described by or...
متن کاملDecoding spike trains instant by instant using order statistics and the mixture-of-Poissons model.
In the brain, spike trains are generated in time and presumably also interpreted as they unfold in time. Recent work (Oram et al., 1999; Baker and Lemon, 2000) suggests that in several areas of the monkey brain, individual spike times carry information because they reflect an underlying rate variation. Constructing a model based on this stochastic structure allows us to apply order statistics t...
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There are two basic problems in the statistical analysis of neural data. The "encoding" problem concerns how information is encoded in neural spike trains: can we predict the spike trains of a neuron (or population of neurons), given an arbitrary stimulus or observed motor response? Conversely, the "decoding" problem concerns how much information is in a spike train, in particular, how well can...
متن کاملModel-based decoding, information estimation, and change- point detection in multi-neuron spike trains
Understanding how stimulus information is encoded in spike trains is a central problem in computational neuroscience. Decoding methods provide an important tool for addressing this problem, by allowing us to explicitly read out the information contained in spike responses. Here we introduce several decoding methods based on point-process neural encoding models (i.e. “forward” models that predic...
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تاریخ انتشار 2002