Computational Neuroscience Seminar - LCN
02.10.09 Friday, 14h15,
Room: tba
Sebastian Gerwinn, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Bayesian Decoding of Leaky-Integrate-and-Fire Neurons
Abstract:
The timing of action potentials in spiking neurons depends on the
temporal dynamics of their inputs and contains information about
temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons
constitute a popular class of encoding models, in which spike times
depend directly on the temporal structure of the inputs. However,
optimal decoding rules for these models have only been studied
explicitly in the noiseless case. Here, we study decoding rules for
probabilistic inference of a continuous stimulus from the spike times
of a population of leaky integrate-and-fire neurons with threshold noise.
We derive three algorithms for approximating
the posterior distribution over stimuli as a function of the observed
spike trains. In addition to a reconstruction of the stimulus we thus
obtain an estimate of the uncertainty as well. Furthermore, we
derive a `spike-by-spike' online decoding scheme that recursively
updates the posterior with the arrival of each new spike. We use these
decoding rules to reconstruct time varying stimuli represented by a
Gaussian process from spike trains of single neurons as well as neural
populations.
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