Bayesian population decoding of spiking neurons Export

@article{Gerwinn_2009_bayesian, 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.}, author = {Gerwinn, Sebastian and Macke, Jakob H. and Bethge, Matthias}, citeulike-article-id = {6032466}, citeulike-linkout-0 = {http://www.frontiersin.org/computationalneuroscience/paper/10.3389/neuro.10/021.2009/}, citeulike-linkout-1 = {http://dx.doi.org/10.3389/neuro.10/021.2009}, doi = {10.3389/neuro.10/021.2009}, issn = {ISSN 1662-5188}, journal = {Frontiers in Computational Neuroscience}, keywords = {bayesian, decoding, population}, posted-at = {2009-10-29 15:32:31}, priority = {2}, title = {Bayesian population decoding of spiking neurons}, url = {http://dx.doi.org/10.3389/neuro.10/021.2009}, volume = {3}, year = {2009} }

TY - JOUR ID - Gerwinn_2009_bayesian L3 - citeulike-article-id:6032466 N2 - 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. JF - Frontiers in Computational Neuroscience SN - ISSN 1662-5188 TI - Bayesian population decoding of spiking neurons VL - 3 KW - bayesian KW - decoding KW - population AU - Gerwinn, Sebastian AU - Macke, Jakob AU - Bethge, Matthias PY - 2009/// UR - http://dx.doi.org/10.3389/neuro.10/021.2009 ER -