Bayesian Spiking Neurons I: Inference

@article{citeulike:2811503, abstract = {We show that the dynamics of spiking neurons can be interpreted as a form of Bayesian inference in time. Neurons that optimally integrate evidence about events in the external world exhibit properties similar to leaky integrate-and-fire neurons with spike-dependent adaptation and maximally respond to fluctuations of their input. Spikes signal the occurrence of new information--what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic representation of probabilities.}, author = {Deneve, Sophie }, citeulike-article-id = {2811503}, journal = {Neural Comp.}, keywords = {bayesian, spikes}, month = {January}, number = {1}, pages = {91--117}, posted-at = {2008-05-19 04:11:27}, priority = {2}, title = {Bayesian Spiking Neurons I: Inference}, url = {http://neco.mitpress.org/cgi/content/abstract/20/1/91}, volume = {20}, year = {2007} }

TY - JOUR ID - citeulike:2811503 L3 - citeulike-article-id:2811503 TI - Bayesian Spiking Neurons I: Inference JF - Neural Comp. VL - 20 IS - 1 SP - 91 EP - 117 N2 - We show that the dynamics of spiking neurons can be interpreted as a form of Bayesian inference in time. Neurons that optimally integrate evidence about events in the external world exhibit properties similar to leaky integrate-and-fire neurons with spike-dependent adaptation and maximally respond to fluctuations of their input. Spikes signal the occurrence of new information--what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic representation of probabilities. KW - bayesian KW - spikes AU - Deneve, Sophie PY - 2007/01/01/ UR - http://neco.mitpress.org/cgi/content/abstract/20/1/91 ER -