Title :
Bayesian head state prediction: Computing the dynamic prior with spiking neurons
Author :
Paulin, M.G. ; Hoffman, L.F.
Author_Institution :
Dept. of Zoology, Univ. of Otago, Dunedin, New Zealand
Abstract :
In a model of the vestibular system the Bayesian posterior density of head state is represented by the spatial density of spikes in brainstem vestibular neurons. Individual sensory spikes are measurements of head state. We show how to compute the Bayesian dynamic prior density for each sensory spike, from the posterior density based on previous spikes, using natural neuron-like operations on spikes. Head movement dynamics together with Bayes rule determine the architecture of the required neural network. This is a natural model of neural computation using spikes as operands.
Keywords :
Bayes methods; brain; neural nets; neurophysiology; Bayesian head state prediction; Bayesian posterior density; brainstem vestibular neurons; head movement dynamics; natural neuron-like operations; neural network; spiking neurons; vestibular system; Adaptation models; Bayesian methods; Brain modeling; Computational modeling; Kalman filters; Lattices; Neurons; Bayesian inference; Kalman filter; adaptive filter; cerebellum; neural code; neural network; particle filter;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022088
