Timezone: »
It is becoming increasingly evident that organisms acting in uncertain dynamical environments often employ exact or approximate Bayesian statistical calculations in order to continuously estimate the environmental state, integrate information from multiple sensory modalities, form predictions and choose actions. What is less clear is how these putative computations are implemented by cortical neural networks. An additional level of complexity is introduced because these networks observe the world through spike trains received from primary sensory afferents, rather than directly. A recent line of research has described mechanisms by which such computations can be implemented using a network of neurons whose activity directly represents a probability distribution across the possible ``world states''. Much of this work, however, uses various approximations, which severely restrict the domain of applicability of these implementations. Here we make use of rigorous mathematical results from the theory of continuous time point process filtering, and show how optimal realtime state estimation and prediction may be implemented in a general setting using linear neural networks. We demonstrate the applicability of the approach with several examples, and relate the required network properties to the statistical nature of the environment, thereby quantifying the compatibility of a given network with its environment.
Author Information
Omer Bobrowski (Technion, Israel Institue of Technology)
Ron Meir (Technion)
Shy Shoham
Yonina Eldar (Israel Institute of Technology)
Related Events (a corresponding poster, oral, or spotlight)

2007 Poster: A neural network implementing optimal state estimation based on dynamic spike train decoding »
Wed Dec 5th 06:30  06:40 PM Room None
More from the Same Authors

2017 Poster: Convolutional Phase Retrieval »
Qing Qu · Yuqian Zhang · Yonina Eldar · John Wright 
2015 Poster: A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding »
Yuval Harel · Ron Meir · Manfred Opper 
2015 Spotlight: A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding »
Yuval Harel · Ron Meir · Manfred Opper 
2014 Poster: Optimal Neural Codes for Control and Estimation »
Alex K Susemihl · Ron Meir · Manfred Opper 
2014 Poster: Expectation Backpropagation: ParameterFree Training of Multilayer Neural Networks with Continuous or Discrete Weights »
Daniel Soudry · Itay Hubara · Ron Meir 
2011 Poster: Analytical Results for the Error in Filtering of Gaussian Processes »
Alex K Susemihl · Ron Meir · Manfred Opper 
2008 Poster: Temporal Difference Based Actor Critic Learning  Convergence and Neural Implementation »
Dotan Di Castro · Dima Volkinshtein · Ron Meir