A neural network implementing optimal state estimation based on dynamic spike train decoding

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 fr...

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar
• Keywords: Cortical Neural Networks, Information Technology, Multiple Sensory Modalities, Neural Networks, NIPS 2007