One of the key computations performed in human brain is multi-sensory cue integration, through which humans are able to estimate the current state of the world to discover relative reliabilities and relations between observed cues. Mammalian cortex consists of highly distributed and interconnected populations of neurons, each providing a specific type of information about the state of the world. Connections between areas seemingly realize functional relationships amongst them and computation occurs by each area trying to be consistent with the areas it is connected to. In this paper using line-attraction dynamics and divisive normalization, we present a computational framework which is able to learn arbitrary non-linear relations between multiple cues using a simple Hebbian Learning principle. After learning, the network dynamics converges to the stable state so to satisfy the relation between connected populations. This network can perform several principle computational tasks such as inference, de-noising and cue-integration. By applying a real world multi-sensory integrating scenario, we demonstrate that the network can encode relative reliabilities of cues in different areas of the state space, over distributed population vectors. This reliability based encoding biases the network’s dynamics in favor of more reliable cues and realizes a near optimal sensory integration mechanism. Additional important features of the network are its scalability to cases with higher order of modalities and its flexibility to learn smooth functions of relations which is necessary for a system to operate in a dynamic environment.
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- Flexible Cue Integration by Line Attraction Dynamics and Divisive Normalization
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