Joy Bose’s Weblog

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Computational neuroscience

Posted by joyboseroy on November 8, 2007

The idea of Comp-neuro is that the brain is a deterministic system, and computer modellign can help us understand how it works. It benefits both ways: computer scientists and engineers can get insights on how to produce better fastre and error tolerant systems (made out of unreliable components) from the brain, and neuroscientists can get validation for their theories, because without theories neuroscience is just a mass of data without any understanding of systems level functioning.

The hippocampus (see Rolls and Treves’ book) isone of the best studied system, its implicated in short term memory (CA3/CA1 areas) before it gets converted to long term memory in the neocortex and higher brain regions. People and animals with hippocampal lesions (where this part of brain is damaged)  have retrogade amnesia (difficult to form new memories but old memories, and procedural memories remain). Many have postulated this area as being responsible for spatial maps (esp in rat hippocampus), LTP/LTD plasticity, episodic memory, etc.

A typical comp-neuro experiment is illustrated beautifully in hippocampus chapter of Rolls and treves. First, there needs to be a falsifiable hypothesis. Rolls studied current theories and found their defects, did some mathematical calculations of the number of connections between different layers, the nature of the connections (recurrent etc), how they could be modelled and the expected memory capacity and the constraints on their connectivity, a falsifiable prediction that turned out to be wide off the mark, because of many assumptions. Instead Rolls proposed a simple model based on multiple layers of associative or recurrent neurons, each of whose capacities could be modelled. Then he tested the theory by performing simulation: giving a random pattern to a layer and expecting it to be able to learn and recall by hebbian learning, keeping the connections ratio roughly same as biology data.

Theres lot of speculation involved in neuroscience (this is one way it could happen etc) , and based on that scientists make falsoifiable predictions which are then verified. Treves/Rolls’ book starts with some standard types of neural nets including pattern associative memories, autoassociative memories, competitive nets and recurrent nets trained with error backprop, then starts on different brain regions and how they canbe modelled using these 4-5 basic types of memories.


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