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Towards a theory of layered neural circuit architectures

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If you have a question about this talk, please contact Daniel McNamee.

A main challenge in neuroscience is finding a general computational principle that explains why cortical circuits are organized in particular structures. I will start out with the optimal storage principle as a guideline to derive optimal neural architecture. For optimal storage, one needs to have the maximal capacity of a neural network and a learning rule to achieve the capacity. For conventional recurrent neural networks, the maximal capacity is known as the Gardner bound, and this bound is achieved via the Three-Threshold Learning Rule (3TLR). However, calculating the storage capacity of hierarchical neural circuits has been problematic. I will present my recent results suggesting that the capacity of an expansive autoencoder increases superlinearly with the expansion ratio using simulations and Gardner’s replica theory. I will discuss some of the theoretical challenges and limitations of these networks.

This talk is part of the Computational Neuroscience series.

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