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Poster
On Neuronal Capacity
Pierre Baldi · Roman Vershynin

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #142
in Tue Poster Session A »

We define the capacity of a learning machine to be the logarithm of the number (or volume) of the functions it can implement. We review known results, and derive new results, estimating the capacity of several neuronal models: linear and polynomial threshold gates, linear and polynomial threshold gates with constrained weights (binary weights, positive weights), and ReLU neurons. We also derive capacity estimates and bounds for fully recurrent networks and layered feedforward networks.

Keywords: Information Theory · Learning Theory · Theory · Neural Coding · Hardware and Systems · Computational Complexity
[ Paper] 

Author Information

Pierre Baldi (UC Irvine)
Roman Vershynin (UCI)

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