Blaise Agüera y Arcas, Adrienne Fairhall, William Bialek
In this paper we formulate a description of the computation per(cid:173) formed by a neuron as a combination of dimensional reduction and nonlinearity. We implement this description for the Hodgkin(cid:173) Huxley model, identify the most relevant dimensions and find the nonlinearity. A two dimensional description already captures a significant fraction of the information that spikes carry about dy(cid:173) namic inputs. This description also shows that computation in the Hodgkin-Huxley model is more complex than a simple integrate(cid:173) and-fire or perceptron model.