Dan Butts, Garrett Stanley, 9/15/03
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A Quick Information Calculation Based On Linear Estimates of Visual
Neurons
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Information theory provides assumption-free measures of the encoding
properties of neurons, but as a result of remaining assumption free,
these measures often require a prohibitive amount of data to properly
estimate.  We derive a model-based information calculation (MBIC) that
uses a neuron's linear kernel and its non-linear mapping to a firing
rate, allowing the instantaneous information rate of a neuron to be
calculated using a fraction of the data required by existing direct
methods.  We find that a strict application of this quasi-linear model
to real neurons does not fully capture the information encoded by these
neurons, but that an adjustment of the model's non-linear mapping that
takes into account the neuronal refractory period can correct this and
properly estimate the information. We apply these methods to models of
visual neurons, where we calculate information rates for spatiotemporal
input. Thus, this technique allows information calculations in a
variety of systems where such characterizations are currently
impractical, and furthermore explicitly relates easily measurable
properties of a neuron's encoding to its ability to transmit
information.