|Title||Neural decision boundaries for maximal information transmission.|
|Publication Type||Journal Article|
|Year of Publication||2007|
|Authors||Sharpee, T, Bialek, W|
|Keywords||Animals, Automatic Data Processing, Decision Making, Entropy, Humans, Learning, Models, Neurological, Nerve Net, Nervous System Physiological Phenomena, Neurons, Normal Distribution, Probability, Signal Transduction, Space Perception, Synaptic Transmission, Visual Perception|
We consider here how to separate multidimensional signals into two categories, such that the binary decision transmits the maximum possible information about those signals. Our motivation comes from the nervous system, where neurons process multidimensional signals into a binary sequence of responses (spikes). In a small noise limit, we derive a general equation for the decision boundary that locally relates its curvature to the probability distribution of inputs. We show that for Gaussian inputs the optimal boundaries are planar, but for non-Gaussian inputs the curvature is nonzero. As an example, we consider exponentially distributed inputs, which are known to approximate a variety of signals from natural environment.
|Alternate Journal||PLoS ONE|