Towards a general theory of neural computation based on prediction by single neurons.

ABSTRACT: Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior informat

SUBMITTER: Fiorillo CD 

PROVIDER: S-EPMC2553191 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature