Neural Implementation of Bayesian Inference in Population Codes

Such a coding strategy is called population coding. It is conceivable that population coding has advantage of being robust to the fluctuation in a single neuron's activity. However, people argue that population coding may have other computationally desirable properties. One such property is to provide a framework for encoding complex objects by using basis functions [1]. This is inspired by the recent progresses in nonlinear function approximation, such as, sparse coding, overcomplete representationand kernel regression. These methods are efficient and show some interesting neuron-like behaviors [2,3].