A Bayesian Confidence Neural Network (BCPNN) is an artificial neural network

Artificial neural network

An artificial neural network , usually called neural network , is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes...

 inspired by Bayes' theorem

Bayes' theorem

In probability theory and applications, Bayes' theorem relates the conditional probabilities P and P. It is commonly used in science and engineering. The theorem is named for Thomas Bayes ....

: node activations represent probability ("confidence") in the presence of input features or categories, synaptic weights are based on estimated correlations and the spread of activation corresponds to calculating posteriori probabilities. It was originally proposed by Anders Lansner and Örjan Ekeberg at KTH.

The basic network is a feedforward neural network with continuous activation. This can be extended to include spiking units and hypercolumns

Cortical column

A cortical column, also called hypercolumn or sometimes cortical module, is a group of neurons in the brain cortex which can be successively penetrated by a probe inserted perpendicularly to the cortical surface, and which have nearly identical receptive fields...

, representing mutually exclusive or interval coded features. This network has been used for classification tasks and data mining, for example for discovery of adverse drug reactions. The units can also be connected as a recurrent neural network (losing the strict interpretation of their activations as probabilities) but becoming a possible abstract model of biological neural networks and memory.