Novelty Detection, Insect Olfaction, Mismatch Negativity, and the Representation of Probability in the Brain
We present a unified model of how groups of neurons can represent and learn probability distributions using a biologically plausible online learning rule. We first present this in the context of insect olfaction, where we map our model onto a well-known biological circuit where a single output neuron represents whether the current stimulus is novel or not. We show that the model approximates a Bayesian inference process, providing an explanation as to why the current flowing into the output neuron is proportional to the expected probability of that stimulus. Finally, we extend this model to show that the same circuit can detect temporal patterns such as those violations of expectations that produce the EEG mismatch negativity signal.