Single neuron distribution modelling for anomaly detection and evidence integration
Probability theory is often used to model animal behaviour, but the gap between high-level models and how those are realized in neural implementations often remains. In this paper we show how biologically plausible cognitive representations of continuous data, called Spatial Semantic Pointers, can be used to construct single neuron estimators of probability distributions. These representations form the basis for neural circuits that perform anomaly detection and evidence integration for decision making. We tested these circuits on simple anomaly detection and decision-making tasks. In the anomaly detection task, the circuit was asked to determine whether observed data was anomalous under a distribution implied by training data. In the decision-making task, the agent had to determine which of two distributions were most likely to be generating the observed data. In both cases we found that the neural implementations performed comparably to a non-neural Kernel Density Estimator baseline. This work distinguishes itself from prior approaches to neural probability by using neural representations of continuous states, e.g., grid cells or head direction cells. The circuits in this work provide a basis for further experimentation and for generating hypotheses about behaviour as greater biological fidelity is achieved.