Studying the interaction of a hidden Markov model with a Bayesian spiking neural network

This paper explores a novel hybrid approach for classifying sequential data such as isolated spoken words. The approach combines a hidden Markov model (HMM) with a spiking neural network (SNN). The HMM, consisting of states and transitions, forms a fixed backbone with nonadaptive transition probabilities. The SNN, however, implements a Bayesian computation by using an appropriately selected spike timing dependency (STDP) learning rule. A separate SNN, each with the same architecture, is associated with each of the p states of the HMM. Because of the STDP tuning, each SNN implements an expectation maximization (EM) algorithm to learn the particular observation probabilities for one particular HMM state. When applied to an isolated spoken word (as a popular sequential data) recognition problem, the hybrid model performs well and efficiently with a desirable accuracy rate. Because of the model's uniqueness and initial success, it warrants further study. Future work intends to broaden its capabilities and improve the biological realism.