Training a Hidden Markov Model with a Bayesian Spiking Neural Network

TL;DR

This paper presents a hybrid model combining hidden Markov models with spiking neural networks to classify sequential data, integrating statistical and biological mechanisms, and demonstrates promising initial results on speech data.

Contribution

It introduces a novel hybrid approach that uses SNNs with STDP to learn emission probabilities in an HMM framework, bridging statistical models and neural computation.

Findings

Model performs favorably on speech data

SNNs implement expectation maximization for emission learning

Preliminary results show potential of the hybrid approach

Abstract

It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential data, such as individual spoken works. 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 biologically based Bayesian computation that derives from the spike timing-dependent plasticity (STDP) learning rule. The emission (observation) probabilities of the HMM are represented in the SNN and trained with the STDP rule. A separate SNN, each with the same architecture, is associated with each of the states of the HMM. Because of the STDP training, each SNN implements an expectation maximization algorithm to learn the emission probabilities for one HMM state. The model was studied on synthesized spike-train data and also on spoken word data. Preliminary results suggest its performance compares favorably with other biologically motivated approaches. Because of the model's uniqueness and initial promise, it warrants further study. It provides some new ideas on how the brain might implement the equivalent of an HMM in a neural circuit.