  This paper presents a novel approach to address the challenge of online
temporal memory learning for decision-making under uncertainty in
non-stationary, partially observable environments. The proposed algorithm,
Distributed Hebbian Temporal Memory (DHTM), is based on factor graph formalism
and a multicomponent neuron model. DHTM aims to capture sequential data
relationships and make cumulative predictions about future observations,
forming Successor Features (SF). Inspired by neurophysiological models of the
neocortex, the algorithm utilizes distributed representations, sparse
transition matrices, and local Hebbian-like learning rules to overcome the
instability and slow learning process of traditional temporal memory algorithms
like RNN and HMM. Experimental results demonstrate that DHTM outperforms LSTM
and a biologically inspired HMM-like algorithm, CSCG, in the case of
non-stationary datasets. Our findings suggest that DHTM is a promising approach
for addressing the challenges of online sequence learning and planning in
dynamic environments.
