Learning Sequence Attractors in Hopfield Networks with Hidden Neurons

Yao Lu, Si Wu

Keywords: neural networks, attractor networks, threshold networks, sequence attractors, sequence memory, episodic memory

Abstract: The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how networks of Hopfield type learn sequence attractors to store predefined pattern sequences and retrieve them robustly. We show that to store arbitrary pattern sequences, it is necessary for the network to include hidden neurons even though their role in displaying sequence memories is indirect. We develop a local learning algorithm to learn sequence attractors in the networks with hidden neurons. The algorithm is proven to converge and lead to sequence attractors. We demonstrate that our model can store and retrieve sequences robustly on synthetic and real-world datasets. We hope that this study provides new insights in understanding sequence memory and temporal information processing in the brain.

Submission Number: 5