INRC Forum: Robert Legenstein

Memory-enriched computation and learning through synaptic and non-synaptic plasticity

Abstract:Virtually any task faced by humans has a temporal component and therefore demands some form of memory. Consequently, a variety of memory systems and mechanisms have been shown to exist in the brain of humans and other animals. These memory systems operate on a multitude of time scales, from seconds to years. Yet, it is still not well understood how memory is implemented in the brain and how cortical neuronal networks utilize these systems for computation. In this talk, I will present some recent models that extend (spiking and non-spiking) neural network models with memory using Hebbian and non-Hebbian types of plasticity. I will discuss the similarities between these models and transformers, arguably the most powerful models for sequence processing in the area of machine learning. I will show that Hebbian plasticity can significantly increase the computational and learning capabilities of spiking neural networks. Further, I will show how neurons with non-synaptic plasticity can be utilized for memory and how networks of such neurons can be trained without the need to backpropagate errors through time.

Bio: Dr. Robert Legenstein received his PhD in computer science from the Graz University of Technology, Graz, Austria, in 2002. He is a full professor at the Department of Computer Science, TU Graz, head of the Institute for Theoretical Computer Science, and leading the Graz Center for Machine Learning. Dr. Legenstein has served as associate editor of IEEE Transactions on Neural Networks and Learning Systems (2012-2016). He is an action editor for Transactions on Machine Learning Research, and he was on the program committee for NeurIPS and ICLR several times. His primary research interests are learning in models for biological networks of neurons and neuromorphic hardware, probabilistic neural computation, novel brain-inspired architectures for computation and learning, and memristor-based computing concepts.

For the meeting link, see the full INRC Forum Spring 2023 Schedule (accessible only to INRC Affiliates and Fully Engaged Members).