VSR Seminar with two talks

23rd October 2023 11:30 AM
23rd October 2023 12:30 PM
Jülich Supercomputing Centre, Lecture Hall, building 16.3, room 222

1st talk: Correlated electrons and functional renormalisation group on HPC Systems at JSC - Local efforts and community perspectives of the projects jjsc45 and enhanceRG

Speaker: Dr. Daniel Rohe, Forschungszentrum Jülich, Jülich Supercomputing Centre (JSC)

A core objective of SDLs at JSC consists in exploring and harvesting the potential of highly parallel HPC architectures for applications in specific scientific fields and branches. Based on two compute time projects, we illustrate how this task was approached in the context of functional renormalisation group calculations in condensed matter physics, and what status quo has been reached as of today.

2nd talk: Dendritic modulation of feedforward processing for dynamic task switching in artificial and spiking neural networks

Speaker: Dr. Willem Wybo, Forschungszentrum Jülich, Computational and Systems Neuroscience (INM-6) and Theoretical Neuroscience (IAS-6)

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

Last Modified: 28.09.2023