Raoul-Martin Memmesheimer

This paper links microscopic neuronal representational drift to macroscopic memory consolidation using a statistical physics model, predicting engram dynamics in the mouse brain.

The paper explains how cooperative coding in neural networks reduces synaptic requirements while balancing response speed through specific recurrent connections.

The paper presents a model of neuronal assembly generation and maintenance using STDP, showing how assemblies can form, persist, and change without additional homeostatic mechanisms.

The paper investigates a two-population model of hippocampal CA1 to generate ripple oscillations and sequence replay, finding that structured connectivity and temporally narrow excitation are key for realistic oscillations and sparse pyramidal cell spiking.

This paper proposes a memory model where neural assemblies continuously remodel over time while maintaining persistent memory through activity-dependent and homeostatic plasticity.

This paper compares weight perturbation and node perturbation learning rules on temporally extended tasks, finding that weight perturbation can perform as well or better under certain conditions.

This paper develops a neuronal network model for the moon jelly Aurelia aurita to understand how neural activity controls its energy-efficient swimming behavior.