Angelika Steger

The paper investigates how presynaptic stochasticity and plasticity improve energy efficiency and alleviate the stability-plasticity dilemma in neural networks.

This paper introduces a learning algorithm that achieves high sample efficiency using bio-plausible mechanisms, showing that adaptive tuning curve widths improve both sample efficiency and final precision.

This paper proposes a hippocampal model that stores and bidirectionally replays spatio-temporal memory sequences, using mechanisms like synfire chains, global inhibition, and cholinergic modulation.