Topic: neuromorphic systems

Florian Marquardt surveys approaches to reducing the energy and computational cost of neural networks by implementing their functions in physical devices. The central question is how experiments can efficiently determine updates to adjustable parameters, allowing the hardware itself to learn. Examples include learning through time reversal, obtaining nonlinear computation from linear optical scattering, and training analog quantum simulators as neuromorphic systems. The seminar is available in person and through the registration link on the organizer’s event page.

Current SNNs studies frequently ignore dendrites, the thin membranous extensions of biological neurons that receive and preprocess nearly all synaptic inputs in the brain. However, decades of experimental and theoretical research suggest that dendrites possess compelling computational capabilities that greatly influence neuronal and circuit functions. Notably, standard point-neuron networks cannot adequately capture most hallmark dendritic properties. Meanwhile, biophysically detailed neuron models are impractical for large-network simulations due to their complexity, and high computational co

It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of modularity. We used artificial neural networks to test the hypothesis that structural modularity is sufficient to guarantee functional specialization, and find that in general, this doesn't necessarily hold. We then systematically tested which features of the environment and network do lead to the emergence of specialization. We used a simple toy environment, task and network, allowing us precise control, and show that

Neural replay, a phenomenon where activation sequences experienced during wakefulness are replayed during rest or sleep, is thought to play a crucial role in memory consolidation and planning [1]. It is commonly modeled as a chain of activity, where links are formed based on temporal associations in the stimulus. Existing work [2,3] focuses on simple patterns, yet we know that the brain is capable of learning complex spatiotemporal dynamics. Specifically chaotic dynamics are even challenging for conventional deep learning algorithms due to exploding gradients. We demonstrate that by introducin