The Fortieth Annual Conference on Neural Information Processing Systems brings together interdisciplinary machine-learning research through peer-reviewed sessions, invited talks, demonstrations, tutorials, workshops, and an exposition.
Seminar · Computational Neuroscience
Energy efficient learning in neural networks
Mark van Rossum · University of Nottingham
Wed, May 7, 2025 · 15:00 UTC
The brain is one of the most energy intense organs. Some of this energyis used for neural information processing, however, fruitfly experiments have shown that also learning is metabolically costly. We will present estimates of this cost and introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that cansave energy need for plasticity. Either by modifying the objective function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will disc
ePoster · Neuroscience
A synergistic core for human brain evolution and cognition
Andrea Luppi · Neuromatch 5
Wed, Sep 28, 2022
How does the organization of neural information processing enable humans’ sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional, structural, and diffusion-weighted MRI with meta-analytic results from NeuroSynth, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-or
ePoster · Deep Learning
Nu-Wave State Space Models: Traveling waves as a biologically plausible context
T Anderson Keller · COSYNE 2025
Mounting evidence suggests that traveling waves are a key dynamical motif in biological neural information processing systems [Muller et al., 2018], yet their precise computational role remains under investigation. While the Transformer architecture [Vaswani et al., 2023] has achieved state-of-the-art performance across various data modalities, it relies heavily on an explicit `context’ – processing the entire input sequence simultaneously. This approach is both biologically implausible and computationally inefficient, resulting in quadratic complexity relative to sequence length. Recent inter