5 papers
Spiking neurons as predictive controllers of linear systems
Paolo Agliati, André Urbano, Pablo Lanillos +3
Neurons communicate with downstream systems via sparse and incredibly brief electrical pulses, or spikes. Using these events, they control various targets such as neuromuscular uni…
Noise-based reward-modulated learning
Jesús GarcÃa Fernández, Nasir Ahmad, Marcel van Gerven
The pursuit of energy-efficient and adaptive artificial intelligence (AI) has positioned neuromorphic computing as a promising alternative to conventional computing. However, achie…
A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation
Jesús GarcÃa Fernández, Nasir Ahmad, Marcel van Gerven
Iterative optimization is central to modern artificial intelligence (AI) and provides a crucial framework for understanding adaptive systems. This review provides a unified perspec…
Correlations Are Ruining Your Gradient Descent
Nasir Ahmad
Herein the topics of (natural) gradient descent, data decorrelation, and approximate methods for backpropagation are brought into a common discussion. Natural gradient descent illu…
Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer +6
Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their…