2 citations · 2 across the 4 of their papers we have counts for
5 papers
A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation
Jesús García 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…
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 García 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…
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…
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…