11 citations · 19 across the 8 of their papers we have counts for
8 papers
Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines
Jesus Garcia Fernandez, Nasir Ahmad, Marcel van Gerven
Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient…
Efficient Deep Learning with Decorrelated Backpropagation
Sander Dalm, Joshua Offergeld, Nasir Ahmad +1
The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a signific…
Node Perturbation Can Effectively Train Multi-Layer Neural Networks
Sander Dalm, Marcel van Gerven, Nasir Ahmad
Backpropagation (BP) remains the dominant and most successful method for training parameters of deep neural network models. However, BP relies on two computationally distinct phase…
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Burcu Küçükoğlu, Walraaf Borkent, Bodo Rueckauer +3
Advances in reinforcement learning (RL) often rely on massive compute resources and remain notoriously sample inefficient. In contrast, the human brain is able to efficiently learn…
Constrained Parameter Inference as a Principle for Learning
Nasir Ahmad, Ellen Schrader, Marcel van Gerven
Learning in neural networks is often framed as a problem in which targeted error signals are directly propagated to parameters and used to produce updates that induce more optimal…
Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks
Sander Dalm, Nasir Ahmad, Luca Ambrogioni +1
Many of the recent advances in the field of artificial intelligence have been fueled by the highly successful backpropagation of error (BP) algorithm, which efficiently solves the…
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni
Traditional backpropagation of error, though a highly successful algorithm for learning in artificial neural network models, includes features which are biologically implausible fo…
Overcoming the Weight Transport Problem via Spike-Timing-Dependent Weight Inference
Nasir Ahmad, Luca Ambrogioni, Marcel A. J. van Gerven
We propose a solution to the weight transport problem, which questions the biological plausibility of the backpropagation algorithm. We derive our method based upon a theoretical a…