☆ 4.7 Article

Low-variance Forward Gradients using Direct Feedback Alignment and momentum

NEURAL NETWORKS (2024)

Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation

Tielin Zhang et al.

Summary: Spiking neural networks (SNNs) are considered the third generation of artificial neural networks (ANNs) and have more biologically realistic structures. To address the dynamic characteristics of SNNs, a biologically plausible reward propagation (BRP) algorithm is proposed for training SNNs, which has shown comparable accuracy to state-of-the-art BP-based SNNs while saving computational cost. This approach provides insights toward a better understanding of the intelligent nature of biological systems.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Neurosciences

Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks

Charlotte Frenkel et al.

Summary: The direct random target projection (DRTP) algorithm proposed in this work views one-hot-encoded labels in supervised classification problems as a proxy for error signs, enabling layerwise feedforward training of hidden layers to solve weight transport and update locking issues while reducing computational and memory requirements.

FRONTIERS IN NEUROSCIENCE (2021)

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Review Computer Science, Artificial Intelligence