IBM and ETH Zurich researchers make progress in reconciling neurophysiological insights with machine intelligence, proposing a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates synaptic integration principles from biology. GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals) leads to improvements in the training time convergence, accuracy and scalability of ANNs and SNNs.

Here is a quick read: Toward a New Generation of Neuromorphic Computing: IBM & ETH Zurich’s Biologically Inspired Optimizer Boosts FCNN and SNN Training.

The paper Learning in Deep Neural Networks Using a Biologically Inspired Optimizer is on arXiv.



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