HTC predicts PNN classification loss via a power law, with experimental and simulated data from distinct physical systems collapsing onto task-specific curves.
and Pandit, Tej and Merkel, Cory and Kubendran, Rajkumar and Aimone, James B
2 Pith papers cite this work, alongside 257 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
A novel MPI-based construction method for spiking neural networks on multi-GPU clusters is introduced, with scaling demonstrated on two cortical models using point-to-point and collective communication.
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Power law scaling for classification accuracy in physical neural networks
HTC predicts PNN classification loss via a power law, with experimental and simulated data from distinct physical systems collapsing onto task-specific curves.
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Scalable Construction of Spiking Neural Networks using up to thousands of GPUs
A novel MPI-based construction method for spiking neural networks on multi-GPU clusters is introduced, with scaling demonstrated on two cortical models using point-to-point and collective communication.