Brain-inspired spiking neural networks bring real-time AI to edge devices, boosting performance, reducing power use, and ...

“As far as I know, no one else has ever been able to ablate [knock out] specific synapses without affecting the whole neuron,” he said. The ability to selectively dismantle parts of a neural network — ...

In hippocampal slices, synchronous CA3 network activity induced persistent strengthening of active positive-feedback synapses ... the Hebb memory model at the neural-network level and suggest ...

and reveals a novel neural-glia-fibroblast-lymphatic regulatory axis. This provides a new framework for understanding how the brain adapts its lymphatic network based on functional needs ...

How do we think, feel, remember, or move? It all depends on transmission of chemical signals in the brain, carried and released by molecular containers called vesicles. In a new study, researchers ...

But neural networks only predict based on patterns from the past—what happens when the weather does something that's unprecedented ... Swarming is one of the principal forms of bacterial ...

07 Aug 2014 -- Scientists from IBM (NYSE: IBM) today unveiled the first neurosynaptic computer chip to achieve an unprecedented scale of one million programmable neurons, 256 million programmable ...

Abstract: Recently, stealing highly-valuable and large-scale deep neural network (DNN) models becomes pervasive. The stolen models may be re-commercialized, e.g., deployed in embedded devices, ...

As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications ...

Convolutional Neural Networks (ConvNets or CNNs) are a class of neural networks algorithms that are mostly used in visual recognition tasks such as image classification, object detection, and image ...