297 citations · 701 across the 6 of their papers we have counts for
8 papers
Meta-learning Spiking Neural Networks with Surrogate Gradient Descent
Kenneth Stewart, Emre Neftci
Adaptive "life-long" learning at the edge and during online task performance is an aspirational goal of AI research. Neuromorphic hardware implementing Spiking Neural Networks (SNN…
Brain-Inspired Learning on Neuromorphic Substrates
Friedemann Zenke, Emre O. Neftci
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solv…
Embodied Neuromorphic Vision with Event-Driven Random Backpropagation
Jacques Kaiser, Alexander Friedrich, J. Camilo Vasquez Tieck +4
Spike-based communication between biological neurons is sparse and unreliable. This enables the brain to process visual information from the eyes efficiently. Taking inspiration fr…
Surrogate Gradient Learning in Spiking Neural Networks
Emre O. Neftci, Hesham Mostafa, Friedemann Zenke
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neu…
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Jacques Kaiser, Hesham Mostafa, Emre Neftci
A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, discus…
Contrastive Hebbian Learning with Random Feedback Weights
Georgios Detorakis, Travis Bartley, Emre Neftci
Neural networks are commonly trained to make predictions through learning algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by gradient backpropagation, i…
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Georgios Detorakis, Sadique Sheik, Charles Augustine +6
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large…
Neuromorphic Deep Learning Machines
Emre Neftci, Charles Augustine, Somnath Paul +1
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and tempora…