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Found 15 papers, 11 papers with code
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks
We find that the latter provides the missing theoretical basis for surrogate gradients in stochastic spiking neural networks.
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience.
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis
Equilibrium propagation (EP) is a compelling alternative to the backpropagation of error algorithm (BP) for computing gradients of neural networks on biological or analog neuromorphic substrates.
Implicit variance regularization in non-contrastive SSL
To gain further theoretical insight into non-contrastive SSL, we analytically study learning dynamics in conjunction with Euclidean and cosine similarity in the eigenspace of closed-form linear predictor networks.
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations
Equilibrium propagation (EP) is an alternative to backpropagation (BP) that allows the training of deep neural networks with local learning rules.
Fluctuation-driven initialization for spiking neural network training
Thus fluctuation-driven initialization provides a practical, versatile, and easy-to-implement strategy for improving SNN training performance on diverse tasks in neuromorphic engineering and computational neuroscience.
Ranked #8 on
Audio Classification
on SHD
Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities.
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce.
Brain-Inspired Learning on Neuromorphic Substrates
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams.
Finding trainable sparse networks through Neural Tangent Transfer
Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial.
Surrogate gradients for analog neuromorphic computing
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time.
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain.
Ranked #4 on
Audio Classification
on SHD
Surrogate Gradient Learning in Spiking Neural Networks
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing.
SuperSpike: Supervised learning in multi-layer spiking neural networks
In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike-time patterns.
Continual Learning Through Synaptic Intelligence
While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning.
