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Found 14 papers, 9 papers with code
Spiking Music: Audio Compression with Event Based Auto-encoders
Neurons in the brain communicate information via punctual events called spikes.
High-performance deep spiking neural networks with 0.3 spikes per neuron
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains.
Trial matching: capturing variability with data-constrained spiking neural networks
A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials.
Context selectivity with dynamic availability enables lifelong continual learning
"You never forget how to ride a bike", -- but how is that possible?
An Exact Mapping From ReLU Networks to Spiking Neural Networks
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence.
Mesoscopic modeling of hidden spiking neurons
Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved?
Fitting summary statistics of neural data with a differentiable spiking network simulator
Fitting network models to neural activity is an important tool in neuroscience.
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown.
Eligibility traces provide a data-inspired alternative to backpropagation through time
Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns.
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known biological mechanisms.
Long short-term memory and learning-to-learn in networks of spiking neurons
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain.
Ranked #22 on
Speech Recognition
on TIMIT
Deep Rewiring: Training very sparse deep networks
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate.
