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Found 18 papers, 11 papers with code
Adversarially Robust Spiking Neural Networks Through Conversion
Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications.
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications.
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity
Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years.
Ranked #7 on
Question Answering
on bAbi
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching
Experimental benchmark evaluations show that output code matching is superior to existing regularized weight quantization based defenses, and an effective defense against stealthy weight bit-flip attacks.
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations.
Ranked #4 on
Question Answering
on bAbi
Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately.
Embodied Synaptic Plasticity with Online Reinforcement learning
We demonstrate this framework to evaluate Synaptic Plasticity with Online REinforcement learning (SPORE), a reward-learning rule based on synaptic sampling, on two visuomotor tasks: reaching and lane following.
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.
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources.
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.
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning
These data are inconsistent with common models for network plasticity, and raise the questions how neural circuits can maintain a stable computational function in spite of these continuously ongoing processes, and what functional uses these ongoing processes might have.
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.
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Our theoretical analysis shows that stochastic search could in principle even attain optimal network configurations by emulating one of the most well-known nonlinear optimization methods, simulated annealing.
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
We reexamine in this article the conceptual and mathematical framework for understanding the organization of plasticity in spiking neural networks.
Network Plasticity as Bayesian Inference
General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference.
