research
∙
06/09/2023
Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
Biological evidence suggests that adaptation of synaptic delays on short...
share
research
∙
05/24/2023
Block-local learning with probabilistic latent representations
The ubiquitous backpropagation algorithm requires sequential updates acr...
research
∙
03/10/2023
Efficient Real Time Recurrent Learning through combined activity and parameter sparsity
Backpropagation through time (BPTT) is the standard algorithm for traini...
research
∙
06/13/2022
EGRU: Event-based GRU for activity-sparse inference and learning
The scalability of recurrent neural networks (RNNs) is hindered by the s...
research
∙
02/28/2022
Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
Neuroscience models commonly have a high number of degrees of freedom an...
research
∙
03/03/2020
Embodied Synaptic Plasticity with Online Reinforcement learning
The endeavor to understand the brain involves multiple collaborating res...
research
∙
09/16/2019
Reservoirs learn to learn
We consider reservoirs in the form of liquid state machines, i.e., recur...
research
∙
03/26/2018
Long short-term memory and Learning-to-learn in networks of spiking neurons
Networks of spiking neurons (SNNs) are frequently studied as models for ...
research
∙
03/17/2017
