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06/14/2023
Are training trajectories of deep single-spike and deep ReLU network equivalent?
Communication by binary and sparse spikes is a key factor for the energy...
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06/02/2023
GateON: an unsupervised method for large scale continual learning
The objective of continual learning (CL) is to learn tasks sequentially ...
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12/23/2022
An Exact Mapping From ReLU Networks to Spiking Neural Networks
Deep spiking neural networks (SNNs) offer the promise of low-power artif...
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05/26/2022
Mesoscopic modeling of hidden spiking neurons
Can we use spiking neural networks (SNN) as generative models of multi-n...
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06/18/2021
Fitting summary statistics of neural data with a differentiable spiking network simulator
Fitting network models to neural activity is becoming an important tool ...
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10/16/2020
Towards truly local gradients with CLAPP: Contrastive, Local And Predictive Plasticity
Back-propagation (BP) is costly to implement in hardware and implausible...
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01/25/2019
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
The way how recurrently connected networks of spiking neurons in the bra...
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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 ...
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11/14/2017
Deep Rewiring: Training very sparse deep networks
Neuromorphic hardware tends to pose limits on the connectivity of deep n...
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03/17/2017
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artific...
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03/06/2017