activity
20172026
most citedSurrogate Gradient Learning in Spiking Neural Networks

149 citations · 167 across the 11 of their papers we have counts for

collaborators
Showing q-bio.NCShow all

5 papers · 1 filter

q-bio.NC2025

Teaching signal synchronization in deep neural networks with prospective neurons

Nicolas Zucchet, Qianqian Feng, Axel Laborieux +3

Working memory requires the brain to maintain information from the recent past to guide ongoing behavior. Neurons can contribute to this capacity by slowly integrating their inputs…

q-bio.NC2024★ 1 cited

Decoding finger velocity from cortical spike trains with recurrent spiking neural networks

Tengjun Liu, Julia Gygax, Julian Rossbroich +3

Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection…

q-bio.NC2024

Theories of synaptic memory consolidation and intelligent plasticity for continual learning

Friedemann Zenke, Axel Laborieux

Humans and animals learn throughout life. Such continual learning is crucial for intelligence. In this chapter, we examine the pivotal role plasticity mechanisms with complex inter…

q-bio.NC2023★ 4 cited

Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity

Julian Rossbroich, Friedemann Zenke

How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience. Various studies have suggested plausible solutions for back-propagating…

q-bio.NC2017

SuperSpike: Supervised learning in multi-layer spiking neural networks

Friedemann Zenke, Surya Ganguli

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spik…