17 citations · 26 across the 9 of their papers we have counts for
10 papers
Rewiring of adult neural networks - learning from structural plasticity across multiple scales
Michael Fauth, Han Lu, Jannik Luboeinski +36
Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their topology…
Space as Time Through Neuron Position Learning
Balázs Mészáros, James C. Knight, Danyal Akarca +1
Biological neural networks exist in physical space where distance influences communication delays: a fundamental coupling between space and time absent in most artificial neural ne…
FeNN-DMA: A RISC-V SoC for SNN acceleration
Zainab Aizaz, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks…
A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2
Balázs Mészáros, James C. Knight, Jonathan Timcheck +1
Spiking Neural Networks are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks for edge computing. Neuromorphic computi…
A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks
James C. Knight, Johanna Senk, Thomas Nowotny
The majority of research in both training Artificial Neural Networks (ANNs) and modeling learning in biological brains focuses on synaptic plasticity, where learning equates to cha…
FeNN: A RISC-V vector processor for Spiking Neural Network acceleration
Zainab Aizaz, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) have the potential to drastically reduce the energy requirements of AI systems. However, mainstream accelerators like GPUs and TPUs are designed for…
Constructive community race: full-density spiking neural network model drives neuromorphic computing
Johanna Senk, Anno C. Kurth, Steve Furber +18
The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a mo…
Eventprop training for efficient neuromorphic applications
Thomas Shoesmith, James C. Knight, Balázs Mészáros +2
Neuromorphic computing can reduce the energy requirements of neural networks and holds the promise to `repatriate' AI workloads back from the cloud to the edge. However, training n…
Efficient Event-based Delay Learning in Spiking Neural Networks
Balázs Mészáros, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Net…
Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks
Thomas Nowotny, James P. Turner, James C. Knight
Event-based machine learning promises more energy-efficient AI on future neuromorphic hardware. Here, we investigate how the recently discovered Eventprop algorithm for gradient de…