I am pleased to share our new preprint: “Nonlinear transient amplification in recurrent neural networks with short-term plasticity” led by Yue Kris Wu in which we study possible mechanisms for transient Hebbian amplification.https://biorxiv.org/cgi/content/short/2021.06.09.447718v1 Hebbian assembliesContinue reading
Paper: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
We’re excited to see this published. A new take on spatial credit assignment in cortical circuits based on Richard Naud’s idea on burst multiplexing. A truly collaborative effort lead by Alexandre Payeur and Jordan GuerguievContinue reading
Hiring: Context-dependent information processing in biological neural networks
We are looking for a Ph.D. student to work on context-dependent information processing in biologically inspired neural networks. We will investigate the effect of stereotypical circuit motifs and neuromodulation on neural information processing and learningContinue reading
The lab at CoSyNe 2021
The lab selfie at Cosyne 2021 clearly looks different than we would have imagined a year ago.
Publication: Visualizing a joint future of neuroscience and neuromorphic engineering
Happy to share this report summarizing the key contributions, discussion outcomes, and future research directions as presented at the spiking neural networks as universal function approximators meeting SNUFA2020. Paper: https://www.cell.com/neuron/fulltext/S0896-6273(21)00009-XAuthor link: https://authors.elsevier.com/a/1cbg3_KOmxI%7E6f
Paper: Brain-Inspired Learning on Neuromorphic Substrates
I’m happy to share our new overview paper (https://ieeexplore.ieee.org/document/9317744, preprint: arxiv.org/abs/2010.11931) on brain-inspired learning on neuromorphic substrates in (spiking) recurrent neural networks. We systematically analyze how the combination of Real-Time-Recurrent Learning (RTRL; Williams and Zipser,Continue reading
Kris and Claire present exciting excitation-inhibition work at the Bernstein conference
Both Claire and Kris will present virtual posters about their modeling work in the olfactory system. If you are interested in learning more about what precise EI balance and transient attractor states have to doContinue reading
Hiring: Information processing in spiking neural networks
We are looking for Ph.D. students to work on the computational principles of information processing in spiking neural networks. The project strives to understand computation in the sparse spiking and sparse connectivity regime, in whichContinue reading
Online workshop: Spiking neural networks as universal function approximators
Dan Goodman and myself are organizing an online workshop on new approaches to training spiking neural networks, Aug 31st / Sep 1st 2020. Invited speakers: Sander Bohte (CWI), Iulia M. Comsa (Google), Franz Scherr (TUG), Emre Neftci (UC Irvine),Continue reading
Paper: Finding sparse trainable neural networks through Neural Tangent Transfer
New paper led by Tianlin Liu on “Finding sparse trainable neural networks through Neural Tangent Transfer” https://arxiv.org/abs/2006.08228 (and code) which was accepted at ICML. In the paper we leverage the neural tangent kernel to instantiate sparse neuralContinue reading