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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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09/02/2022
A taxonomy of surprise definitions
Surprising events trigger measurable brain activity and influence human ...
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08/19/2022
Kernel Memory Networks: A Unifying Framework for Memory Modeling
We consider the problem of training a neural network to store a set of p...
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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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05/25/2021
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
We study how permutation symmetries in overparameterized multi-layer neu...
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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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10/23/2019
Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks
Reservoir computing is a powerful tool to explain how the brain learns t...
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07/05/2019
An Approximate Bayesian Approach to Surprise-Based Learning
Surprise-based learning allows agents to adapt quickly in non-stationary...
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07/05/2019
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
The permutation symmetry of neurons in each layer of a deep neural netwo...
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02/27/2019
Biologically plausible deep learning -- but how far can we go with shallow networks?
Training deep neural networks with the error backpropagation algorithm i...
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12/17/2018
Learning to Generate Music with BachProp
As deep learning advances, algorithms of music composition increase in p...
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02/14/2018
BachProp: Learning to Compose Music in Multiple Styles
Hand in hand with deep learning advancements, algorithms of music compos...
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02/14/2018
A General Model of Music Composition
Hand in hand with deep learning advancements, algorithms of music compos...
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02/12/2018
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Modern reinforcement learning algorithms reach super-human performance i...
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12/29/2017
Non-linear motor control by local learning in spiking neural networks
Learning weights in a spiking neural network with hidden neurons, using ...
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12/28/2017
Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory
Learning and memory are intertwined in our brain and their relationship ...
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02/21/2017
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
Brains need to predict how the body reacts to motor commands. It is an o...
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12/09/2016
Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity
In machine learning, error back-propagation in multi-layer neural networ...
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06/23/2016
Algorithmic Composition of Melodies with Deep Recurrent Neural Networks
A big challenge in algorithmic composition is to devise a model that is ...
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06/17/2016