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07/05/2023
Machine learning at the mesoscale: a computation-dissipation bottleneck
The cost of information processing in physical systems calls for a trade...
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11/21/2022
Neural networks trained with SGD learn distributions of increasing complexity
The ability of deep neural networks to generalise well even when they in...
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02/01/2022
Data-driven emergence of convolutional structure in neural networks
Exploiting data invariances is crucial for efficient learning in both ar...
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01/24/2022
Input correlations impede suppression of chaos and learning in balanced rate networks
Neural circuits exhibit complex activity patterns, both spontaneously an...
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09/20/2020
Epidemic mitigation by statistical inference from contact tracing data
Contact-tracing is an essential tool in order to mitigate the impact of ...
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05/25/2020
Optimal Learning with Excitatory and Inhibitory synapses
Characterizing the relation between weight structure and input/output st...
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12/29/2018
Training dynamically balanced excitatory-inhibitory networks
The construction of biologically plausible models of neural circuits is ...
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05/20/2016
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
In artificial neural networks, learning from data is a computationally d...
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02/04/2016
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Cataloging the neuronal cell types that comprise circuitry of individual...
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11/18/2015
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to effic...
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09/18/2015