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07/31/2023
A theory of data variability in Neural Network Bayesian inference
Bayesian inference and kernel methods are well established in machine le...
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07/27/2023
Speed Limits for Deep Learning
State-of-the-art neural networks require extreme computational power to ...
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05/12/2023
Optimal signal propagation in ResNets through residual scaling
Residual networks (ResNets) have significantly better trainability and t...
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01/24/2023
Neuronal architecture extracts statistical temporal patterns
Neuronal systems need to process temporal signals. We here show how high...
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03/21/2022
Origami in N dimensions: How feed-forward networks manufacture linear separability
Neural networks can implement arbitrary functions. But, mechanistically,...
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02/10/2022
Decomposing neural networks as mappings of correlation functions
Understanding the functional principles of information processing in dee...
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12/10/2021
Unified Field Theory for Deep and Recurrent Neural Networks
Understanding capabilities and limitations of different network architec...
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10/13/2020
Unfolding recurrence by Green's functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic tem...
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12/02/2019