7 citations · 12 across the 4 of their papers we have counts for
4 papers · 1 filter
Dendritic structure enables powerful plasticity
Ben von Hünerbein, Federico Benitez, Kevin Max +10
Over the past decades, it has become increasingly clear that the complex morphology of cortical neurons is more than just a quirk of evolution, and that dendritic compartments serv…
A Variational Latent Equilibrium for Learning in Neuronal Circuits
Simon Brandt, Paul Haider, Walter Senn +2
Brains remain unrivaled in their ability to recognize and generate complex spatiotemporal patterns. While AI is able to reproduce some of these capabilities, deep learning algorith…
Backpropagation through space, time, and the brain
Benjamin Ellenberger, Paul Haider, Jakob Jordan +5
How physical networks of neurons, bound by spatio-temporal locality constraints, can perform efficient credit assignment, remains, to a large extent, an open question. In machine l…
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Paul Haider, Benjamin Ellenberger, Laura Kriener +3
The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag.…