89 citations · 328 across the 17 of their papers we have counts for
6 papers · 1 filter
Conductance-based dendrites perform Bayes-optimal cue integration
Jakob Jordan, João Sacramento, Willem A. M. Wybo +2
A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimall…
Conductance-based dendrites perform reliability-weighted opinion pooling
Jakob Jordan, João Sacramento, Mihai A. Petrovici +1
Cue integration, the combination of different sources of information to reduce uncertainty, is a fundamental computational principle of brain function. Starting from a normative mo…
Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks
Thomas Mesnard, Gaetan Vignoud, Joao Sacramento +2
In the past few years, deep learning has transformed artificial intelligence research and led to impressive performance in various difficult tasks. However, it is still unclear how…
Dendritic cortical microcircuits approximate the backpropagation algorithm
João Sacramento, Rui Ponte Costa, Yoshua Bengio +1
Deep learning has seen remarkable developments over the last years, many of them inspired by neuroscience. However, the main learning mechanism behind these advances - error backpr…
Dendritic error backpropagation in deep cortical microcircuits
João Sacramento, Rui Ponte Costa, Yoshua Bengio +1
Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates the necessary synaptic modifications acro…
Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory
João Sacramento, Andreas Wichert
We investigate from a computational perspective the efficiency of the Willshaw synaptic update rule in the context of familiarity discrimination, a binary-answer, memory-related ta…