Towards deep learning with segregated dendrites
Deep learning has led to significant advances in artificial intelligence, in part, by adopting
strategies motivated by neurophysiology. However, it is unclear whether deep learning …
strategies motivated by neurophysiology. However, it is unclear whether deep learning …
Dendritic cortical microcircuits approximate the backpropagation algorithm
J Sacramento, R Ponte Costa… - Advances in neural …, 2018 - proceedings.neurips.cc
Deep learning has seen remarkable developments over the last years, many of them
inspired by neuroscience. However, the main learning mechanism behind these advances …
inspired by neuroscience. However, the main learning mechanism behind these advances …
Single-phase deep learning in cortico-cortical networks
The error-backpropagation (backprop) algorithm remains the most common solution to the
credit assignment problem in artificial neural networks. In neuroscience, it is unclear whether …
credit assignment problem in artificial neural networks. In neuroscience, it is unclear whether …
No free lunch from deep learning in neuroscience: A case study through models of the entorhinal-hippocampal circuit
Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance
based on deep learning. Unique to Neuroscience, deep learning models can be used not …
based on deep learning. Unique to Neuroscience, deep learning models can be used not …
Deep learning for cognitive neuroscience
KR Storrs, N Kriegeskorte - arXiv preprint arXiv:1903.01458, 2019 - arxiv.org
Neural network models can now recognise images, understand text, translate languages,
and play many human games at human or superhuman levels. These systems are highly …
and play many human games at human or superhuman levels. These systems are highly …
A deep learning framework for neuroscience
Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …
Spike-driven multi-scale learning with hybrid mechanisms of spiking dendrites
Neural dendrites play a critical role in various cognitive functions, including spatial
navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal …
navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal …
Individual differences among deep neural network models
Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …
a modeling framework for neural computations in the primate brain. Just like individual …
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related
behaviors. Well-known experimental results have revealed specialized cell types in MEC …
behaviors. Well-known experimental results have revealed specialized cell types in MEC …
Credit assignment in neural networks through deep feedback control
A Meulemans, M Tristany Farinha… - Advances in …, 2021 - proceedings.neurips.cc
The success of deep learning sparked interest in whether the brain learns by using similar
techniques for assigning credit to each synaptic weight for its contribution to the network …
techniques for assigning credit to each synaptic weight for its contribution to the network …