The neuroconnectionist research programme
A Doerig, RP Sommers, K Seeliger… - Nature Reviews …, 2023 - nature.com
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
Robust heterogeneous federated learning under data corruption
Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …
However, due to the limitations of data collection, storage, and transmission conditions, as …
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing interpretability, adversarial robustness and zero-shot learning
F Mumuni, A Mumuni - Cognitive Systems Research, 2023 - Elsevier
We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
State-dependent pupil dilation rapidly shifts visual feature selectivity
To increase computational flexibility, the processing of sensory inputs changes with
behavioural context. In the visual system, active behavioural states characterized by motor …
behavioural context. In the visual system, active behavioural states characterized by motor …
The algonauts project 2023 challenge: How the human brain makes sense of natural scenes
The sciences of biological and artificial intelligence are ever more intertwined. Neural
computational principles inspire new intelligent machines, which are in turn used to advance …
computational principles inspire new intelligent machines, which are in turn used to advance …
The sensorium competition on predicting large-scale mouse primary visual cortex activity
The neural underpinning of the biological visual system is challenging to study
experimentally, in particular as the neuronal activity becomes increasingly nonlinear with …
experimentally, in particular as the neuronal activity becomes increasingly nonlinear with …
Robust deep learning object recognition models rely on low frequency information in natural images
Machine learning models have difficulty generalizing to data outside of the distribution they
were trained on. In particular, vision models are usually vulnerable to adversarial attacks or …
were trained on. In particular, vision models are usually vulnerable to adversarial attacks or …
Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robustness
While some state-of-the-art artificial neural network systems in computer vision are strikingly
accurate models of the corresponding primate visual processing, there are still many …
accurate models of the corresponding primate visual processing, there are still many …
Lcanets: Lateral competition improves robustness against corruption and attack
Abstract Although Convolutional Neural Networks (CNNs) achieve high accuracy on image
recognition tasks, they lack robustness against realistic corruptions and fail catastrophically …
recognition tasks, they lack robustness against realistic corruptions and fail catastrophically …
Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization
Deciphering the brain's structure-function relationship is key to understanding the neuronal
mechanisms underlying perception and cognition. The cortical column, a vertical …
mechanisms underlying perception and cognition. The cortical column, a vertical …