557 citations · 909 across the 24 of their papers we have counts for
28 papers
Letting the neural code speak: Automated characterization of monkey visual neurons through human language
Vedang Lad, Katrin Franke, Tamar Rott Shaham +4
Understanding what individual neurons encode is a core question in neuroscience. In primary visual cortex (V1), mathematical models (e.g., Gabor functions) capture neural selectivi…
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert +18
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains un…
Replay Can Provably Increase Forgetting
Yasaman Mahdaviyeh, James Lucas, Mengye Ren +3
Continual learning seeks to enable machine learning systems to solve an increasing corpus of tasks sequentially. A critical challenge for continual learning is forgetting, where th…
Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela Vystrčilová +4
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embedd…
TRACE: Contrastive learning for multi-trial time-series data in neuroscience
Lisa Schmors, Dominic Gonschorek, Jan Niklas Böhm +9
Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Co…
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová +22
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural…
Most discriminative stimuli for functional cell type clustering
Max F. Burg, Thomas Zenkel, Michaela Vystrčilová +15
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types c…
Generalization properties of contrastive world models
Kandan Ramakrishnan, R. James Cotton, Xaq Pitkow +1
Recent work on object-centric world models aim to factorize representations in terms of objects in a completely unsupervised or self-supervised manner. Such world models are hypoth…
Continual Learning: Applications and the Road Forward
Eli Verwimp, Rahaf Aljundi, Shai Ben-David +17
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting wh…
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Laura Hansel +10
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual inpu…
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Anthony Zador, Sean Escola, Blake Richards +24
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in…
The Sensorium competition on predicting large-scale mouse primary visual cortex activity
Konstantin F. Willeke, Paul G. Fahey, Mohammad Bashiri +12
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect t…
Understanding robustness and generalization of artificial neural networks through Fourier masks
Nikos Karantzas, Emma Besier, Josue Ortega Caro +4
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generaliz…
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks
Zhe Li, Andreas S. Tolias, Xaq Pitkow
Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal. While graph neural networks provide a useful relational inductive…
Towards robust vision by multi-task learning on monkey visual cortex
Shahd Safarani, Arne Nix, Konstantin Willeke +5
Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to image distortions is surprisingly fragile. In contrast, the…
AvaTr: One-Shot Speaker Extraction with Transformers
Shell Xu Hu, Md Rifat Arefin, Viet-Nhat Nguyen +3
To extract the voice of a target speaker when mixed with a variety of other sounds, such as white and ambient noises or the voices of interfering speakers, we extend the Transforme…
Class-Incremental Learning with Generative Classifiers
Gido M. van de Ven, Zhe Li, Andreas S. Tolias
Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay,…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
Probing Few-Shot Generalization with Attributes
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang +5
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge. In this work, we consider few-shot classification, a…
Factorized Neural Processes for Neural Processes: -Shot Prediction of Neural Responses
R. James Cotton, Fabian H. Sinz, Andreas S. Tolias
In recent years, artificial neural networks have achieved state-of-the-art performance for predicting the responses of neurons in the visual cortex to natural stimuli. However, the…
Learning From Brains How to Regularize Machines
Zhe Li, Wieland Brendel, Edgar Y. Walker +7
Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input…
A community-based transcriptomics classification and nomenclature of neocortical cell types
Rafael Yuste, Michael Hawrylycz, Nadia Aalling +68
To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological…
Three scenarios for continual learning
Gido M. van de Ven, Andreas S. Tolias
Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning. In recent yea…
A rotation-equivariant convolutional neural network model of primary visual cortex
Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis +7
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural respo…