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Found 20 papers, 10 papers with code
Generalization properties of contrastive world models
We systematically test the model under a number of different OOD generalization scenarios such as extrapolation to new object attributes, introducing new conjunctions or new attributes.
Most discriminative stimuli for functional cell type clustering
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
Continual Learning: Applications and the Road Forward
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 what was learned in the past.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
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 respect to visual input.
Understanding robustness and generalization of artificial neural networks through Fourier masks
Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place.
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks
In this work we trained graph neural networks to fit time series from an example nonlinear dynamical system, the belief propagation algorithm.
Towards robust vision by multi-task learning on monkey visual cortex
We found that our co-trained network is more sensitive to content than noise when compared to a Baseline network that we trained for image classification alone.
Class-Incremental Learning with Generative Classifiers
As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x|y).
Generalization in data-driven models of primary visual cortex
With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network.
Exploring representation learning for flexible few-shot tasks
In this work, we consider a realistic setting where the relationship between examples can change from episode to episode depending on the task context, which is not given to the learner.
Probing Few-Shot Generalization with Attributes
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge.
Factorized Neural Processes for Neural Processes: $K$-Shot Prediction of Neural Responses
We overcome this limitation by formulating the problem as $K$-shot prediction to directly infer a neuron's tuning function from a small set of stimulus-response pairs using a Neural Process.
Rotation-invariant clustering of neuronal responses in primary visual cortex
Similar to a convolutional neural network (CNN), the mammalian retina encodes visual information into several dozen nonlinear feature maps, each formed by one ganglion cell type that tiles the visual space in an approximately shift-equivariant manner.
Learning From Brains How to Regularize Machines
We propose to regularize CNNs using large-scale neuroscience data to learn more robust neural features in terms of representational similarity.
Three scenarios for continual learning
Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning.
Generative replay with feedback connections as a general strategy for continual learning
A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previously learned tasks when trained on a new one.
Three continual learning scenarios and a case for generative replay
To enable more meaningful comparisons, we identified three distinct continual learning scenarios based on whether task identity is known and, if it is not, whether it needs to be inferred.
A rotation-equivariant convolutional neural network model of primary visual cortex
We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations.
