Representations and generalization in artificial and brain neural networks
Humans and animals excel at generalizing from limited data, a capability yet to be fully
replicated in artificial intelligence. This perspective investigates generalization in biological …
replicated in artificial intelligence. This perspective investigates generalization in biological …
The neural architecture of language: Integrative modeling converges on predictive processing
The neuroscience of perception has recently been revolutionized with an integrative
modeling approach in which computation, brain function, and behavior are linked across …
modeling approach in which computation, brain function, and behavior are linked across …
An ecologically motivated image dataset for deep learning yields better models of human vision
Deep neural networks provide the current best models of visual information processing in
the primate brain. Drawing on work from computer vision, the most commonly used networks …
the primate brain. Drawing on work from computer vision, the most commonly used networks …
Building transformers from neurons and astrocytes
Glial cells account for between 50% and 90% of all human brain cells, and serve a variety of
important developmental, structural, and metabolic functions. Recent experimental efforts …
important developmental, structural, and metabolic functions. Recent experimental efforts …
Texture-like representation of objects in human visual cortex
The human visual ability to recognize objects and scenes is widely thought to rely on
representations in category-selective regions of the visual cortex. These representations …
representations in category-selective regions of the visual cortex. These representations …
Neural representational geometry underlies few-shot concept learning
Understanding the neural basis of the remarkable human cognitive capacity to learn novel
concepts from just one or a few sensory experiences constitutes a fundamental problem. We …
concepts from just one or a few sensory experiences constitutes a fundamental problem. We …
High-performing neural network models of visual cortex benefit from high latent dimensionality
Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core
representational principles of computational models in neuroscience. Here we examined the …
representational principles of computational models in neuroscience. Here we examined the …
Learning efficient coding of natural images with maximum manifold capacity representations
The efficient coding hypothesis proposes that the response properties of sensory systems
are adapted to the statistics of their inputs such that they capture maximal information about …
are adapted to the statistics of their inputs such that they capture maximal information about …
Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading
The visual word form area (VWFA) is a region of human inferotemporal cortex that emerges
at a fixed location in the occipitotemporal cortex during reading acquisition and …
at a fixed location in the occipitotemporal cortex during reading acquisition and …
Rich and lazy learning of task representations in brains and neural networks
How do neural populations code for multiple, potentially conflicting tasks? Here, we used
computational simulations involving neural networks to define “lazy” and “rich” coding …
computational simulations involving neural networks to define “lazy” and “rich” coding …