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The neuroconnectionist research programme
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 …
Convolutional neural networks as a model of the visual system: Past, present, and future
Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …
biological vision. They have since become successful tools in computer vision and state-of …
Getting aligned on representational alignment
Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …
Deep problems with neural network models of human vision
Deep neural networks (DNNs) have had extraordinary successes in classifying
photographic images of objects and are often described as the best models of biological …
photographic images of objects and are often described as the best models of biological …
THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
Understanding object representations requires a broad, comprehensive sampling of the
objects in our visual world with dense measurements of brain activity and behavior. Here …
objects in our visual world with dense measurements of brain activity and behavior. Here …
Revealing the multidimensional mental representations of natural objects underlying human similarity judgements
Abstract Objects can be characterized according to a vast number of possible criteria (such
as animacy, shape, colour and function), but some dimensions are more useful than others …
as animacy, shape, colour and function), but some dimensions are more useful than others …
Harmonizing the object recognition strategies of deep neural networks with humans
The many successes of deep neural networks (DNNs) over the past decade have largely
been driven by computational scale rather than insights from biological intelligence. Here …
been driven by computational scale rather than insights from biological intelligence. Here …
Deep learning: the good, the bad, and the ugly
Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …
solved before machines can act intelligently. Recent developments in a branch of machine …
Improving neural network representations using human similarity judgments
Deep neural networks have reached human-level performance on many computer vision
tasks. However, the objectives used to train these networks enforce only that similar images …
tasks. However, the objectives used to train these networks enforce only that similar images …
Human alignment of neural network representations
Today's computer vision models achieve human or near-human level performance across a
wide variety of vision tasks. However, their architectures, data, and learning algorithms differ …
wide variety of vision tasks. However, their architectures, data, and learning algorithms differ …