[HTML][HTML] Using artificial neural networks to ask 'why'questions of minds and brains
Neuroscientists have long characterized the properties and functions of the nervous system,
and are increasingly succeeding in answering how brains perform the tasks they do. But the …
and are increasingly succeeding in answering how brains perform the tasks they do. But the …
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 …
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 …
Self-supervised learning of representations for space generates multi-modular grid cells
To solve the spatial problems of map**, localization and navigation, the mammalian
lineage has developed striking spatial representations. One important spatial representation …
lineage has developed striking spatial representations. One important spatial representation …
On the importance of severely testing deep learning models of cognition
Researchers studying the correspondences between Deep Neural Networks (DNNs) and
humans often give little consideration to severe testing when drawing conclusions from …
humans often give little consideration to severe testing when drawing conclusions from …
Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution
We can now measure the connectivity of every neuron in a neural circuit, but we are still
blind to other biological details, including the dynamical characteristics of each neuron. The …
blind to other biological details, including the dynamical characteristics of each neuron. 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 …
From lazy to rich to exclusive task representations in neural networks and neural codes
Neural circuits—both in the brain and in “artificial” neural network models—learn to solve a
remarkable variety of tasks, and there is a great current opportunity to use neural networks …
remarkable variety of tasks, and there is a great current opportunity to use neural networks …
Modelling dataset bias in machine-learned theories of economic decision-making
Normative and descriptive models have long vied to explain and predict human risky
choices, such as those between goods or gambles. A recent study reported the discovery of …
choices, such as those between goods or gambles. A recent study reported the discovery of …
Shared functional specialization in transformer-based language models and the human brain
When processing language, the brain is thought to deploy specialized computations to
construct meaning from complex linguistic structures. Recently, artificial neural networks …
construct meaning from complex linguistic structures. Recently, artificial neural networks …