[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 …
Improving the accuracy of single-trial fMRI response estimates using GLMsingle
Advances in artificial intelligence have inspired a paradigm shift in human neuroscience,
yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide …
yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide …
Brain-like functional specialization emerges spontaneously in deep neural networks
The human brain contains multiple regions with distinct, often highly specialized functions,
from recognizing faces to understanding language to thinking about what others are …
from recognizing faces to understanding language to thinking about what others are …
[HTML][HTML] A neural population selective for song in human auditory cortex
How is music represented in the brain? While neuroimaging has revealed some spatial
segregation between responses to music versus other sounds, little is known about the …
segregation between responses to music versus other sounds, little is known about the …
Computational models of category-selective brain regions enable high-throughput tests of selectivity
Cortical regions apparently selective to faces, places, and bodies have provided important
evidence for domain-specific theories of human cognition, development, and evolution. But …
evidence for domain-specific theories of human cognition, development, and evolution. But …
Understanding human object vision: a picture is worth a thousand representations
Objects are the core meaningful elements in our visual environment. Classic theories of
object vision focus upon object recognition and are elegant and simple. Some of their …
object vision focus upon object recognition and are elegant and simple. Some of their …
Cortical topographic motifs emerge in a self-organized map of object space
The human ventral visual stream has a highly systematic organization of object information,
but the causal pressures driving these topographic motifs are highly debated. Here, we use …
but the causal pressures driving these topographic motifs are highly debated. Here, we use …
Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Brain networks exist within the confines of resource limitations. As a result, a brain network
must overcome the metabolic costs of growing and sustaining the network within its physical …
must overcome the metabolic costs of growing and sustaining the network within its physical …
What can 1.8 billion regressions tell us about the pressures sha** high-level visual representation in brains and machines?
The rapid development and open-source release of highly performant computer vision
models offers new potential for examining how different inductive biases impact …
models offers new potential for examining how different inductive biases impact …