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 …

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

The human brain contains multiple regions with distinct, often highly specialized functions,
from recognizing faces to understanding language to thinking about what others are …

Visual neurons respond selectively to features that become increasingly complex from the
eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) …

A central principle in neuroscience is that neurons within the brain act in concert to produce
perception, cognition, and adaptive behavior. Neurons are organized into specialized brain …

The rapid development and open-source release of highly performant computer vision
models offers new potential for examining how different inductive biases impact …

Understanding the brain's perception algorithm is a highly intricate problem, as the inherent
complexity of sensory inputs and the brain's nonlinear processing make characterizing …

The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to
diverse visual features. Yet it remains unclear how neural populations in each area partition …

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of
primate and rodent. However, they highly simplify the computational properties of neurons …

Convolutional neural networks trained on object recognition derive inspiration from the
neural architecture of the visual system in mammals, and have been used as models of the …