Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

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 …

Mental representations remain the central posits of psychology after many decades of
scrutiny. However, there is no consensus about the representational format (s) of biological …

Whether current or near-term AI systems could be conscious is a topic of scientific interest
and increasing public concern. This report argues for, and exemplifies, a rigorous and …

Prominent accounts of sentient behavior depict brains as generative models of organismic
interaction with the world, evincing intriguing similarities with current advances in generative …

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 …

In the cognitive, computational, and neuro-sciences, practitioners often reason about what
computational models represent or learn, as well as what algorithm is instantiated. The …

Models that predict brain responses to stimuli provide one measure of understanding of a
sensory system and have many potential applications in science and engineering. Deep …

One of the most impactful findings in computational neuroscience over the past decade is
that the object recognition accuracy of deep neural networks (DNNs) correlates with their …

Researchers studying the correspondences between Deep Neural Networks (DNNs) and
humans often give little consideration to severe testing when drawing conclusions from …