Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.

Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other.

Tests for consciousness in humans and beyond.

Which systems/organisms are conscious? New tests for consciousness ('C-tests') are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology.

The neuronal basis of human creativity.

Human creativity is a powerful cognitive ability underlying all uniquely human cultural and scientific advancement. However, the neuronal basis of this creative ability is unknown. Here, I propose that slow, spontaneous fluctuations in neuronal activity, also known as "resting state" fluctuations, constitute a universal mechanism underlying all facets of human creativity.

Perceptual stability reflected in neuronal pattern similarities in human visual cortex.

The magnitude of neuronal activation is commonly considered a critical factor for conscious perception of visual content. However, this dogma contrasts with the phenomenon of rapid adaptation, in which the magnitude of neuronal activation drops dramatically in a rapid manner while the visual stimulus and the conscious experience it elicits remain stable. Here, we report that the profiles of multi-site activation patterns and their relational geometry-i.