Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

Flexible behaviors over long timescales are thought to engage recurrent neural networks in
deep brain regions, which are experimentally challenging to study. In insects, recurrent …

Deep neural networks currently provide the best quantitative models of the response
patterns of neurons throughout the primate ventral visual stream. However, such networks …

Brains process information in spiking neural networks. Their intricate connections shape the
diverse functions these networks perform. Yet how network connectivity relates to function is …

Decades of neurobiological research have disclosed the diverse manners in which the
response properties of neurons are dynamically modulated to support adaptive cognitive …

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre-and postsynaptic activity. However, models that rely …

Highlights•Multiple scales of learning (and hence meta-learning) are ubiquitous in
nature.•Many existing lines of work in neuroscience and cognitive science touch upon …

Deep neural networks provide the current best models of visual information processing in
the primate brain. Drawing on work from computer vision, the most commonly used networks …

The human brain is a complex, adaptive system comprised of billions of cells with trillions of
connections. The interactions between the elements of the system oppose this seemingly …