What are the core learning algorithms in brains? Nativists propose that intelligence emerges
from innate domain-specific knowledge systems, whereas empiricists propose that …

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance
based on deep learning. Unique to Neuroscience, deep learning models can be used not …

The brain can represent behaviorally relevant information through the firing of individual
neurons as well as the coordinated firing of ensembles of neurons. Neurons in the …

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory
networks in the brain must form object representations invariant to stimulus changes, such …

Abstract Language neuroscience currently relies on two major experimental paradigms:
controlled experiments using carefully hand-designed stimuli, and natural stimulus …

Humans and animals have a rich and flexible understanding of the physical world, which
enables them to infer the underlying dynamical trajectories of objects and events, plausible …

Neuroscientists apply a range of analysis tools to recorded neural activity in order to glean
insights into how neural circuits drive behavior in organisms. Despite the fact that these tools …

Normative models of synaptic plasticity use computational rationales to arrive at predictions
of behavioral and network-level adaptive phenomena. In recent years, there has been an …

It is widely believed that grid cells provide cues for path integration, with place cells
encoding an animal's location and environmental identity. When entering a new …

Our algorithmic understanding of vision has been revolutionized by a reverse engineering
paradigm that involves building artificial systems that perform the same tasks as biological …