Significant experimental, computational, and theoretical work has identified rich structure
within the coordinated activity of interconnected neural populations. An emerging challenge …

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

How do neural populations code for multiple, potentially conflicting tasks? Here we used
computational simulations involving neural networks to define" lazy" and" rich" coding …

When experts are immersed in a task, do their brains prioritize task-related activity? Most
efforts to understand neural activity during well-learned tasks focus on cognitive …

The brain has the ability to flexibly perform many tasks, but the underlying mechanism
cannot be elucidated in traditional experimental and modeling studies designed for one task …

A major shift is happening within neurophysiology: a population doctrine is drawing level
with the single-neuron doctrine that has long dominated the field. Population-level ideas …

The analysis of neural dynamics in several brain cortices has consistently uncovered low-
dimensional manifolds that capture a significant fraction of neural variability. These neural …

Highlights•New generations of recording and computing technologies have enabled
neuroscience at the level of the neural population.•Landmark scientific findings suggest the …

The curse of dimensionality plagues models of reinforcement learning and decision making.
The process of abstraction solves this by constructing variables describing features shared …

Neurons often respond to diverse combinations of task-relevant variables. This form of
mixed selectivity plays an important computational role which is related to the dimensionality …