Computational principles of mechanisms underlying cognitive functions

The brain is composed of billions of neurons. Many of these neurons respond to particular features of the sensory inputs. For example, in the visual system, there are neurons that respond when viewing a horizontal edge but not when viewing a vertical one. These types of neurons have been studied for a long time. However, when the brain is asked to perform more complex tasks than simply observe an individual feature of a visual stimulus, a new class of neuron emerge. These neurons are not so simple: they respond to multiple features of the sensory input and their response is modulated by our expectations, our feelings and more generally by any aspect of our thoughts. Said to have so-called “mixed selectivity,” these neurons were ignored for many years, but, thanks to work from our group and others, their importance is just now being appreciated. That is, even though their responses are not easily interpretable, they play a critical role in solving complex cognitive tasks. In abstract terms, these neurons increase the ability of downstream “output” neurons to generate a much larger set of responses for a given set of inputs. This enhanced ability likely underlies the capacity to perform a complex and flexible set of actions based on the same sensory input. Working in close collaboration with experimental neuroscientist Daniel Salzman, we aim to understand the role of mixed selectivity neurons in processing complex cognitive tasks. We will use technology for simultaneously monitoring the activity of many neurons in multiple brain regions to determine what information is contained in those brain areas and how that information is related to how animals behave. The close link between theory and experiments will facilitate the development of new mathematical tools for analyzing the collective activity of neurons, bringing us one step closer to understanding not just how neurons encode simple stimuli such as horizontal and vertical bars, but perform complex tasks such as decision-making.

Stefano Fusi, Columbia University

Daniel Salzman, Columbia University