The power of mixed selectivity: understanding brain function and cognition

Every day, our brains strive to optimize a trade-off: with so many events happening around us, and at the same time so many internal urges and memories, our thoughts must be flexible but focused enough to guide everything we need to do. In a new paper in the journal Neuron, a team of neuroscientists describes how the brain achieves the cognitive ability to integrate all relevant information without becoming overwhelmed by what doesn’t matter.

The authors argue that this flexibility stems from a key property observed in many neurons: “mixed selectivity.” While many neuroscientists previously thought that each cell had only one specialized function, more recent evidence has shown that many neurons can participate in different computational ensembles working in parallel. In other words, when a rabbit is considering nibbling on some lettuce in the garden, a single neuron might be involved not only in assessing its hunger, but also in hearing a hawk overhead or smelling a coyote in the trees and judging how far away the lettuce is.

The brain does not multitask, said paper co-author Earl K. Miller, a professor at the Picower Institute for the Study of Learning and Memory at MIT and one of the pioneers of the idea of mixed selectivity, but many cells do have the ability to engage in multiple computations (essentially, "thoughts"). In the new paper, the authors describe the specific mechanisms the brain uses to recruit neurons to different computations and to ensure that those neurons represent the right number of dimensions of a complex task.

These neurons perform many functions. With mixed selectivity, you can have a representational space that is as complex as you need it to be, and no more. That's where the flexibility of cognitive function lies."

Earl K. Miller, Professor, Picower Institute for the Study of Learning and Memory, Massachusetts Institute of Technology

Co-author Kay Tai, a professor at the Salk Institute and the University of California, San Diego, said mixed selectivity among neurons, particularly in the medial prefrontal cortex, is key to enabling many mental abilities.

"The MPFC is like a whisper that represents so much information through highly flexible and dynamic ensembles," Tai said. "Mixed selectivity is the property that gives us our flexibility, cognitive ability, and creativity. It's the secret to maximizing computational power, which is essentially the basis of intelligence."

Origin of the idea

The idea of mixed selectivity got its start in 2000, when Miller and his colleague John Duncan defended a surprising result from a study of cognitive function in Miller’s lab. When animals sorted images into categories, about 30 percent of the neurons in the brain’s prefrontal cortex seemed to be recruited. Skeptics who believed that each neuron had a dedicated function scoffed at the idea that the brain could dedicate so many cells to just one task. Miller and Duncan’s answer was that perhaps the cells had the flexibility to participate in many computations. The ability to serve in one brain group, as it did, did not preclude their ability to serve many others.

But what benefit does mixed selectivity bring? In 2013, Miller teamed up with two co-authors of the new paper, Mattia Rigotti of IBM Research and Stefano Fusi of Columbia University, to show how mixed selectivity gives the brain powerful computational flexibility. In essence, an ensemble of neurons with mixed selectivity can accommodate many more dimensions of information about a task than a population of neurons with fixed functions.

"Since our original work, we have made advances in understanding the theory of mixed selectivity through the lens of classical machine learning ideas," Rigotti said. "On the other hand, questions important to experimentalists about the mechanisms implementing this at the cellular level have been relatively understudied. This collaboration and this new paper aim to fill that gap."

In the new paper, the authors imagine a mouse deciding whether to eat a berry. It might smell delicious (that’s one dimension). It might be poisonous (that’s another). Another dimension or two of the problem might come in the form of a social cue. If a mouse smells a berry on another mouse’s breath, the berry is probably edible (depending on the other mouse’s apparent health). A neural ensemble with mixed selectivity could integrate all of this.

Attracting neurons

While mixed selectivity is supported by abundant evidence—it has been observed throughout the cortex and in other brain regions such as the hippocampus and amygdala—open questions remain. For example, how do neurons get recruited to tasks, and how do neurons that are so broad-minded stay tuned to only what’s truly mission-critical?

In the new study, researchers including Marcus Benna of UC San Diego and Felix Taschbach of the Salk Institute identify the forms of mixed selectivity the researchers observed and argue that when oscillations (also known as "brain waves") and neuromodulators (chemicals like serotonin or dopamine that influence neural function) recruit neurons into computational ensembles, they also help them "filter" what's important for that purpose.

Of course, some neurons specialize in a particular input, but the authors point out that they are the exception, not the rule. These cells, the authors say, have "pure selectivity." They only care if the rabbit sees lettuce. Some neurons exhibit "linear mixed selectivity," meaning that their response depends predictably on the sum of multiple inputs (the rabbit sees lettuce and feels hungry). The neurons that add the most measurement flexibility are those with "nonlinear mixed selectivity," which can account for multiple independent variables without having to sum them all together. Instead, they can account for a whole set of independent conditions (e.g., there is lettuce, I am hungry, I can't hear hawks, I can't smell coyotes, but the lettuce is far away, and I can see a pretty sturdy fence).

So what attracts neurons to focus on meaningful factors, no matter how many there are? One mechanism is oscillations, which occur in the brain when many neurons maintain their electrical activity at the same rhythm. This coordinated activity allows information to be shared, essentially tuning them together, like a group of cars all playing the same radio station (a broadcast of a hawk circling overhead, perhaps). Another mechanism the authors highlight is neuromodulators. These are chemicals that, when they reach receptors inside cells, can also influence their activity. For example, a surge in acetylcholine can similarly tune neurons with the appropriate receptors to a particular activity or information (perhaps the sensation of hunger).

"These two mechanisms likely work together to dynamically form functional networks," the authors write.

Understanding mixed selectivity, they continue, is critical to understanding cognition.

"Mixed selectivity is ubiquitous," they conclude. "It is present across species and serves functions ranging from high-level cognition to 'automatic' sensorimotor processes such as object recognition. The widespread occurrence of mixed selectivity highlights its fundamental role in providing the brain with the scalable processing power required for complex thought and action."

Details of the study are available on the CELL journal page