The power of mixed selectivity: Understanding brain function and cognition

Every day our brain strives to optimize a trade-off: with many events happening around us, and at the same time many internal drives 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 is not relevant.

The authors argue that 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 lettuce in the garden, one neuron may be involved not only in judging its hunger, but also in hearing a hawk overhead or smelling a coyote in the trees and determining how far away the lettuce is. p>

The brain is not a multitasker, said co-author Earl K. Miller, a professor at the Picower Institute for 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 computational processes (essentially, "thoughts"). In the new paper, the authors describe specific mechanisms that the brain uses to recruit neurons to perform various computations and to ensure that those neurons represent the correct number of dimensions of a complex problem.

These neurons perform many functions. With mixed selectivity it is possible to have a representative space that is as complex as it needs to be and no more. This is where the flexibility of cognitive function lies."

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

Co-author Kaye Tai, a professor at the Salk Institute and the University of California, San Diego, said that mixed selectivity among neurons, especially 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 is the secret to maximizing processing power, which is essentially the basis of intelligence."

Origin of the idea

The idea of mixed selectivity originated in 2000, when Miller and his colleague John Duncan defended a surprising result from research on cognitive function in Miller's laboratory. When the animals sorted the images into categories, about 30 percent of the neurons in the brain's prefrontal cortex seemed to be activated. 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 cells had the flexibility to participate in many calculations. The ability to serve in one brain group, as it was, did not preclude their ability to serve many others.

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

"Since our initial work, we have made progress 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 that do this at the cellular level have been relatively little explored. This collaboration and this new paper were aimed at filling this gap."

In the new paper, the authors present a mouse deciding whether to eat a berry. She may smell delicious (that's one dimension). It can be poisonous (that's another thing). Another dimension or two of the problem may arise in the form of a social signal. If a mouse smells a berry on another mouse's breath, then the berry is probably edible (depending on the other mouse's apparent health). A neural ensemble with mixed selectivity will be able to integrate all this.

Attracting neurons

Although 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 only to what is really important for the mission?

In a new study, researchers including Marcus Benna of UC San Diego and Felix Taschbach of the Salk Institute identify the forms of mixed selectivity that the researchers observed and argue that when oscillations (also known as "brain waves") and neuromodulators (chemical substances such as serotonin or dopamine that influence neural function) attract neurons into computational ensembles, they also help them “filter” what is important for this purpose.

Of course, some neurons are specialized for a particular input, but the authors note that they are the exception, not the rule. The authors say these cells have "pure selectivity." They only care if the rabbit sees the lettuce. Some neurons exhibit "linear mixed selectivity," meaning that their response depends predictably on the sum of multiple inputs (a rabbit sees lettuce and feels hungry). The neurons adding the most measurement flexibility are those with “nonlinear mixed selectivity,” which can account for multiple independent variables without the need to sum them. Instead, they can take into account a whole set of independent conditions (for example, there is lettuce, I'm hungry, I don't hear any hawks, I don't smell coyotes, but the lettuce is far away and I can see a fairly strong fence).

So, what attracts neurons to focus on significant factors, no matter how many there are? One mechanism is oscillation, which occurs in the brain when many neurons maintain their electrical activity in 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 (maybe a broadcast of a hawk circling overhead). Another mechanism that the authors highlight is neuromodulators. These are chemicals that, when they reach receptors inside cells, can also affect their activity. For example, a surge of acetylcholine can similarly prime neurons with corresponding receptors for a specific activity or information (perhaps the feeling 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 a variety of functions 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 needed for complex thoughts and actions." p>

Read more about the study on CELL magazine