The brain faces a constant challenge of balancing the need to process various external stimuli and internal drives without becoming overwhelmed by irrelevant information. In a new paper published in Neuron, a team of neuroscientists explains how the brain achieves cognitive flexibility through a property known as “mixed selectivity.” This concept challenges the traditional idea that each neuron has a dedicated function and suggests that many neurons can be involved in multiple computational efforts simultaneously.
Mixed selectivity allows neurons to wear multiple “hats” and participate in different computations, depending on the task at hand. This flexibility ensures that the brain can represent the right dimensions of a complex task without unnecessary complexity. According to the authors, mixed selectivity is crucial for enabling mental abilities and cognitive capacity, particularly in the medial prefrontal cortex, where neurons work together in dynamic ensembles to process diverse information.
The idea of mixed selectivity originated in 2000 when researchers discovered that a significant portion of neurons in the prefrontal cortex were involved in multiple tasks, challenging the traditional notion of dedicated functions for each neuron. Subsequent research has shown that ensembles of neurons with mixed selectivity can accommodate more information about a task compared to neurons with invariant functions, providing the brain with computational flexibility.
In the new paper, the researchers illustrate how a neural ensemble with mixed selectivity can integrate multiple dimensions of information when making decisions, such as deciding whether to eat a berry. Despite the evidence supporting mixed selectivity, there are still open questions about how neurons are recruited to tasks and how they remain focused on what is relevant. The authors suggest that brain oscillations and neuromodulators play a crucial role in recruiting neurons into computational ensembles and gating the information that is important for the task.
Different forms of mixed selectivity, such as pure selectivity, linear mixed selectivity, and nonlinear mixed selectivity, help neurons process multiple inputs and make decisions based on various factors. Mechanisms such as brain oscillations and neuromodulators enable neurons to focus on salient information and form functional networks that support cognitive processes. Understanding mixed selectivity is essential for unraveling the complex mechanisms underlying cognition and providing the brain with the processing power needed for complex thought and action.