Mixed selectivity is a computational property that allows neurons to respond to multiple statistically independent variables, enabling them to represent complex information efficiently. This concept is crucial for flexible neural processing, especially in the prefrontal cortex (PFC), where neurons exhibit mixed selectivity by responding to various stimuli and contexts. Mixed selectivity can be categorized into pure, linear, and nonlinear types. Pure selectivity involves neurons responding to a single variable, while linear mixed selectivity neurons respond to a weighted sum of variables. Nonlinear mixed selectivity neurons, however, cannot be described as a linear combination of inputs and are essential for complex computations. Neural circuits with mixed selectivity can represent high-dimensional information, allowing for flexible and adaptive processing. However, encoding all possible variable combinations is impractical, so gating mechanisms like oscillations and neuromodulation dynamically select which variables are mixed and transmitted. These mechanisms enable the brain to handle complex tasks by focusing on relevant variables and adjusting to changing contexts. The PFC, with its diverse and multifunctional neurons, exemplifies the importance of mixed selectivity in flexible behavior. Neurons in the PFC can respond to multiple variables, allowing for the integration of diverse inputs and the execution of complex tasks. This flexibility is crucial for decision-making and adapting to new situations. Mixed selectivity also plays a role in various brain regions, including the hippocampus and sensory systems, where it contributes to memory formation, sensory processing, and context-dependent behavior. The combination of oscillatory dynamics and neuromodulatory signals further enhances the brain's ability to process information efficiently and adapt to different tasks. In summary, mixed selectivity is a fundamental mechanism that enables the brain to handle complex and flexible tasks by integrating diverse inputs and dynamically adjusting to changing contexts. This computational strategy is essential for cognitive flexibility and adaptive behavior.Mixed selectivity is a computational property that allows neurons to respond to multiple statistically independent variables, enabling them to represent complex information efficiently. This concept is crucial for flexible neural processing, especially in the prefrontal cortex (PFC), where neurons exhibit mixed selectivity by responding to various stimuli and contexts. Mixed selectivity can be categorized into pure, linear, and nonlinear types. Pure selectivity involves neurons responding to a single variable, while linear mixed selectivity neurons respond to a weighted sum of variables. Nonlinear mixed selectivity neurons, however, cannot be described as a linear combination of inputs and are essential for complex computations. Neural circuits with mixed selectivity can represent high-dimensional information, allowing for flexible and adaptive processing. However, encoding all possible variable combinations is impractical, so gating mechanisms like oscillations and neuromodulation dynamically select which variables are mixed and transmitted. These mechanisms enable the brain to handle complex tasks by focusing on relevant variables and adjusting to changing contexts. The PFC, with its diverse and multifunctional neurons, exemplifies the importance of mixed selectivity in flexible behavior. Neurons in the PFC can respond to multiple variables, allowing for the integration of diverse inputs and the execution of complex tasks. This flexibility is crucial for decision-making and adapting to new situations. Mixed selectivity also plays a role in various brain regions, including the hippocampus and sensory systems, where it contributes to memory formation, sensory processing, and context-dependent behavior. The combination of oscillatory dynamics and neuromodulatory signals further enhances the brain's ability to process information efficiently and adapt to different tasks. In summary, mixed selectivity is a fundamental mechanism that enables the brain to handle complex and flexible tasks by integrating diverse inputs and dynamically adjusting to changing contexts. This computational strategy is essential for cognitive flexibility and adaptive behavior.