In a study published in Neuron, the researchers from Dr. XU Ninglong’s lab at the Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences, unraveled the computational mechanism of a specifically defined neural circuit in the cerebral cortex underlying the rule-based flexible decision-making behavior.
To survive and prosper, animals need to make dynamic decisions according to environmental changes. With the evolution of neocortex in mammalian brain, this capability is highly developed in mammals who not only learn simple stimulus-response associations, but also extract common structures from changing environmental states through latent learning to form internal models, or abstract knowledge. Such prior knowledge effectively guides inference of the hidden environmental states using incomplete information, and thereby facilitates highly adaptive intelligent behavior.
Studying the neural mechanism underlying such flexible decision-making behavior is the key to solving the mystery of biological basis of intelligence. Previous studies mainly identified that various individual brain regions, including different prefrontal subregions and sensorimotor regions, are related to or involved in behavioral switching upon task rule changes. But how these regions interact and coordinate with each other through complex neural circuits to implement computations underlying flexible and intelligent behaviors remains unknown.
Dr. XU’s lab combined novel quantitative behavioral methods and cutting-edge neural recording and manipulation technologies to investigate the neural circuit mechanisms underlying inference-based flexible decision-making. They trained laboratory mice to perform a flexible auditory categorization task, wherein the mice showed striking cognitive capability of using prior learned task knowledge to infer hidden task rules and make highly flexible sensorimotor decisions. In each trial, mice were required to categorize sound stimuli as high or low categories depending on the categorization boundary.
To test behavioral flexibility, they introduced two different categorization boundaries that changed in different blocks of trials without explicit cues. After training, mice were not only able to change their choice rapidly after boundary switch, but also able to use partial feedback information to infer the hidden task state and correctly classify unexperienced stimuli. This intelligent behavior recapitulated the cognitive function of using prior structural knowledge to infer hidden rules and make flexible and adaptive choices, as is often assessed in human subjects using the Wisconsin Card Sorting Test.
Then, the researchers constructed a reinforcement learning (RL) model incorporating task structural knowledge and state inference to delineate the underlying computational process. This model exhibited high degree of flexibility while using an efficient prediction error based updating mechanism. It accurately captured animals’ behavioral performance, and, importantly, quantified trial-by-trial the hidden cognitive variables associated with state inference.
They performed in vivo two-photo microscopy to image population neuronal activity in the brain during task performance to investigate the neural circuit computation underlying this intelligent behavior. They found that auditory cortex (ACx) neurons not only represent the sound stimulus information, but also encode a hidden cognitive variable, the estimated categorization boundary, critical for making categorical auditory decisions. This finding represents a rare discovery of how neurons in the brain represent hidden cognitive variables.
To study how neuronal circuits implement the computational process of updating category boundary estimate based on behavioral feedback, the researchers employed an all-optical recording and manipulation technology to image population activity in auditory cortex while manipulating the top-down projection from orbitofrontal cortex (OFC) to auditory cortex. They found that the top-down OFC-ACx circuit indeed supports the encoding of the hidden variable of auditory categorization boundary. This neural circuit mechanism is consistent with the computational process described by the task state-dependent RL model that the researchers constructed to recapitulate the behavioral results.
In addition, to study the causal role of the OFC-ACx circuit in flexible auditory categorization behavior, the researchers used chemogenetics to silence bilateral projections from orbitofrontal cortex in auditory cortex, and found that only the behavior flexibility but not the basic auditory discrimination was impaired, indicating that the OFC-ACx circuit indeed causally contribute to the flexibility of auditory categorization. This result corroborates the computational model that the orbitofrontal provided feedback information to update the boundary estimation in auditory cortex.
Using two-photon microscopy to directly image the activity of OFC-ACx axons to test this computational model, they found that the activity of these axons indeed encode the feedback information important for the boundary estimation updating, as predicted by the computational model.
This study employed comprehensive techniques to obtain measurements at neuronal, circuit- and behavioral levels, and acquired correlational, causal and computational evidence to support a new circuit mechanism in the neocortex implementing the computational process underlying a crucial cognitive function, the rule-based flexible decision-making utilizing task structural knowledge and state inference.
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