Distinct value computations support rapid sequential decisions.

The value of the environment determines animals' motivational states and sets expectations for error-based learning1-3. How are values computed? Reinforcement learning systems can store or cache values of states or actions that are learned from experience, or they can compute values using a model of the environment to simulate possible futures3. These value computations have distinct trade-offs, and a central question is how neural systems decide which computations to use or whether/how to combine them4-8. Here we show that rats use distinct value computations for sequential decisions within single trials. We used high-throughput training to collect statistically powerful datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they initiated trials and how long they waited for rewards across states, balancing effort and time costs against expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value computations interact on rapid timescales, and demonstrate the power of using high-throughput training to understand rich, cognitive behaviors.

Distinct value computations support rapid sequential decisions.

The value of the environment determines animals' motivational states and sets expectations for error-based learning1-3. How are values computed? Reinforcement learning systems can store or "cache" values of states or actions that are learned from experience, or they can compute values using a model of the environment to simulate possible futures3. These value computations have distinct trade-offs, and a central question is how neural systems decide which computations to use or whether/how to combine them4-8. Here we show that rats use distinct value computations for sequential decisions within single trials. We used high-throughput training to collect statistically powerful datasets from 291 rats performing a temporal wagering task with hidden reward states. Rats adjusted how quickly they initiated trials and how long they waited for rewards across states, balancing effort and time costs against expected rewards. Statistical modeling revealed that animals computed the value of the environment differently when initiating trials versus when deciding how long to wait for rewards, even though these decisions were only seconds apart. Moreover, value estimates interacted via a dynamic learning rate. Our results reveal how distinct value computations interact on rapid timescales, and demonstrate the power of using high-throughput training to understand rich, cognitive behaviors.

Tracking self-performance in the prefrontal cortex: It's layered.

In this issue of Cell, Spellman and colleagues record and manipulate the activity of neurons in the medial prefrontal cortex of mice performing a task in which they must pay attention to different stimuli. They show that this brain region is important for monitoring the animals' performance, and neurons that appear to contribute to behavior reside in deep cortical layers.

Regulation of cortical activity and arousal by the matrix cells of the ventromedial thalamic nucleus.

The "non-specific" ventromedial thalamic nucleus (VM) has long been considered a candidate for mediating cortical arousal due to its diffuse, superficial projections, but direct evidence was lacking. Here, we show in mice that the activity of VM calbindin1-positive matrix cells is high in wake and REM sleep and low in NREM sleep, and increases before cortical activity at the sleep-to-wake transition. Optogenetic stimulation of VM cells rapidly awoke all mice from NREM sleep and consistently caused EEG activation during slow wave anesthesia, while arousal did not occur from REM sleep. Conversely, chemogenetic inhibition of VM decreased wake duration. Optogenetic activation of the "specific" ventral posteromedial nucleus (VPM) did not cause arousal from either NREM or REM sleep. Thus, matrix cells in VM produce arousal and broad cortical activation during NREM sleep and slow wave anesthesia in a way that accounts for the effects classically attributed to "non-specific" thalamic nuclei.