Beron, C. C. | bioRxiv Science

bioRxiv2022

Local and long-distance inputs dynamically regulate striatal acetylcholine during decision making

Within the basal ganglia, striatal dopamine (DA) and acetylcholine (Ach) are essential for the selection and reinforcement of motor actions and decision making. In vitro studies have revealed a circuit local to the striatum by which each of these two neurotransmitters directly regulates release of the other. Ach, released by a unique population of cholinergic interneurons (CINs), drives DA release via direct axonal depolarization. In turn, DA inhibits CIN activity via dopamine D2 receptors (D2R). Whether and how this circuit contributes to striatal function in vivo remains unknown. To define the in vivo role of this circuit, we monitored Ach and DA signals in the ventrolateral striatum of mice performing a reward-based decision-making task. We establish that DA and Ach exhibit multiphasic and anticorrelated transients that are modulated by decision history and reward outcome. However, CIN perturbations reveal that DA dynamics and reward-prediction error encoding do not require Ach release by CINs. On the other hand, CIN-specific deletion of D2Rs shows that DA inhibits Ach levels in a D2R-dependent manner, and loss of this regulation impairs decision-making. To determine how other inputs to striatum shape Ach signals, we assessed the contribution of projections from cortex and thalamus and found that glutamate release from both sources is required for Ach release. Altogether, we uncover a dynamic relationship between DA and Ach during decision making and reveal modes of CIN regulation by local DA signals and long-range cortical and thalamic inputs. These findings deepen our understanding of the neurochemical basis of decision making and behavior.

neuroscience↗

bioRxiv2021

Efficient and stochastic mouse action switching during probabilistic decision making

In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a "two-armed bandit" task, thirsty mice use information about recent action and action-outcome histories to choose between two ports that deliver water probabilistically. Here, we comprehensively modeled choice behavior in this task, including the trial-to-trial changes in port selection - i.e. action switching behavior. We find that mouse behavior is, at times, deterministic and, at others, apparently stochastic. The behavior deviates from that of a theoretically optimal agent performing Bayesian inference in a Hidden Markov Model (HMM). We formulate a set of models based on logistic regression, reinforcement learning, and sticky Bayesian inference that we demonstrate are mathematically equivalent and that accurately describe mouse behavior. The switching behavior of mice in the task is captured in each model by a stochastic action policy, a history-dependent representation of action value, and a tendency to repeat actions despite incoming evidence. The models parsimoniously capture behavior across different environmental conditionals by varying the stickiness parameter, and, like the mice, they achieve nearly maximal reward rates. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by a set of equivalent models with a small number of relatively fixed parameters. SignificanceTo obtain rewards in changing and uncertain environments, animals must adapt their behavior. We found that mouse choice and trial-to-trial switching behavior in a dynamic and probabilistic two-choice task could be modeled by equivalent theoretical, algorithmic, and descriptive models. These models capture components of evidence accumulation, choice history bias, and stochasticity in mouse behavior. Furthermore, they reveal that mice adapt their behavior in different environmental contexts by modulating their level of stickiness to their previous choice. Despite deviating from the behavior of a theoretically ideal observer, the empirical models achieve comparable levels of near-maximal reward. These results make predictions to guide interrogation of the neural mechanisms underlying flexible decision-making strategies.

neuroscience↗