Biology subjects

Qu, A. J.

Publications and source records attributed to Qu, A. J..

4 recordsLinked to original sources

bioRxiv2024

The adolescent frontal cortex shows stronger population-level encoding of information than the adult during a putative sensitive period

Adolescence is commonly claimed to be a sensitive period for brain development, but it is not clear how experience at this stage can have such strong impacts. We hypothesized that enhanced sensitivity to experience during adolescence may manifest as stronger encoding of task-related information in the dorsomedial prefrontal cortex (dmPFC). To enable optical access to task-related layer 2/3 neural activity in the developing mouse dmPFC, we imaged mice under a 2-photon microscope while they learned an auditory go/no-go task. We found adolescent mice (postnatal day P30-45) learned the task to criterion faster than adult mice (P60-75). When we compared neural activity in expert mice with comparable performance between the two age groups, we found that a similar fraction of single cells encoded task variables in the two groups. However, task information could be better decoded from the adolescent dmPFC population activity than the adult, even when we controlled for differences in head-fixed running. Adolescents also showed greater noise correlation than adults and shuffling to remove this noise correlation suggested noise correlation contributed to gain of function in adolescent compared to adult brain. We suggest a working model for adolescent brain function in which greater noise correlation supports greater capacity for distributed encoding of information driving, for better or for worse, increased sensitivity to experiences at this stage of life.

neuroscience↗

bioRxiv2024

Hidden state inference requires abstract contextual representations in ventral hippocampus

The ability to form and utilize subjective, latent contextual representations to influence decision making is a crucial determinant of everyday life. The hippocampus is widely hypothesized to bind together otherwise abstract combinations of stimuli to represent such latent contexts, and to allow their use to support the process of hidden state inference. Yet, direct evidence for this remains limited. Here we show that the CA1 area of the ventral hippocampus is necessary for mice to perform hidden state inference during a 2-armed bandit task. vCA1 neurons robustly differentiate between the two abstract contexts required for this strategy in a manner similar to the differentiation of spatial locations, despite the contexts being formed only from past probabilistic outcomes. These findings offer insight into how latent contextual information is used to optimize decision-making processes, and emphasize a key role of the hippocampus in hidden state inference.

neuroscience↗

bioRxiv2023

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison among a wide set of cognitive models based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation contributes to instrumental task performance in mice and is reflected in mesolimbic dopamine signaling.

animal behavior and cognition↗

bioRxiv2021

Diverse operant control of different motor cortex populations during learning

During motor learning, as well as during neuroprosthetic learning, animals learn to control motor cortex activity in order to generate behavior. Two different population of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations, it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a Calcium imaging based brain-machine interface (CaBMI) and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that animals learn to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that motor cortex is optimized to control the activity of pyramidal track neurons, embedded deep in cortex, and relaying motor commands outside of the telencephalon.

neuroscience↗