Biology subjects

Yang, M. A.

Publications and source records attributed to Yang, M. A..

3 recordsLinked to original sources

bioRxiv2025

Pallidal prototypic neuron and astrocyte activities regulate flexible reward-seeking behaviors

Behavioral flexibility allows animals to adjust actions to changing environments. While the basal ganglia are critical for adaptation, the specific role of the external globus pallidus (GPe) is unclear. This study examined the contributions of two major GPe cell types--prototypic neurons projecting to the subthalamic nucleus (ProtoGPe[->]STN neurons) and astrocytes--to behavioral flexibility. Using longitudinal operant conditioning with context reversals, we found that ProtoGPe[->]STN neurons dynamically represent contextual information correlating with behavioral optimality. In contrast, GPe astrocytes exhibited gradual contextual encoding independent of performance. Deleting ProtoGPe[->]STN neurons impaired adaptive responses to changing action-outcome contingencies without altering initial reward-seeking acquisition, highlighting their specific role in enabling behavioral flexibility. Furthermore, we discovered that ProtoGPe[->]STN neurons integrate inhibitory striatal and excitatory subthalamic inputs, modulating downstream basal ganglia circuits to support flexible behavior. This research elucidates the complementary roles of ProtoGPe[->]STN neurons and astrocytes in cellular mechanisms of flexible reward-seeking behavior.

neuroscience↗

bioRxiv2020

Biological Reinforcement Learning via Predictive Spacetime Encoding

Recent advances in reinforcement learning (RL) have successfully addressed several challenges, such as performance, scalability, or sample efficiency associated with the use of this technology. Although RL algorithms bear relevance to psychology and neuroscience in a broader context, they lack biological plausibility. Motivated by recent neural findings demonstrating the capacity of the hippocampus and prefrontal cortex to gather space and time information from the environment, this study presents a novel RL model, called spacetime Q-Network (STQN), that exploits predictive spatiotemporal encoding to reliably learn highly uncertain environment. The proposed method consists of two primary components. The first component is the successor representation with theta phase precession implements hippocampal spacetime encoding, acting as a rollout prediction. The second component, called Q switch ensemble, implements prefrontal population coding for reliable reward prediction. We also implement a single learning rule to accommodate both hippocampal-prefrontal replay and synaptic homeostasis, which subserves confidence-based metacognitive learning. To demonstrate the capacity of our model, we design a task array simulating various levels of environmental uncertainty and complexity. Results show that our model significantly outperforms a few state-of-the-art RL models. In the subsequent ablation study, we showed unique contributions of each component to resolving task uncertainty and complexity. Our study has two important implications. First, it provides the theoretical groundwork for closely linking unique characteristics of the distinct brain regions in the context of RL. Second, our implementation is performed in a simple matrix form that accommodates expansion into biologically-plausible, highly-scalable, and generalizable neural architectures.

neuroscience↗

bioRxiv2019

Genomic approaches reveal an endemic sub-population of gray wolves in Southern China

Despite being one of the most widely distributed terrestrial mammals, the history of gray wolves (Canis lupus) in China is not well understood as their habitats have been destroyed with growing economic development. Using six specimens from wolf skins in Chinese Natural History museums, we sequenced their genome using a modified ancient DNA procedure. Using whole genome sequence analysis, we showed that gray wolves from Southern China (SC) derive from a single lineage, distinct from gray wolves from the Tibetan Plateau (Canis lupus chanco) and Northern China, suggesting that SC gray wolves may form a distinct sub-population. Of SC gray wolves, one wolf from Zhejiang carries a genetic component from a canid that must have diverged earlier from other wolves than jackals did, perhaps through gene flow from a population related to or further diverged from wolves than the dhole, a species distributed in Southern China and Southeast Asia. This may indicate that interspecific gene flow likely played an important role in shaping the speciation patterns and population structure in the genus Canis. Our study is the first to survey museum genomes of gray wolves from Southern China, revealing the presence of an endemic population with ancient interspecific gene flow from a population related to the dhole, and highlighting how sequencing the paleogenome from museum specimens can help us to study extinct species.

evolutionary biology↗