Biology subjects

Wu, H. Z.

Publications and source records attributed to Wu, H. Z..

3 recordsLinked to original sources

bioRxiv2025

ALM enables contextual decision-making via dynamic reconfiguration of local circuits

Cognitive operations often require flexible implementation of stimulus-response contingencies, depending on context. We developed an olfactory task in which mice learned to associate a test odor with a directional lick response, conditional on a preceding context odor drawn from a different odor set. Two-photon imaging revealed that anterior lateral motor cortex (ALM) contains distinct populations encoding context, test odors, and choice. Optogenetic silencing during the context and delay periods impaired performance, suggesting that ALM contributes to configuring the appropriate contingency. Although context odors that instructed the same mapping were represented by separate populations, their influence converged at the level of choice-selective neurons. A subpopulation of these neurons exhibited dual selectivity for context and choice, forming what we term "contingency neurons." These findings suggest that ALM supports flexible behavior not by abstracting over context cues, but by dynamically reconfiguring local circuits to route sensory input to the appropriate motor output.

neuroscience↗

bioRxiv2025

Asymmetric Social Representations in the Prefrontal Cortex for Cooperative Behavior

Cooperation is a hallmark of social species, enabling individuals to achieve goals that are unattainable alone. Across species, cooperative behaviors are often organized by distinct social roles such as leaders and followers, yet the neural mechanisms supporting such role-based coordination remain elusive. Here we introduce a new paradigm for studying cooperation in mice, where pairs of animals engage in a joint spatial foraging task that naturally gives rise to stable leader-follower roles predictive of learning speed. Disruption of medial prefrontal cortex (mPFC) activity, particularly in followers, impairs cooperation and induces reciprocal shifts in how animals weigh self- and partner-related cues for decision-making. Calcium imaging reveals that mPFC encodes both leadership dynamics and an egocentric social value map of the partners position, each in an asymmetric, role-specific manner. Combining this behavior with a novel multi-agent inverse reinforcement learning framework, we identify latent value functions that guide cooperative decisions and are decodable from mPFC activity. These findings uncover fundamental neural computations that support cooperation, revealing how social roles shape decision-making in real time. Our work opens new avenues for investigating the cellular and circuit basis of social cognition and collective behavior.

neuroscience↗

bioRxiv2024

Unveiling the latent dynamics in social cognition with multi-agent inverse reinforcement learning

Social behavior requires individuals to consider not only their own goals but also those of others. Latent value functions that encode such goals can be recovered from behavior using inverse reinforcement learning in single-agent settings. However, extending it to multi-agent interactions is challenging, because value functions are defined over joint state spaces that grow exponentially with the number of agents. Existing approaches often manage this complexity by imposing strong structural assumptions about social interactions, thereby limiting their applicability and interpretability. Here we show that joint value functions governing social interactions can be effectively represented through value decomposition into individual value maps for each agent and low-dimensional interaction terms. We develop a multi-agent inverse reinforcement learning framework (MAIRL) to infer these representations from behavior. In mouse and primate social tasks, MAIRL reveals interpretable value maps that are conditioned on the distinct social roles animals play during group behavior. Together, these results establish MAIRL as an interpretable and scalable framework for identifying latent value representations guiding multi-agent behavior across species.

animal behavior and cognition↗