Biology subjects

Duan, C. A.

Publications and source records attributed to Duan, C. A..

8 recordsLinked to original sources

bioRxiv2026

Behavioural and neural mechanisms for stochastic choices in mixed strategy games

Adaptive behaviour usually requires exploiting regularities in the environment, but in competitive settings the opposite can be true: predictable choice patterns can be exploited by others, making unpredictability itself advantageous. How neural circuits generate such strategic variability remains poorly understood. Here, we trained mice to play a zero-sum game against an opponent that exploited statistical regularities in their choices and rewards, and tracked their behaviour and dorsal cortical dynamics across learning. Using a hidden Markov model, we found that mice transitioned from structured, predictable strategies towards a near-optimal stochastic strategy as they learned. Applying the same framework to monkeys playing the same game identified a shared stochastic strategy across species, despite differences in how it was deployed. Cortex-wide imaging revealed that stochastic choices were associated with reduced representation of reward history, while immediate reward signals remained robust. Critically, while the strength of cortical reward signals predicted subsequent choice during reward-guided behaviour, this relationship was abolished during stochastic behaviour. Thus, adaptive stochasticity does not simply arise from a loss of reward information, but from selectively decoupling reward from future choice. These results reveal a neural mechanism through which animals suppress otherwise useful reward-guided structure to generate adaptive unpredictability in competitive environments.

neuroscience↗

bioRxiv2025

A frontal motor circuit for economic decisions and actions

Flexible behaviour requires transforming abstract cognitive representations, such as value preferences, into concrete motor actions. During economic decision-making, individuals evaluate options to guide choices and then transform these choices into specific actions to obtain rewards. Understanding how neural circuits convert these abstract economic decisions into spatial actions remains challenging because decision formation and motor planning are typically intertwined. Here we introduce a mouse task that temporally dissociates value-guided decisions from spatial action planning, and show that a frontal motor network implements the transformation across decision stages through dynamic circuit reconfiguration. Using cortex-wide imaging and optogenetic perturbations, we identified a frontal motor circuit that was causally required for both abstract and motor stages of choice. During the abstract decision stage, neurons in this circuit encoded option values and economic choices independently of sensorimotor contingencies, and unilateral silencing impaired decisions without spatial bias. In contrast, during spatial planning, value and spatial signals were non-linearly integrated to guide action selection, and unilateral silencing produced an ipsilateral bias. A dynamical model captured this transformation, predicting a mode switch from cooperative interhemispheric maintenance of economic choice to competitive, lateralized control of actions, which we validated with simultaneous bilateral recordings. These findings demonstrate how frontal motor circuits reconfigure their interactions to bridge abstract cognition and concrete actions, providing a circuit-level mechanism for flexible, value-guided behaviour.

neuroscience↗

bioRxiv2025

Mice dynamically adapt to opponents in competitive multi-player games

Competing for resources in dynamic social environments is fundamental for survival and requires continuous monitoring of both self and others to guide effective choices. Yet most of our understanding of value-based decision-making comes from studying individuals in isolation, leaving open key questions about how animals adapt their strategies during social competition. Here, we developed an ethologically relevant multi-player game in which freely moving mice made valuebased decisions in a competitive spatial foraging task. We found that mice integrated real-time spatial information about both themselves and their opponents to flexibly shift their preferences toward safer, low-payout options when appropriate. Analyses of mice behaviour and reinforcement learning agents revealed that these adaptations could not be explained by simple reward learning, but were instead consistent with optimal decision strategies guided by opponent features. Using a dynamical model of decision-making, we found that competition increased choice variability and sensitivity to initial conditions, generating testable predictions for future neural recordings and perturbations. Together, our findings reveal a fundamental mechanism for flexible competitive foraging and propose novel frameworks for quantitatively understanding value-based decision-making in fast-changing social environments.

neuroscience↗

bioRxiv2024

Zapit: Open Source Random-Access Photostimulation For Neuroscience

Optogenetic tools are indispensable for understanding the causal neural mechanisms underlying animal behavior,providing millisecond-precision control over genetically defined neural populations. Random-access laser-scanning optogenetics is a powerful and underutilized method for transiently manipulating cortical activity in mice. Despite the utility of this technique, no general-purpose open-source implementation currently exists, and potential users need expertise across optics, real-time hardware control, and programming. This represents a major barrier to adoption, particularly for newly established research groups. Here we present 'Zapit', the first open-source general-purpose platform for random-access laser-scanning optogenetic experiments in head-fixed mice. Zapit is fully documented, has a user-friendly GUI, works in stereotaxic coordinates, and comes with easy to build hardware options that extend functionality beyond any published system. We validate Zapit's performance through electrophysiological recordings and cortical photoinhibition in behaving mice. Zapit is a novel and innovative tool with the potential to democratize laser-scanning optogenetics and significantly increase uptake throughout the scientific community.

neuroscience↗

bioRxiv2019

Interrogating theoretical models of neural computation with deep inference

1A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon - whether behavioral or a pattern of neural activity - and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example inferring conductance parameters in a circuit model of the stomatogastric ganglion. Then, with recurrent neural networks of increasing size, we show that EPI allows precise control over the behavior of inferred parameters, and that EPI scales better in parameter dimension than alternative techniques. In the remainder of this work, we present novel theoretical findings gained through the examination of complex parametric structure captured by EPI. In a model of primary visual cortex, we discovered how connectivity with multiple inhibitory subtypes shapes variability in the excitatory population. Finally, in a model of superior colliculus, we identified and characterized two distinct regimes of connectivity that facilitate switching between opposite tasks amidst interleaved trials, characterized each regime via insights afforded by EPI, and found conditions where these circuit models reproduce results from optogenetic silencing experiments. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

neuroscience↗

bioRxiv2019

A cortico-collicular pathway for motor planning in a memory-dependent perceptual decision task

Survival in a dynamic environment requires animals to plan future actions based on past sensory evidence. However, the neural circuit mechanism underlying this crucial brain function, referred to as motor planning, remains unclear. Here, we employ projection-specific imaging and perturbation methods to investigate the direct pathway linking two key nodes in the motor planning network, the secondary motor cortex (M2) and the midbrain superior colliculus (SC), in mice performing a memory-dependent perceptual decision task. We find dynamic coding of choice information in SC-projecting M2 neurons during motor planning and execution, and disruption of this information by inhibiting M2 terminals in SC selectively impaired decision maintenance. Furthermore, cell-type-specific optogenetic circuit mapping shows that M2 terminals modulate both excitatory and inhibitory SC neurons with balanced synaptic strength. Together, our results reveal the dynamic recruitment of the premotor-collicular pathway as a circuit mechanism for motor planning.

neuroscience↗

bioRxiv2018

Dynamic and causal contribution of parietal cortex to perceptual decisions during category learning

Making perceptual decisions to categorize unknown sensory stimuli is a fundamental cognitive function, known as category learning. The posterior parietal cortex (PPC), although has been intensively studied for its role in decision-making and other cognitive functions, its causal link with behavior remains controversial. Here we combine in vivo two-photon imaging, circuit manipulation and auditory psychophysics behavior in mice to probe the role of PPC and its connectivity with sensory cortex in decision-making during category learning. We show that PPC neuronal populations exhibit coding dynamics characteristic of category learning, showing representations for both new sensory stimuli and prior learned categories. Circuit-specific perturbations of PPC and its projections to auditory cortex impaired decision performance specifically for categorizing new auditory stimuli. These data reveal a dynamic and causal role of the parietal-auditory circuit in decision-making, integrating prior knowledge to guide categorical decisions on new sensory stimuli.

neuroscience↗

bioRxiv2018

Collicular circuits for flexible sensorimotor routing

Historically, cognitive processing has been thought to rely on cortical areas such as prefrontal cortex (PFC), with the outputs of these areas modulating activity in lower, putatively simpler spatiomotor regions, such as the midbrain superior colliculus (SC). Using a rat task in which subjects switch rapidly between task contexts that demand changes in sensorimotor mappings, we report a surprising role for the SC in non-spatial cognitive processes. Before spatial response choices could be formed, neurons in SC encoded task context more strongly than neurons in PFC, and bilateral SC silencing impaired behavioral performance. Once spatial choices could begin to be formed, SC neurons encoded the choice faster than PFC, while bilateral SC silencing no longer impaired choices. A set of dynamical models of the SC replicates our findings. Our results challenge cortically-focused views of cognition, and suggest that ostensibly spatiomotor structures can play central roles in non-spatiomotor cognitive processes.

neuroscience↗