Biology subjects

Aoi, M. C.

Publications and source records attributed to Aoi, M. C..

7 recordsLinked to original sources

bioRxiv2026

Discovering compositional, interpretable structure in social behavior

Quantitative assessment of behavior is vital for rigorous analysis of how animals interact in the world and how behavior is generated from brain activity. Recent advances in automated pose detection have made possible data-driven discovery of movement action sequences. While many algorithms have been developed for this purpose, their categorical nature, i.e., the assumption that behavior is one action at a time, prohibits expressive quantification of social behavior. Such behavior is combinatorial and compositional, with multiple animals performing actions that may be either independent or coordinated over time. Here we show how developments in decomposed linear dynamical systems (dLDS) can instead represent action-generating dynamics as a signed, graded mixture of shared linear operators acting directly on keypoint positions. We demonstrate our approach across both single-mouse and two-mouse datasets, comparing against a state-of-the-art switching model, keypoint-MoSeq. In single-mouse data, we find that a composition of three operators can capture the full repertoire of 21 keypoint-MoSeq syllables, showing that even for a single animal, the action space is more parsimoniously described by a continuous subspace than clustered motifs. In a two-mouse resident--intruder assay, we identify, without behavioral labels, separate primitives for self-action, inter-animal-geometry, and social-action. The latter couples an animal's action to its partner's relative bearing, enabling the action to incorporate the social geometry in which it occurs. dLDS further decodes human-labeled social behaviors (i.e., "attack", "investigation", "mount", and "other") more accurately than behavior-selective keypoint-MoSeq syllable sets using fewer units. Together, these results demonstrate that compositional, time-varying linear dynamics can provide a path towards flexible and interpretable modeling of action-level behavior for both individual and social behavior.

animal behavior and cognition↗

bioRxiv2026

Changes in perceptual sampling contribute to representational drift

Gradual changes in neural response patterns to the same stimulus over time, termed representational drift, have been widely observed across cortical areas. Drift is typically attributed to intrinsic neural dynamics driven by synaptic plasticity or turnover. Here we test a complementary hypothesis that drift arises due to systematic changes in behavior such as shifts in attention or gaze. We conducted a longitudinal eye-tracking experiment in which fourteen adults freely viewed naturalistic images across 6 experimental sessions spanning 2-4 weeks, with a subset of images repeated within and across sessions. For each participant, we quantified the similarity of fixation density maps between session pairs using the Wasserstein distance. Fixation patterns became increasingly dissimilar with greater temporal separation between sessions, indicating systematic and directional drift in gaze behavior. To assess whether these behavioral changes could plausibly induce changes in neural representations, we passed fixation-masked images through CORnet-S, a hierarchical deep neural network model of the primate ventral visual stream. Representational distances, quantified as the Frobenius norm of pairwise activation differences, increased with the number of sessions that separated the fixation maps across all four model layers (V1, V2, V4, IT). A kernel-based maximum mean discrepancy test further confirmed that the empirical distribution of representational distances differed significantly from shuffled controls. These findings suggest that small but systematic shifts in the sampling of a visual scene over time are sufficient to cause representational drift in visual cortex. More generally, these results suggest that subtle changes in behavior over time are inevitable, even in simple tasks, and that these changes may be sufficient to drive representational drift in the absence of intrinsic reconfigurations of neural codes. Author summaryIn studies of representational drift in visual cortex, shifts in attention and gaze across repeated stimulus exposures can produce drift-like neural patterns. While the current focus is on contributions of changes in visual sampling to drift, the results raise the possibility that other subtle changes in behavior, many that are hard to quantify, could drive drift in other domains.

neuroscience↗

bioRxiv2024

Representing the dynamics of natural marmoset vocal behaviors in frontal cortex

Here we tested the respective contributions of primate premotor and prefrontal cortex to support vocal behavior. We applied a model-based GLM analysis that better accounts for the inherent variance in natural, continuous behaviors to characterize the activity of neurons throughout frontal cortex as freely-moving marmosets engaged in conversational exchanges. While analyses revealed functional clusters of neural activity related to the different processes involved in the vocal behavior, these clusters did not map to subfields of prefrontal or premotor cortex, as has been observed in more conventional task-based paradigms. Our results suggest a distributed functional organization for the myriad neural mechanisms underlying natural social interactions and has implications for our concepts of the role that frontal cortex plays in governing ethological behaviors in primates.

neuroscience↗

bioRxiv2022

A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making

The ability to flexibly switch our response to external stimuli according to contextual information is critical for successful interactions with a complex world. Context-dependent computations are necessary across many domains1-3, yet their neural implementations remain poorly understood. Here we developed a novel behavioral task in rats to study context-dependent selection and accumulation of evidence for decision-making4-6. Under assumptions supported by both monkey and rat data, we first show mathematically that a network can solve this problem through a combination of three defined components. These components can be identified and tested directly with experimental data. We further show that existing electrophysiological and modeling data are compatible with the full variety of possible combinations of these components, suggesting that different individuals could use different component combinations. To study variability across individual subjects, we developed automated, high-throughput methods to train rats on our task, and we trained many subjects on it. Consistent with theoretical predictions, neural and behavioral analyses revealed substantial heterogeneity across rats, despite uniformly good task performance. Our theory further predicts a specific link between behavioral and neural signatures, which was robustly supported in the data. In summary, our results provide a new experimentally-supported theoretical framework to analyze individual variability in biological and artificial systems performing flexible decision-making tasks, they open the door to cellular-resolution studies of individual variability in higher cognition, and they provide insights into neural mechanisms of context-dependent computation more generally.

neuroscience↗

bioRxiv2020

Recurrent dynamics of prefrontal cortex during context-dependent decision-making

A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animals performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

neuroscience↗

bioRxiv2020

Identifying signal and noise structure in neuralpopulation activity with Gaussian process factormodels

Neural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis of such datasets is to characterize systematic aspects of neural activity that carry information about the repeated stimulus or behavior of interest, which can be considered "signal", and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considered "noise". Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data. However, they have not yet been adapted to the problem of characterizing signal and noise in multi-trial datasets. Here we address this shortcoming by proposing "signal-noise" Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity. To learn the parameters of our model, we introduce a Fourier-domain black box variational inference method that quickly identifies smooth latent structure. The resulting model reliably uncovers latent signal and trial-to-trial noise-related fluctuations in large-scale recordings. We use this model to show that predominantly, noise fluctuations perturb neural activity within a subspace orthogonal to signal activity, suggesting that trial-by-trial noise does not interfere with signal representations. Finally, we extend the model to capture statistical dependencies across brain regions in multi-region data. We show that in mouse visual cortex, models with shared noise across brain regions out-perform models with independent per-region noise.

neuroscience↗

bioRxiv2019

Prefrontal cortex exhibits multi-dimensional dynamic encoding during decision-making

Recent work has suggested that prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. Here we investigate population-level coding of decision variables in monkey PFC using a new method for identifying task-relevant dimensions of neural activity. Our analyses reveal that, in contrast to one-dimensional attractor models, PFC has a multi-dimensional code for decisions, context, and relevant as well as irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases, and show that they are not the product of distinct populations, but of a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a sustained plateau in decoding accuracy, revealing that rotational dynamics in PFC preserve sensory as well as choice information for the duration of the stimulus integration period.

neuroscience↗