Biology subjects

Vilimelis Aceituno, P.

Publications and source records attributed to Vilimelis Aceituno, P..

3 recordsLinked to original sources

bioRxiv2025

Activation of latent programs for maternal behavior

Maternal behaviors in mammals, which are critical for offspring survival, involve complex interactions between innate neural circuits and behavioral flexibility. This flexibility depends on environmental needs and external stimuli. Recent studies suggest that social transmission plays a crucial role in the acquisition of maternal behaviors in alloparental (non-maternal) subjects, particularly highlighting visual observation for virgin female mice to acquire pup retrieval behavior. However, the extent to which these behaviors rely on social transmission versus the activation of pre-existing, latent behaviors remains unclear. Here, we systematically investigated the necessity of social observation for maternal behavior acquisition, using a social transmission pup retrieval paradigm in pup-naive virgin female mice. Following the same protocol as previous studies, we compared retrieval performance on conditions that either allowed or prevented the visual observation of maternal demonstrations. Contrary to previous findings reporting substantial differences between these conditions, we found that virgin females readily acquired pup retrieval behavior regardless of their visual access to experienced mothers. We observed no significant correlation between maternal and virgin performance after task exposure. Detailed behavioral analysis revealed that the primary difference between mothers and virgins lay on the latency to initiate the behavior, but once started, virgins complete the retrieval as fast as mothers. Together, our findings show that pup exposure is enough to activate latent behaviors, and suggest that pup retrieval might not be transmitted by visual observation.

animal behavior and cognition↗

bioRxiv2025

Inhibition from the oxytocin system tunes prefrontal circuit dynamics to promote social behavior

The prefrontal cortex (PFC) plays a central role in the selection and expression of diverse social behaviors. A balance of excitation and inhibition is necessary for normal social functioning, but it remains unclear how inhibition sculpts pyramidal activity to promote specific social behaviors. To address this question, we developed a novel decision-making task in which female mice chose between interaction with a male (sociosexual stimulus) or an appetitive non-social option (milk solution). To explore the role of a task-relevant inhibitory subpopulation, we targeted neurons in the medial PFC (mPFC) expressing oxytocin receptors (OXTR neurons). Combining optogenetic inhibition of OXTR neurons with population calcium imaging of pyramidal neural activity, we found that OXTR neurons normally promote interaction with a male compared to a non-social alternative. OXTR neurons also regulate pyramidal activity, which enables a specific ensemble to represent the male option during decision-making. Computational modeling reproduced these findings through a pyramidal competition mechanism, in which regulation by OXTR neurons allows a male-representing pyramidal ensemble to effectively compete against the remaining pyramidal population to drive male choice. These results provide a candidate mechanism by which inhibition enables mPFC pyramidal activity to select for specific social behaviors and may help explain why excitation/inhibition balance is so important for social functioning.

neuroscience↗

bioRxiv2022

Mini-batching ecological data to improve ecosystem models with machine learning

Process-based, dynamic models are essential for extrapolating beyond current trends and anticipating biodiversity responses to global change. However, their practical adoption for forecasting purposes remains limited due to difficulties in calibrating them against data and structural inaccuracies in their mathematical formulations. While ecological time series could, in principle, be used to directly estimate model parameters and refine model structures, the large noise levels in ecological datasets and the strong nonlinearity of ecological dynamics challenge conventional calibration methods. Here, we present a robust and scalable calibration framework that addresses these challenges by integrating techniques from scientific computing and deep learning. Our approach combines a segmentation strategy where state variables are estimated independently, differentiable programming for efficient gradient computation, parameter transformations to ensure the feasibility and stability of the model simulations, and mini-batching to accomodate large datasets. Through comprehensive benchmarks using simulated food web dynamics of increasing complexity, we demonstrate that the framework substantially improves the convergence of gradient descent algorithms and Monte Carlo sampling methods, accommodating for realistic scenarios with noisy and partial observations. This yields improved parameter estimation and forecasts within both mode estimation and full posterior distribution contexts. Crucially, we show that the calibration framework scales effectively with both the number of parameters and state variables. The improved convergence and scalability of the calibration framework enables hybrid modeling approaches, where neural networks parameterize complex processes within the dynamic model. In particular, we demonstrate that neural networks can effectively capture environmental dependencies in demographic rates and recover functional responses governing trophic interactions. Neural network-based parameterizations have the capability to improve the structural inaccuracies of models while maintaining ecological interpretability through post-hoc analysis of learned representations. We provide an implementation of the calibration framework and other key utilities as the open-source Julia package HybridDynamicModels.jl, with the hope that the package will facilitate the development of hybrid modeling approaches in quantitative ecology and related fields.

ecology↗