Biology subjects

Vergara, H. M.

Publications and source records attributed to Vergara, H. M..

4 recordsLinked to original sources

bioRxiv2026

The Training Village: an open platform for continuous testing of rodents in cognitive tasks

The development of new methods for dissecting neural circuits in rodents promises to transform the study of higher-order cognitive functions. However, the adaptation of complex cognitive tasks from humans to rodents remains very limited as these tasks require long periods of animal training with significant researcher expertise and effort. Multi-box setups can mitigate the problem but remain labor-intensive and costly to maintain. Newly developed home cage training systems, while minimizing costs, are limited to specific conditions and tasks. Here, we present the Training Village (TV), an open-source, affordable, and fully automated system for continuous rodent training. In the TV, a group of rodents lives in enriched arenas while individually accessing an operant box at any time. The system provides personalized training, continuous monitoring of task performance and home cage activity, and real-time remote supervision. Its graphical interface and modular design make it user-friendly and easy to integrate with other behavioral systems running a wide range of cognitive paradigms. We validated the TV across multiple tasks and cohorts of mice and rats, demonstrating efficient operant box usage, stable long-term task engagement, and its potential to link home cage behavior with task performance. Overall, the TV is an easy-to-adopt platform that can greatly accelerate brain research by fully automating rodent training in complex cognitive tasks.

animal behavior and cognition↗

bioRxiv2022

Action prediction error: a value-free dopaminergic teaching signal that drives stable learning

Animals choice behavior is characterized by two main tendencies: taking actions that led to rewards and repeating past actions. Theory suggests these strategies may be reinforced by different types of dopaminergic teaching signals: reward prediction error (RPE) to reinforce value-based associations and movement-based action prediction errors to reinforce value-free repetitive associations. Here we use an auditory-discrimination task in mice to show that movement-related dopamine activity in the tail of the striatum encodes the hypothesized action prediction error signal. Causal manipulations reveal that this prediction error serves as a value-free teaching signal that supports learning by reinforcing repeated associations. Computational modeling and experiments demonstrate that action prediction errors alone cannot support reward-guided learning but when paired with the RPE circuitry they serve to consolidate stable sound-action associations in a value-free manner. Together we show that there are two types of dopaminergic prediction errors that work in tandem to support learning.

neuroscience↗

bioRxiv2020

Whole-body integration of gene expression and single-cell morphology

Animal bodies are composed of hundreds of cell types that differ in location, morphology, cytoarchitecture, and physiology. This is reflected by cell type-specific transcription factors and downstream effector genes implementing functional specialisation. Here, we establish and explore the link between cell type-specific gene expression and subcellular morphology for the entire body of the marine annelid Platynereis dumerilii. For this, we registered a whole-body cellular expression atlas to a high-resolution electron microscopy dataset, automatically segmented all cell somata and nuclei, and clustered the cells according to gene expression or morphological parameters. We show that collective gene expression most efficiently identifies spatially coherent groups of cells that match anatomical boundaries, which indicates that combinations of regionally expressed transcription factors specify tissue identity. We provide an integrated browser as a Fiji plugin to readily explore, analyse and visualise multimodal datasets with remote on-demand access to all available datasets.

cell biology↗

bioRxiv2020

A standardized and reproducible method to measure decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We designed a task for head-fixed mice that combines established assays of perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be successfully reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path towards achieving reproducibility in neuroscience through collaborative open-science approaches.

neuroscience↗