Biology subjects

Gosselin, B.

Publications and source records attributed to Gosselin, B..

4 recordsLinked to original sources

bioRxiv2026

Spinotrode: long-term intraspinal electrophysiological recordings to unravel dorsal horn neuron dynamics in behaving mice

Recording single-unit neural activity in the spinal cord in freely moving rodents is crucial for understanding spinal network dynamics but remains challenging due to specific biomechanical constraints. So far, the vast majority of studies have been conducted in anesthetized or restrained animals, limiting the correlation of neuronal activity with naturalistic behaviours. Here we introduce the Spinotrode, a vertebral implant able to stably record signals (over several weeks) at the single-cell level in the bilateral dorsal horns of adult mice. It is engineered to minimize postural constraints and interrogate spinal activity during sensory stimulation and motor behaviours. No functional impairment or tissue damage was apparent. Spinotrode recordings allowed identification of distinct functional types of neurons associated with paw withdrawal, revealed dorsal horn activity during locomotor behaviour distinct from proprioception and touch, and uncovered contralateral sensory activation upon nociceptive reflex responses, which forces reassessment of data obtained in anesthetized animals.

neuroscience↗

bioRxiv2026

A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics

Bidirectional interfaces combined with neural de-coding algorithms are essential for closed-loop (CL) neuromodulation, enabling simultaneous neural monitoring and responsive optogenetic stimulation. However, implementing these capabilities in compact wireless headstages for freely moving animals remains challenging, as most existing platforms rely on tethered setups and external processors to execute computationally intensive decoders. This work presents the design and optimization of a neural decoder integrated into a bidirectional wireless system for CL optogenetic experiments in rodents. The proposed platform combines 32-channel electrophysiological recording with neuromorphic feature extraction, dimensionality reduction, and a nonlinear support vector machine (NL-SVM) decoder implemented on a resource-constrained Spartan-6 FPGA. Temporal dynamics are captured using spike-count features and leaky integrators, while principal component analysis (PCA) reduces the feature space to six components, enabling sub-millisecond inference with minimal memory and power requirements. Model size is further reduced using k-means clustering during training to limit the number of support vectors. Decoder performance was validated using datasets from non-human primate and rat motor cortex recordings. The proposed decoder achieved accuracy comparable to convolutional neural networks (R2 =0.85 vs. 0.87) and outperformed Wiener filters (R2 = 0.81) while requiring significantly fewer computational resources. The full system was further demonstrated in vivo through wireless closed-loop optogenetic stimulation in rats, achieving a variance accounted for (VAF) of 0.9148. Overall, this work introduces a versatile, fully self-contained, and resource-efficient platform for real-time untethered closed-loop neuroscience experiments.

neuroscience↗

bioRxiv2024

PrecisionTrack: Reliable Tracking of Large Groups of Animals Interacting in Complex Environments Over Extended Periods

Large-scale ethological behavioral studies can provide insights into the neuronal processes underlying complex and social behaviors, potentially opening new avenues for mental health research. However, studying socially interacting animals in naturalistic environments remains technically challenging, as current approaches struggle to simultaneously maintain subject identity, extract behavior, and characterize social interactions over prolonged periods. Here, we present PrecisionTrack, an open-source and fully integrated framework designed for real-time multi-animal tracking, behavioral analysis, and social interaction inference in large groups of interacting animals. PrecisionTrack achieves high spatiotemporal accuracy in crowded and highly occlusive environments while maintaining robust long-term identity tracking and low-latency processing. To extend behavioral inference beyond pose estimation, we developed the Multi-animal Action Recognition Transformer (MART), a transformer-based architecture enabling real-time subject-level action recognition, and Graph-MART (G-MART), a graph neural network module that infers directed social interactions and interaction partners within groups. In addition, PrecisionTrack supports quantitative analysis of evolving social networks across time, enabling investigation of the temporal organization and stability of social dynamics in naturalistic settings. The entire framework is open source and accompanied by standardized workflows and documentation, enabling users to train, evaluate, and deploy custom behavioral analysis pipelines across species and experimental contexts. PrecisionTrack provides a scalable platform for quantitative investigation of complex social behaviors at a resolution and duration not accessible with existing methods.

neuroscience↗

bioRxiv2024

The Tailtag System: Tracking Multiple Mice in a Complex Environment Over a Prolonged Period Using ArUco Markers.

Despite recent advancements, safely and reliably tracking individual movements over extended periods, particularly within complex social groups, remains challenging. Traditional methods like colour coding, tagging, and RFID tracking, while effective, have notable practical limitations. State-of-the-art neural network-based trackers often struggle to maintain individual identities in large groups for more than a few seconds. Fiducial tags like ArUco codes present a potential solution by enabling accurate tracking and identity management, yet their topical application on mammals has proven difficult without frequent human intervention. In this study, we introduce the Tailtag system: a non-invasive, ergonomic tail ring embedded with an ArUco marker. This system includes a comprehensive parameter optimization guide along with practical guidelines on marker selection. Our Tailtag system demonstrated the ability to automatically and reliably track individual mice in social colonies of up to 20 individuals over a period of seven days without performance degradation, facilitating a detailed analysis of social dynamics in naturalized environments.

animal behavior and cognition↗