Biology subjects

Perez Rotondo, A.

Publications and source records attributed to Perez Rotondo, A..

4 recordsLinked to original sources

bioRxiv2026

Closed-loop imitation learning reveals muscle-centric and latent-goal codes in primate sensorimotor cortex

Dexterous grasping requires the seamless integration of proprioceptive feedback with predictive motor commands. Yet, how cortical circuits combine afferent feedback with efference copies to support skilled hand control remains poorly understood. Here we develop a closed-loop, muscle-level model of primate grasping that integrates biomechanics, imitation learning, and neural recordings. A neural network policy trained on a 39-muscle musculoskeletal hand reproduces naturalistic pre-contact shaping and develops internal states that quantitatively explain single-neuron activity in primary motor (M1) and somatosensory (S1) cortices. Three principles emerged. First, muscle-based controllers generate representations that align more closely with cortical dynamics than joint-based controllers, despite lower kinematic accuracy. Second, recurrent architectures with temporal memory, especially LSTMs, provide an inductive bias that enhances neural predictability. Third, model-to-brain alignment peaked at the layer integrating proprioceptive and goal signals. Finally, by decoding the models latent trajectory representation from M1, we demonstrated direct neural control of the policy: with activity from only tens of neurons, the brain-driven controller generated coherent grasp trajectories and showed markedly greater robustness to noise than joint-angle decoding. These findings reveal that S1 and M1 embed integrated, temporally structured, muscle-centric states and establish a stimulus-computable mechanistic framework for modeling sensorimotor control, while opening a novel route for creating brain-body models.

neuroscience↗

bioRxiv2025

Deep-learning models of the ascending proprioceptive pathway are subject to illusions

Proprioception is essential for perception and action. Like any other sense, proprioception is also subject to illusions. In this study, we model classic proprioceptive illusions in which tendon vibrations lead to biases in estimating the state of the body. We investigate these illusions with task-driven models that have been trained to infer the state of the body from distributed sensory muscle spindle inputs (primary and secondary afferents). Recent work has shown that such models exhibit representations similar to the neural code along the ascending proprioceptive pathway. Importantly, we did not train the models on illusion experiments and simulated muscle-tendon vibrations by considering their effect on primary afferents. Our results demonstrate that task-driven models are indeed susceptible to proprioceptive illusions, with the magnitude of the illusion depending on the vibration frequency. This work illustrates that primary afferents alone are sufficient to account for these classic illusions and provides a foundation for future theory-driven experiments.

neuroscience↗

bioRxiv2024

Modeling Sensorimotor Processing with Physics-Informed Neural Networks

Proprioception is essential for planning and executing precise movements. Muscle spindles, the key mechanoreceptors for proprioception, are the principle sensory neurons enabling this process. Emerging evidence suggests spindles act as adaptable processors, modulated by gamma motor neurons to meet task demands. Yet, the specifics of this modulation remain unknown. Here, we present a novel, physics-informed neural network model that integrates biomechanics and neural dynamics to capture spindle function with high fidelity and efficiency, while maintaining computational tractability. Through validation across multiple experimental datasets and species, our model not only outperforms existing approaches but also reveals key drivers of variability in spindle responses, offering new insights into proprioceptive mechanisms.

neuroscience↗

bioRxiv2022

How cerebellar architecture facilitates rapid online learning

SO_SCPLOWUMMARYC_SCPLOWThe cerebellum has a distinctive architecture in which inputs undergo a massive size expansion in dimensionality in early layers. Marr and Albuss classic codon theory and more recent extensions1-4 argue that this architecture facilitates learning via pattern separation. The essence of this idea is that sparsely active clusters -- codons-- of inputs are more easily separable in a higher dimensional representation. However, recent physiological data indicate that cerebellar activity is not sparse in the way anticipated by codon theory. Moreover, there is a conceptual gap between static pattern separation and the critical role of the cerebellum in dynamic tasks such as motor learning. We use mathematical analysis and simulations of cerebellar learning to identify specific difficulties inherent to online learning of dynamic tasks. We find that size expansions directly mitigate these difficulties, and that this benefit is maximised when granule cell activity is dense.

neuroscience↗