Biology subjects

Coraggioso, M.

Publications and source records attributed to Coraggioso, M..

3 recordsLinked to original sources

bioRxiv2025

A sensorimotor instability drives a locomotor transition during fish development

Animals rely on movement to survive -- to explore their environment, find food and mates and avoid danger. During development, changes in body shape, muscle strength and physiological needs drive the continuous adjustment of locomotor patterns. How these changes are orchestrated in a flexible and adaptive manner remains unknown. We explore this question in Danionella cerebrum, a miniature freshwater fish that is emerging as an important vertebrate model in systems neuroscience. We identify a clear transition in locomotion, from continuous to burst-and-coast swimming occurring around 3 weeks of age. We demonstrate that this transition is an energy saving strategy, and that it reflects an insta-bility in the sensorimotor process governing speed regulation. Rather than a preprogrammed developmental switch, it is therefore directly tied to the animal swimming strength. We confirmed this finding by manipulating sensory feedback in order to induce a similar transition at fixed developmental stages. Together, our results illustrate a dynamic interplay between body, brain, and environment during development, offering new insights into the principles governing adaptive locomotion.

biophysics↗

bioRxiv2025

Logarithmic coding leads to adaptive stabilization in the presence of sensorimotor delays

Animals respond to sensory stimuli with motor actions, which in turn generate new sensory inputs. This sensorimotor loop is constrained by time delays that impose a trade-off between responsiveness and stability. Additionally, as the relationship between a motor command and the corresponding sensory feedback is context-dependent, the response must be adapted in real time. It is generally believed that this adaptation process relies on an internal model that is continuously updated through prediction error minimization. Here, we experimentally reveal an alternative strategy based on a simpler feedback mechanism that does not require any internal model. We developed a virtual reality system for the miniature transparent fish Danionella cerebrum that enables in vivo brain-wide imaging during fictive navigation. By systematically manipulating the feedback parameters, we dissected the motor control process that allows the animal to stabilize its position using optic flow. The sensorimotor loop can be fully described by a single delay differential equation, whose solutions quantitatively capture the observed behavior across all experimental conditions. Both behavioral and neural data indicate that the observed adaptive response arises from the logarithmic nonlinearities at the sensory (Weber-Fechner law) and motor (Hennemans size principle) ends. These fundamental properties of the nervous system, conserved across species and sensory modalities, have traditionally been interpreted in terms of efficient coding. Our findings unveil a distinct functional role for such nonlinear transformations: ensuring stability in sensorimotor control despite inherent delays and sensory uncertainty.

neuroscience↗

bioRxiv2024

Linking Brain and Behavior States in Zebrafish Larvae Locomotion using Hidden Markov Models

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analysis, we exploit the generative nature of the HMM, using the neural sequences to generate synthetic trajectories whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.

animal behavior and cognition↗