Biology subjects

Ferrario, A.

Publications and source records attributed to Ferrario, A..

4 recordsLinked to original sources

bioRxiv2025

Beyond Locomotion: How Specialized Motor Rhythms Enable Vertebrate Escape from Capture

Escape behaviors following capture are crucial for survival, yet their underlying neurobiological mechanisms remain poorly understood. We investigated how Xenopus laevis tadpoles use struggling movements to escape head restraint. High-speed video tracking revealed a stereotyped sequence of body flexions with distinct kinematics during capture and release. We further recorded motoneuron and motor nerve activity along the body axis during fictive struggling to reconstruct biologically realistic struggling commands, to drive the movement of a biomechanically detailed tadpole model. Simulations showed that struggling - characterized by long-duration, low-frequency, caudorostral muscle activation - was optimized to generate escape forces. Notably, hydrodynamic thrust alone proved insufficient for release. However, direct mechanical interactions between the tadpoles body and the restraining object generated additional reactive forces that facilitated escape. These findings demonstrate how animals use coordinated motor outputs and body mechanics to interact with environment to generate maximal freeing forces as the fundamental escape strategy.

neuroscience↗

bioRxiv2024

Sensory and central contributions to motor pattern generation in a spiking, neuro-mechanical model of the salamander spinal cord

This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplified the dynamics of the locomotor networks, our model includes detailed simulations of the classes of neurons that are considered responsible for generating movement patterns. The locomotor circuits, modeled as a spiking neural network of adaptive leaky integrate-and-fire neurons, are coupled to a three-dimensional mechanical model of a salamander with realistic physical parameters and simulated muscles. In open-loop simulations (i.e., without sensory feedback), the model replicates locomotor patterns observed in-vitro and in-vivo for swimming and trotting gaits. Additionally, a modular descending reticulospinal drive to the central pattern generation network allows to accurately control the activation, frequency and phase relationship of the different sections of the limb and axial circuits. In closed-loop swimming simulations (i.e. including axial stretch feedback), systematic evaluations reveal that intermediate values of feedback strength increase the tail beat frequency and reduce the intersegmental phase lag, contributing to a more coordinated, faster and energy-efficient locomotion. Interestingly, the result is conserved across different feedback topologies (ascending or descending, excitatory or inhibitory), suggesting that it may be an inherent property of axial proprioception. Moreover, intermediate feedback strengths expand the stability region of the network, enhancing its tolerance to a wider range of descending drives, internal parameters modifications and noise levels. Conversely, high values of feedback strength lead to a loss of controllability of the network and a degradation of its locomotor performance. Overall, this study highlights the beneficial role of proprioception in generating, modulating and stabilizing locomotion patterns, provided that it does not excessively override centrally-generated locomotor rhythms. This work also underscores the critical role of detailed, biologically-realistic neural networks to improve our understanding of vertebrate locomotion. Author summaryIn this paper, we developed a computational model to investigate how salamanders move, both while swimming and walking. Unlike previous studies that often oversimplified the dynamics of these complex neural networks, our model includes detailed simulations of the classes of neurons that are considered responsible for generating movement patterns. The locomotor circuits, modeled as a spiking neural network, are coupled to a three-dimensional mechanical model of a salamander with realistic physical parameters and simulated muscles. The neural model integrates axial proprioceptive sensory feedback from the bodys movements to modulate the locomotor gaits. Our simulations suggest that this sensory feedback plays a major role in controlling the rhythm and coordination of movements. This has implications for understanding not only how salamanders move but also provides insights into the evolution of locomotion in vertebrates. By investigating how central and sensory mechanisms interact to produce efficient and adaptable movement, our work contributes to the broader field of neuroscience and robotics, offering potential strategies for designing more effective biomimetic robots.

neuroscience↗

bioRxiv2022

Hierarchical processing underpins competition in tactile perceptual bistability

Ambiguous sensory information can lead to spontaneous alternations between perceptual states, recently shown to extend to tactile perception. The authors recently proposed a simplified form of tactile rivalry which evokes two competing percepts for a fixed difference in input amplitudes across antiphase, pulsatile stimulation of the left and right fingers. This study addresses the need for a tactile rivalry model that captures the dynamics of perceptual alternations and that incorporates the structure of the somatosensory system. The model features hierarchical processing with two stages; a first stage resolves perceptual competition, leading to perceptual alternations; and a second stage encodes perceptual interpretations. The first stage could be located downstream of brainstem nuclei and the second stage could be located within the primary somatosensory cortex (area 3b). The model captures dynamical features specific to the tactile rivalry percepts and produces general characteristics of perceptual rivalry: input strength dependence of dominance times (Levelts proposition II), short-tailed skewness of dominance time distributions and the ratio of distribution moments. The presented modelling work leads to experimentally testable predictions. The same hierarchical model could generalise to account for percept formation, competition and alternations for bistable stimuli that involve pulsatile inputs from the visual and auditory domains. Author summaryPerceptual ambiguity involving the touch sensation has seen increased recent interest. It provides interesting opportunity to explore how our perceptual experience is resolved by dynamic computations in the brain. We recently proposed a simple form of tactile rivalry where stimuli consisted of antiphase sequences of high and low intensity pulses delivered to the right and left index fingers. The stimulus can be perceived as either one simultaneous pattern of vibration on both hands, or as a pattern of vibrations that jumps from one hand to the other, giving a sensation of apparent movement. During long presentation of the stimuli, ones perception switches every 5-20 seconds between these two interpretations, a phenomenon called tactile perceptual bistability. This study presents the first computational model for tactile bistability and is based on the structure of sensory brain areas. The model captures important characteristics of perceptual interpretations for tactile rivalry. We offer predictions in terms of how left-right tactile intensity differences are encoded and propose a location for the encoding of perceptual interpretations in sensory brain areas. The model provides a generalisable framework that can make useful predictions for future behavioural experiments with tactile and other types of stimuli.

neuroscience↗

bioRxiv2021

Whole animal modelling reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming in frog tadpoles

Animal behaviour is based on interaction between nervous, musculoskeletal and environmental systems. How does an animal process sensory stimuli, use it to decide whether and how to respond, and initiate the locomotor behaviour? We build the whole body computer models of a simple vertebrate with a complete chain of neural circuits and body units for sensory information processing, decision-making, generation of spiking activities, muscle innervation, body flexion, body-water interaction, and movement. Our Central Nervous System (CNS) model generates biologically-realistic spiking and reveals that sensory memory populations on two hindbrain sides compete for swimming initiation and first body flexion. Biomechanical 3-dimensional "Virtual Tadpole" (VT) model is constructed to evaluate if motor outputs of CNS model can produce swimming-like movements in a volume of "water". We find that whole animal modelling generates reliable and realistic swimming. The combination of CNS and VT models opens a new perspective for experiments with immobilised tadpoles.

bioinformatics↗