A novel behavioural paradigm for characterising anticipatory postural adjustments in mice

Daily we use purposeful, voluntary movements to interact with our environment. These movements demand and cause our body to experience a weight redistribution, i.e., anticipatory postural adjustments (APAs), and it’s the appropriate employment of these APAs that allows us to complete said voluntary movements without falling over or losing our equilibrium. The literature suggests that for humans, monkeys, and several quadrupeds, APAs are crucial at initiation and during movement. However, research has been somewhat limited due to the lack of behavioural paradigms that would allow for a better understanding into the neural circuitry involved with APAs. Given the widespread availability of genetic tools and advanced viral techniques in mice I focused my efforts in developing a novel behavioral paradigm for this species. The first chapters detail the reasoning behind the development of this novel behavioural paradigm while also providing a complete description of the different components and their functions. Later chapters use the custom-designed setup to characterise mouse APAs, incorporating various recording approaches designed to quantify APAs and compare them to those described in prior work, highlighting possible interspecifies similarities and differences. Additionally, I briefly discuss the potential neural circuitry of APAs informed by my own data and research that has been done in different animals, providing a comprehensive overview of APAs in mice

Investigating Sensorimotor Control in Locomotion using Robots and Mathematical Models

Locomotion is a very diverse phenomenon that results from the interactions of a body and its environment and enables a body to move from one position to another. Underlying control principles rely among others on the generation of intrinsic body movements, adaptation and synchronization of those movements with the environment, and the generation of respective reaction forces that induce locomotion. We use mathematical and physical models, namely robots, to investigate how movement patterns emerge in a specific environment, and to what extent central and peripheral mechanisms contribute to movement generation. We explore insect walking, undulatory swimming and bimodal terrestrial and aquatic locomotion. We present relevant findings that explain the prevalence of tripod gaits for fast climbing based on the outcome of an optimization procedure. We also developed new control paradigms based on local sensory pressure feedback for anguilliform swimming, which include oscillator-free and decoupled control schemes, and a new design methodology to create physical models for locomotion investigation based on a salamander-like robot. The presented work includes additional relevant contributions to robotics, specifically a new fast dynamically stable walking gait for hexapedal robots and a decentralized scheme for highly modular control of lamprey-like undulatory swimming robots

A neural model of the motor control system

In this thesis I present the Recurrent Error-driven Adaptive Control Hierarchy (REACH); a large-scale spiking neuron model of the motor cortices and cerebellum of the motor control system. The REACH model consists of anatomically organized spiking neurons that control a nonlinear three-link arm to perform reaching and handwriting, while being able to adapt to unknown changes in arm dynamics and structure. I show that the REACH model accounts for data across 19 clinical and experimental studies of the motor control system. These data includes a mix of behavioural and neural spiking activity, across normal and damaged subjects performing adaptive and static tasks. The REACH model is a dynamical control system based on modern control theoretic methods, specifically operational space control, dynamic movement primitives, and nonlinear adaptive control. The model is implemented in spiking neurons using the Neural Engineering Framework (NEF). The model plans trajectories in end-effector space, and transforms these commands into joint torques that can be sent to the arm simulation. Adaptive components of the model are able to compensate for unknown kinematic or dynamic system parameters, such as arm segment length or mass. Using the NEF the adaptive components of the system can be seeded with approximations of the system kinematics and dynamics, allowing faster convergence to stability. Stability proofs for nonlinear adaptation methods implemented in distributed systems with scalar output are presented. By implementing the motor control model in spiking neurons, biological constraints such as neurotransmitter time-constants and anatomical connectivity can be imposed, allowing further comparison to experimental data for model validation. The REACH model is compared to clinical data from human patients as well as neural recording from monkeys performing reaching experiments. The REACH model represents a novel integration of control theoretic methods and neuroscientific constraints to specify a general, adaptive, biologically plausible motor control algorithm.4 month

Functional organization of cutaneous reflex pathways during locomotion and reorganization following peripheral nerve and/or spinal cord lesions

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