WORKSHOPS

Living Machines 2022 / WORKSHOPS

Workshop 1. 19 July 2022

How do animal neuromechanical systems perform communication, coordination and control?

Organizers

Roger Quinn, Professor, rdq@case.edu, http://biorobots.case.edu
Hillel Chiel, Professor, hjc@case.edu, https://case.edu/artsci/biology/chiellab/ Case Western Reserve University

The purpose of this workshop is to compare and contrast how neuromechanical systems in animals from different phyla allow them to perform functions essential to their survival for the purpose of modeling them and implementing them in robots. The workshop first focuses on an overview on how the frequency of movement and size of limbs can affect the control of locomotion (Dr. Gregory Sutton). The role of joint stresses and strains on control of limbed locomotion is then discussed (Dr. Matthew Tresch). A discussion of how to fit multiple parameters that are necessary for neuromechanical models provides useful guidance for others who are working in this area (Dr. Matthieu Chardon). New insights on insect walking will bridge between single neuron function and system performance of six-legged walking (Dr. Ansgar Büschges). To further understand sensory inputs to the insect leg, a mechanical model of key leg strain sensors, campaniform sensilla, has been used to gain insights (Dr. Alexander Blanke). The workshop should be of broad general interest to those studying the neural and mechanical basis of behavior and those interested in creating autonomous artificial devices inspired by animals.

Program: talks

This workshop will focus on how animals’ neuromechanical systems solve problems that autonomous robots must solve. Thus, it should be of broad general interest to those studying the neural and mechanical basis of behavior and those interested in creating autonomous artificial devices inspired by animals.

“Limb control parameters are determined by size and frequency of movement”

Gregory Sutton, Professor at Royal Society University, Department of Life Sciences, College of Science, University of Lincoln, U.K.

While biological systems generate legged locomotion in very different ways, there are two parameters, limb size and leg swing frequency, which can be used to determine the control strategy necessary to generate locomotion. We show that the fundamental physics of legged locomotion can be derived from these two parameters, and that these two parameters can then determine the fundamental control strategy necessary to generate stable locomotion. Consequently, while animals generate locomotion very differently, the fundamental physics of the movement can yield universal parameters for all legged locomotion.

“The importance of internal joint mechanics for understanding neural coordination strategies.”

Matthew Tresch, Professor, Biomedical Engineering, Neuroscience, Physical Medicine and Rehabilitation, Northwestern University

Studies of motor control usually examine how the CNS activates muscles to achieve task performance – grasping an object or walking to a target. However, to achieve task performance, muscles must act through joints and so different control strategies, each achieving the same task performance, might result in aberrant stresses and strains within our joints. We show how consideration of these internal joint stresses and strains leads to better understanding of neural control strategies and might help guide investigations into neural circuitry underlying movement.

“Algorithmic Parameter Estimation and Uncertainty Quantification for Hodgkin–Huxley Neuron Models”

Matthieu Chardon, Research Assistant Prof, Dept of Neuroscience, Northwestern University

Neuromechanical models often rely on Hodgkin-Huxley (H-H) formulations for their neuron models. A challenge with H-H models is that many different sets of ion channel conductances can produce the same response from the model. This makes computational approaches for inferring ion channel parameters from observations of voltage data difficult or intractable. We show that by framing the inference in a Bayesian setting, which naturally allows multiple solutions, and employing a specific algorithm from the Markov chain Monte Carlo family allows us to successfully reconstruct “landscapes” or “maps” of possible parameter sets. The visualization of these solution maps (i.e., posteriors) enables physiologists to inspect and reason about the vast possibilities, sensitivities, and uncertainties of ion channel parameters. We will cover how this algorithm is being used to reverse engineer a locomotion circuit.

“Task-specificity in the Control of Insect Walking”

Ansgar Büschges, Department of Animal Physiology, Institute of Zoology, University of Cologne

Recent methodological advances in high-resolution-high-speed video analysis of locomotor behavior combined with new neurogenetic tools allow for studying the neural control of six-legged insect walking from the systems level, i.e., interleg coordination, to the single-leg level and to the level of single neuron function in generating a stepping cycle. The talk will report recent advances in unravelling the neural basis of six-legged walking with a specific focus on the generation of task-specific modifications.

“Activation of insect load sensor fields: Lessons from finite element analysis based on ultra-high- resolution tomography”

Alexander Blanke, Institute of Evolutionary Biology and Animal Ecology, University of Bonn

Campaniform sensilla (CS) are load sensors embedded in the exoskeleton of insects. They are activated by deformations of the surrounding exoskeleton but it remained unclear how we can imagine the loading of whole fields of these load sensors and how their substructures interact with each other in a given sensor field. Using ultra high-resolution tomography, we were able to generate a detailed biomechanical model of a CS field in order to study single CS under quasi-natural loading. The talk will report how we can imagine single-CS activation within a field, and the associated challenges for the neuronal system to interpret CS-field activation.

Time schedule coming soon!