Biology subjects

Chiel, H. J.

Publications and source records attributed to Chiel, H. J..

6 recordsLinked to original sources

bioRxiv2026

How bending scales: Geometric scaling differentially affects force responses in bending biomechanical structures

The size of an animal's body greatly affects which biomechanical forces (muscle forces, elasticity, viscosity, and inertia) dominate its behavior. By knowing how the relative contributions of these forces scale with the animal's size and geometry, we can make predictions about the neural control required to generate its behaviors and to respond to perturbations. The scaling laws of force-producing and energy-transforming structures utilized in neuromechanics focus on spring-like muscles and elastic structures -- those that generate forces by changing their length. However, many systems generate and transform energy by bending their tissues, that is, changing their curvature. Even though bending structures are common in biomechanical systems, their scaling laws have not been thoroughly studied. In this paper, we investigate how the forces produced by bending structures scale with the size and geometry of an animal and how that scaling differs from standard spring-like muscles and elastic elements. Through analytical and computational investigations, we demonstrate that particular allometries yield different force scaling laws for bending structures. These different scaling laws impact the duration of quasistatic behaviors, may place limits on animal size, and provide material-efficient methods of remodeling in energy-transforming systems. Together, this work demonstrates the differences between bending and straining biomechanical structures and the need for integrative approaches to scaling analysis that account for specific morphologies and deformations, not just a structure's cross-sectional area.

bioengineering↗

bioRxiv2026

Context-dependent mechanical reconfiguration is necessary for multifunctional behavior in a constrained hydrostat

Muscular hydrostats, muscular structures with no rigid skeleton, are ubiquitous within the animal kingdom, from vertebrate tongues to cephalopod arms1,2, but how they perform complex actions remains poorly understood. One model hydrostat studied for its neural control3-7 and biomechanics8-17 is the feeding system (buccal mass) of the sea hare Aplysia (Fig. 1). The buccal mass (Fig. 1b) performs multiple feeding behaviors by coordinating intrinsic muscles to move a grasper (odontophore)18,19. In this paper, we investigated how mechanical reconfiguration from interacting shape-changing elements facilitates large odontophore protractions. During rejection behaviors, mechanical reconfiguration of the odontophore (elongating its shape to a higher aspect ratio) stretches a protractor muscle (I2), allowing I2 to generate stronger protractions12. In biting behaviors, the odontophore has a similar range of motion. However, during biting, the odontophore has a lower aspect ratio throughout protraction, meaning the I2 muscle alone is insufficient to reach observed protractions due to its length/tension property and reduced mechanical advantage9,10,12,18. By combining new analysis of MRI movies of Aplysia feeding12,18 (Fig. 1) with a new biomechanical model for biting and rejection (Fig. 2), we demonstrate two context-dependent mechanical reconfiguration mechanisms that explain the different ways large protractions are produced in biting and rejection (Fig. 3). The mechanisms integrate shape changes, bending and conforming of muscle structures, and shifts in contact interactions. We propose two mechanical subclasses of muscular hydrostats, "constrained" or "unconstrained" (Fig. 4), that may be morphologically similar but employ different control strategies depending on whether mechanical constraints are reliably present. O_FIG O_LINKSMALLFIG WIDTH=150 HEIGHT=200 SRC="FIGDIR/small/715937v1_fig1.gif" ALT="Figure 1"> View larger version (87K): org.highwire.dtl.DTLVardef@ce6e7org.highwire.dtl.DTLVardef@10b6eb2org.highwire.dtl.DTLVardef@1909aeborg.highwire.dtl.DTLVardef@aabc21_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 1.C_FLOATNO Anatomy and kinematics of the Aplysia feeding system (a1) Adult Aplysia californica searching for food and (a2) feeding on Gracilaria macroalgae ((a1) photo credit: Dr. Jeffrey P. Gill, (a2) modified with permission from Bennington et al. 202514). Gray highlight shows the location of the feeding structure, the buccal mass (b). (b) An anatomical diagram of a midline sagittal view of a buccal mass. During feeding, the odontophore (the internal grasper of the buccal mass) protracts through the tubelike I3 muscle. In the midsagittal plane, the I3 is visible as two longitudinal elements, but is one continuous structure that runs circumferentially around the buccal mass. The inner wall of the distal I3 is shown in dark blue. The dashed white line shows the jaw line, which is used as the reference for both the translation and rotation measurements. (c) Configuration of the buccal mass (left: anatomical diagram; middle: MRI frames) showing (c1) peak retraction and (c2) peak protraction. (right) A diagram of the buccal mass was created to highlight key anatomical landmarks for each frame of the MRI video showing a complete biting sequence (d-e). The same diagrammatic representations of the landmarks are shown in (d) and (e) for the protraction and retraction portions of the biting sequence, respectively (See STAR Methods). The frames shown in (c1) and (c2) correspond to the 0 ms and 3410 ms frames, respectively, and are the same between the middle and right portions of the figure. Key frames referred to in the text: t0: start of the behavioral cycle, t1: peak rotation reached, t2: peak translation reached, t3: rotation plateau ended, t4: end of behavioral cycle. (f) Kinematic measurements were taken using the drawn diagrams for each frame in the sequence. See main text for definitions of variables. All scale bars correspond to 10 mm. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/715937v1_fig2.gif" ALT="Figure 2"> View larger version (34K): org.highwire.dtl.DTLVardef@1251ffforg.highwire.dtl.DTLVardef@57c6f1org.highwire.dtl.DTLVardef@c5ff92org.highwire.dtl.DTLVardef@10ddd1a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 2.C_FLOATNO Kinetic/Kinematic biomechanical model of the buccal mass (a) Rest geometry of the biomechanical model. The grasper (odontophore) is modeled as a rigid ellipse (magenta with yellow radula). It is connected to the I1/I3 lumen (blue trapezoid) by the hinge muscle (green). The I2 protractor muscle (red) wraps conformally around the odontophore and attaches at the lateral groove. The net force and torque from the I2 on the odontophore are found by performing an instantaneous force balance on a small arc of the ellipse and integrating across the full region of contact between the I2 and the odontophore. The hinge muscle is modeled as a linearly elastic, geometrically exact beam. At each position along the beams midline, a quasistatic force balance is performed (see STAR Methods). (b1) The tension in the I2 is modeled using the length-tension relationship reported in Yu et al. 1999 scaled by a normalized activation level. (b2) The axial and bending stiffness of the beam hinge were calibrated to ex vivo animal data reported in Sutton et al. 2004. Gray region indicates odontophore displacements observed during biting behaviors (Sutton et al. 2004). (c1-c2) To investigate the effects of mechanical reconfiguration on odontophore position at peak protraction, (c1) the aspect ratio of the odontophore ellipse and (c2) the stretch of the lateral groove were added as additional kinematic constraints. (c1) and (c2) show results from the model but do not correspond to any particular behavior or configuration observed in the animal. These constraints impact the biomechanical model via contact forces from the I1/I3 (see STAR Methods). The lateral groove stretch is converted to a depression angle of the dorsal I1/I3 muscle as a proxy for the wrapping of the dorsal I3 around the odontophore observed during in vivo feeding behaviors (Fig 1). (d-e) MRI frames at peak protraction in (d1, with and without overlay) biting (t2) and (e1, with and without overlay) rejection ({tau}2) compared to corresponding frames from the biomechanical model (d2 and e2, respectively). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/715937v1_fig3.gif" ALT="Figure 3"> View larger version (56K): org.highwire.dtl.DTLVardef@12ef85aorg.highwire.dtl.DTLVardef@b2ff2borg.highwire.dtl.DTLVardef@18e813corg.highwire.dtl.DTLVardef@c69e6d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 3.C_FLOATNO Mechanical reconfiguration of the buccal mass (a) Midsagittal kinematics of the buccal mass during a (left) biting and (right) rejection behavior (see also Figs. S1 and S2). Colored circles (diamonds) show data for an individual frame, and the black line shows the two-point moving average of the signal. Vertical dashed lines show concurrent time points in the different kinematic signals (biting: t0: cycle starts, t1: peak rotation, t2: peak translation, t3: rotation plateau ended, t4: cycle ends. Rejection: {tau}0: cycle starts, {tau}1: rotation plateau ends, {tau}2: peak translation, {tau}3: peak rotation, {tau}4: cycle ends). (b) Model configurations for nine different pairs of aspect ratios ({Phi}) and lateral groove stretches ({lambda}LG ) (numbers correspond to the labeled points in (Fig. S6c)). Note that these simulated results from the model do not necessarily correspond to configurations observed in the animal but rather show changes in the systems configuration due to changes in the kinematic parameters. All configurations here were achieved with an I2 activation of AI2 = 65%. (c-d) Sensitivity of the model translation and rotation at peak protraction to lateral groove shortening ({lambda}LG, top row) and aspect ratio change ({Phi}, bottom row) for biting (c) and rejection (d). The y-axis for all panels reports the difference between the model prediction and observed animal value at peak protraction (for translation or rotation) normalized by the range of motion (ROM) for each behavior. For each panel, one kinematic parameter is held fixed (top:{Phi} fixed; bottom:{lambda} LG fixed) at the value observed in the animal at peak protraction, and the other is varied to determine the effect of changing this parameter on the translation and rotation of the odontophore. Vertical dashed lines show the observed value of the varied parameter in the animal at peak protraction. The horizontal dashed line shows 0 difference for reference. The steepness of the difference curve in the vicinity of the vertical dashed line indicates how sensitive the system is to changes in each kinematic parameter near peak protraction. Here, a steeper curve (with a positive or negative slope) indicates greater sensitivity. For biting simulations, AI2 = 15%, and for rejection, AI2 = 90% based on the results of the model validation. Each curve in (c) and (d) is a 1D cross-section of the 2D contour plots shown in Figs. S6-S7. For a complete view of the sensitivity of translation and rotation to lateral groove stretch and aspect ratio across the kinematic configuration space at different I2 activations, see Figs. S6-S7. Note that (c) and (d) use different vertical scales. The smaller scale for the rejection plots was chosen to better show the difference curves for rejection, and it reflects the overall decreased sensitivity to both lateral groove stretch and aspect ratio changes for the rejection behaviors. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/715937v1_fig4.gif" ALT="Figure 4"> View larger version (36K): org.highwire.dtl.DTLVardef@1cb461aorg.highwire.dtl.DTLVardef@677ff0org.highwire.dtl.DTLVardef@10089acorg.highwire.dtl.DTLVardef@4e9663_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 4.C_FLOATNO Mechanical reconfiguration facilitates behaviors in a variety of constrained hydrostat systems Combinations of the active shape change of internal structures (cyan), changes to the movement constraints and contact interaction (blue), and bending and conforming of structures (magenta) allow constrained hydrostats to mechanically reconfigure their neuromusculature (purple) to perform various behaviors. This can be seen in various systems across various species. As discussed here, the Aplysia buccal mass uses combinations of these mechanisms in (a) biting and (b) rejection behaviors to protract the buccal mass. (c) The pond snail, Lymnaea, has a morphologically similar buccal mass to Aplysia, but its I1/I3 homolog, the anterior jugalis, sits further posterior to the odontophore35, meaning it may more readily rely on the bending of the anterior jugalis and contact interactions during protraction. (d) The octopus and, more broadly, cephalopod buccal masses contain a beak that lacks a fixed articulation. Instead, by activating the lateral mandibular muscle (LMM), the buccal mass can create a stiff rotation point and may shift the function of the posterior mandibular muscle (PMM) from compressing the buccal mass to opening the beak36,37. (e) The human tongue (and other Type I tongues38) sits within the skull and makes use of contact with the hard palate to push food from the oral cavity into the pharynx27,48. (f) Additionally, by changing how the tongue interacts with the palate and teeth, while maintaining the same internal shape, humans can produce various vowel and consonant sounds39,49,50. This use of contact with the palate and teeth is known in the phonetics community as "bracing." Here, by creating a groove in the middle of the tongue, the phonemes /{varepsilon}/ and /ae/ can be produced. By raising the tongue and creating palatal contact while maintaining that groove, these vowels shift to the fricative consonants /s/ and /{theta}/49. Small insets show which of the mechanical configurations are used in each behavior. C_FIG

animal behavior and cognition↗

bioRxiv2025

Neuromechanical Simulation with NEURON and MuJoCo

In computational neuroscience, simulation platforms generally do not have adequate tools to model the brain, body and environment simultaneously. We demonstrate a method for simulating neuromechanical models using a novel combination of widely used software platforms: NEURON and MuJoCo. Different neural models are used to control a realistic musculoskeletal model in both open-loop and closed-loop configurations. Three models are presented: (1) an open-loop model using simple spiking neurons from the NEURON model library; (2) an open-loop model using realistic, spiking motoneurons; and (3) a closed-loop central pattern generator with feedback from the physics engine.

neuroscience↗

bioRxiv2024

Incorporating buccal mass planar mechanics and anatomical features improves neuromechanical modeling of Aplysia feeding behavior

To understand how behaviors arise in animals, it is necessary to investigate both the neural circuits and the biomechanics of the periphery. A tractable model system for studying multifunctional control is the feeding apparatus of the marine mollusk Aplysia californica. Previous in silico and in roboto models have investigated how the nervous and muscular systems interact in this system. However, these models are still limited in their ability to match in vivo data both qualitatively and quantitatively. We introduce a new neuromechanical model of Aplysia feeding that combines a modified version of a previously developed neural model with a novel biomechanical model that better reflects the anatomy and kinematics of Aplysia feeding. The model was calibrated using a combination of previously measured biomechanical parameters and hand-tuning to behavioral data. Using this model, simulation feeding experiments were conducted, and the resulting behavioral metrics were compared to animal data. The model successfully produces three key behaviors seen in Aplysia and demonstrates a good quantitative agreement with biting and swallowing behaviors. Additional work is needed to match rejection behavior quantitatively and to reflect qualitative observations related to the relative contributions of two key muscles, the hinge and I3. Future improvements will focus on incorporating the effects of deformable 3D structures in the simulated buccal mass. Author summaryAnimals need to produce a wide array of behaviors so that they can adapt to changes in their environment. To understand how behaviors are performed, we need to understand how the brain and the body work together in their environment. One tractable system in which to study this brain-body relationship is the feeding behavior of the sea slug Aplysia californica. Despite having a small fraction of the number of neurons that humans have, this animal can produce many behaviors, respond to a changing environment, and learn from previous experiences. We have create an improved computer model of the slugs mouthparts that simulates many of its key muscles and the forces they produce, together with a representation of the network of neurons that control them. With this model, we can recreate the feeding behaviors that we observe in the real animal, including biting, swallowing, and rejection, and use it to make quantitative predictions of how the animal will behave and respond to different stimuli. We found however that some aspects of the system were not well represented by simple 1-dimensional muscles, as has been done in most biomechanical models to date, but requires us to consider more complicated deformations of these soft bodies. Using this model as a tool, we aim to test hypotheses about brain-body interactions in the sea slug to better understand the behavior of small, slowly moving animals.

neuroscience↗

bioRxiv2022

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al., 2015; Lyttle et al., 2017). We established in (Wang et al., 2021) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.

neuroscience↗

bioRxiv2021

Carbon Fiber Electrodes for Intracellular Recording and Stimulation

To understand neural circuit dynamics, it is critical to manipulate and record from many neurons, ideally at the single neuron level. Traditional recording methods, such as glass microelectrodes, can only control a small number of neurons. More recently, devices with high electrode density have been developed, but few of them can be used for intracellular recording or stimulation in intact nervous systems, rather than on neuronal cultures. Carbon fiber electrodes (CFEs) are 8 micron-diameter electrodes that can be organized into arrays with pitches as low as 80 {micro}m. They have been shown to have good signal-to-noise ratios (SNRs) and are capable of stable extracellular recording during both acute and chronic implantation in vivo in neural tissue such as rat motor cortex. Given the small fiber size, it is possible that they could be used in arrays for intracellular stimulation. We tested this using the large identified and electrically compact neurons of the marine mollusk Aplysia californica. The cell bodies of neurons in Aplysia range in size from 30 to over 250 {micro}m. We compared the efficacy of CFEs to glass microelectrodes by impaling the same neurons cell body with both electrodes and connecting them to a DC coupled amplifier. We observed that intracellular waveforms were essentially identical, but the amplitude and SNR in the CFE were lower than in the glass microelectrode. CFE arrays could record from 3 to 8 neurons simultaneously for many hours, and many of these recordings were intracellular as shown by recording from the same neuron using a glass microelectrode. Stimulating through CFEs coated with platinum-iridium had stable impedances over many hours. CFEs not within neurons could record local extracellular activity. Despite the lower SNR, the CFEs could record synaptic potentials. Thus, the stability for multi-channel recording and the ability to stimulate and record intracellularly make CFEs a powerful new technology for studying neural circuit dynamics.

neuroscience↗