Simon Sponberg

Evidence for van der Waals adhesion in gecko setae

Geckos have evolved one of the most versatile and effective adhesives known. The mechanism of dry adhesion in the millions of setae on the toes of geckos has been the focus of scientific study for over a century. We provide the first direct experimental evidence for dry adhesion of gecko setae by van der Waals forces, and reject the use of mechanisms relying on high surface polarity, including capillary adhesion. The toes of live Tokay geckos were highly hydrophobic, and adhered equally well to strongly hydrophobic and strongly hydrophilic, polarizable surfaces. Adhesion of a single isolated gecko seta was equally effective on the hydrophobic and hydrophilic surfaces of a microelectro-mechanical systems force sensor. A van der Waals mechanism implies that the remarkable adhesive properties of gecko setae are merely a result of the size and shape of the tips, and are not strongly affected by surface chemistry. Theory predicts greater adhesive forces simply from subdividing setae to increase surface density, and suggests a possible design principle underlying the repeated, convergent evolution of dry adhesive microstructures in gecko, anoles, skinks, and insects. Estimates using a standard adhesion model and our measured forces come remarkably close to predicting the tip size of Tokay gecko seta. We verified the dependence on size and not surface type by using physical models of setal tips nanofabricated from two different materials. Both artificial setal tips stuck as predicted and provide a path to manufacturing the first dry, adhesive microstructures.

Rate-dependent frictional adhesion in natural and synthetic gecko setae

Geckos owe their remarkable stickiness to millions of dry, hard setae on their toes. In this study, we discovered that gecko setae stick more strongly the faster they slide, and do not wear out after 30 000 cycles. This is surprising because friction between dry, hard, macroscopic materials typically decreases at the onset of sliding, and as velocity increases, friction continues to decrease because of a reduction in the number of interfacial contacts, due in part to wear. Gecko setae did not exhibit the decrease in adhesion or friction characteristic of a transition from static to kinetic contact mechanics. Instead, friction and adhesion forces increased at the onset of sliding and continued to increase with shear speed from 500 nm s−1 to 158 mm s−1. To explain how apparently fluid-like, wear-free dynamic friction and adhesion occur macroscopically in a dry, hard solid, we proposed a model based on a population of nanoscopic stick–slip events. In the model, contact elements are either in static contact or in the process of slipping to a new static contact. If stick–slip events are uncorrelated, the model further predicted that contact forces should increase to a critical velocity (V*) and then decrease at velocities greater than V*. We hypothesized that, like natural gecko setae, but unlike any conventional adhesive, gecko-like synthetic adhesives (GSAs) could adhere while sliding. To test the generality of our results and the validity of our model, we fabricated a GSA using a hard silicone polymer. While sliding, the GSA exhibited steady-state adhesion and velocity dependence similar to that of gecko setae. Observations at the interface indicated that macroscopically smooth sliding of the GSA emerged from randomly occurring stick–slip events in the population of flexible fibrils, confirming our model predictions.

Reconstructing Body Size in Extinct Crown Cetacea (Neoceti) Using Allometry, Phylogenetic Methods and Tests from the Fossil Record

Living cetaceans exhibit interspecific size ranging across several orders of magnitude, and rank among the largest vertebrates ever. Details of how cetaceans evolved different body sizes, however, remain obscure, because they lack basic morphological proxies that have been traditionally used in other fossil vertebrates. Here, we reconstruct the body size of extinct crown group cetaceans (Neoceti) using different regression methods on extant skull and length data, in a phylogenetic context. Because most fossil cetaceans are fragmentary, we developed regression equations to predict total length based on cranial metrics that are preserved on most fossil crania. The resultant regression equations are based on a database of skull and length data from most extant lineages of cetaceans (n = 45 species; 272 specimens), sampling all living mysticete genera and all major clades of odontocetes. In generating predictive equations, we compared both conventional species data regression and independent contrast regression methods, as well as single trait predictors and a new approach that combines the advantages of a partial least squares (PLS) multivariate regression with independent contrasts. This last approach leverages the predictive power of using multiple correlated proxies. Lastly, we used the rare occurrences of fossil cetaceans with preserved total lengths to test the performance of our predictive equations for reconstructing body size from skull measurements alone. Our results demonstrate that incorporating information about phylogenetic relationships and multiple cranial measures in PLS scaling studies increases the accuracy of reconstructed body size, most notably by reducing prediction intervals by more than 70%. With this empirical foundation, we highlight the outline of major features in the evolution of body size for Neoceti and future opportunities to use these metrics for paleobiological questions.

A comparative approach to closed-loop computation.

Neural computation is inescapably closed-loop: the nervous system processes sensory signals to shape motor output, and motor output consequently shapes sensory input. Technological advances have enabled neuroscientists to close, open, and alter feedback loops in a wide range of experimental preparations. The experimental capability of manipulating the topology-that is, how information can flow between subsystems-provides new opportunities to understand the mechanisms and computations underlying behavior. These experiments encompass a spectrum of approaches from fully open-loop, restrained preparations to the fully closed-loop character of free behavior. Control theory and system identification provide a clear computational framework for relating these experimental approaches. We describe recent progress and new directions for translating experiments at one level in this spectrum to predictions at another level. Operating across this spectrum can reveal new understanding of how low-level neural mechanisms relate to high-level function during closed-loop behavior.

A single muscle's multifunctional control potential of body dynamics for postural control and running

A neuromechanical approach to control requires understanding how mechanics alters the potential of neural feedback to control body dynamics. Here, we rewrite activation of individual motor units of a behaving animal to mimic the effects of neural feedback without concomitant changes in other muscles. We target a putative control muscle in the cockroach, Blaberus discoidalis (L.), and simultaneously capture limb and body dynamics through high-speed videography and a micro-accelerometer backpack. We test four neuromechanical control hypotheses. We supported the hypothesis that mechanics linearly translates neural feedback into accelerations and rotations during static postural control. However, during running, the same neural feedback produced a nonlinear acceleration control potential restricted to the vertical plane. Using this, we reject the hypothesis from previous work that this muscle acts primarily to absorb energy from the body. The conversion of the control potential is paralleled by nonlinear changes in limb kinematics, supporting the hypothesis that significant mechanical feedback filters the graded neural feedback for running control. Finally, we insert the same neural feedback signal but at different phases in the dynamics. In this context, mechanical feedback enables turning by changing the timing and direction of the accelerations produced by the graded neural feedback.

Feedback Control as a Framework for Understanding Tradeoffs in Biology

Control theory arose from a need to control synthetic systems. From regulating steam engines to tuning radios to devices capable of autonomous movement, it provided a formal mathematical basis for understanding the role of feedback in the stability (or change) of dynamical systems. It provides a framework for understanding any system with regulation via feedback, including biological ones such as regulatory gene networks, cellular metabolic systems, sensorimotor dynamics of moving animals, and even ecological or evolutionary dynamics of organisms and populations. Here, we focus on four case studies of the sensorimotor dynamics of animals, each of which involves the application of principles from control theory to probe stability and feedback in an organisms response to perturbations. We use examples from aquatic (two behaviors performed by electric fish), terrestrial (following of walls by cockroaches), and aerial environments (flight control by moths) to highlight how one can use control theory to understand the way feedback mechanisms interact with the physical dynamics of animals to determine their stability and response to sensory inputs and perturbations. Each case study is cast as a control problem with sensory input, neural processing, and motor dynamics, the output of which feeds back to the sensory inputs. Collectively, the interaction of these systems in a closed loop determines the behavior of the entire system.

Luminance-dependent visual processing enables moth flight in low light

Not too fast and not too slow Moths are typically active during dawn and dusk when light levels are low and vision is challenging. Slower visual response times can allow for greater light sensitivity, but flying insects are both moving and tracking moving targets, making such tradeoffs potentially problematic. Using a combination of modeling and experiments, Sponberg et al. show that moths are able to avoid this potential decrease in visual acuity (see the Perspective by Warrant). This is because the point at which their perception of movement would be compromised is just above the natural frequency at which flowers sway. Thus, insect vision is precisely adapted to the light and movement conditions of their environment. Science, this issue p. 1245; see also p. 1212 Moth vision in low light matches that needed to follow swaying flowers. [Also see Perspective by Warrant] Animals must operate under an enormous range of light intensities. Nocturnal and twilight flying insects are hypothesized to compensate for dim conditions by integrating light over longer times. This slowing of visual processing would increase light sensitivity but should also reduce movement response times. Using freely hovering moths tracking robotic moving flowers, we showed that the moth’s visual processing does slow in dim light. These longer response times are consistent with models of how visual neurons enhance sensitivity at low light intensities, but they could pose a challenge for moths feeding from swaying flowers. Dusk-foraging moths avoid this sensorimotor tradeoff; their nervous systems slow down but not so much as to interfere with their ability to track the movements of real wind-blown flowers.

Flexible multielectrodes can resolve multiple muscles in an insect appendage

Research into the neuromechanical basis of behavior, either in biomechanics, neuroethology, or neuroscience, is frequently limited by methods of data collection. Two of the most pressing needs are for methods with which to (1) record from multiple neurons or muscles simultaneously and (2) perform this recording in intact, behaving animals. In this paper we present the fabrication and testing of flexible multielectrode arrays (fMEAs) that move us significantly towards these goals. The fMEAs were used to record the activity of several distinct units in the coxa of the cockroach Blaberus discoidalis. The devices fabricated here address the first goal in two ways: (1) their flexibility allows them to be inserted into an animal and guided through internal tissues in order to access distinct groups of neurons and muscles and (2) their recording site geometry has been tuned to suit the anatomy under study, yielding multichannel spike waveforms that are easily separable under conditions of spike overlap. The flexible nature of the devices simultaneously addresses the second goal, in that it is less likely to interfere with the natural movement of the animal.

Shifts in a single muscle's control potential of body dynamics are determined by mechanical feedback

Muscles are multi-functional structures that interface neural and mechanical systems. Muscle work depends on a large multi-dimensional space of stimulus (neural) and strain (mechanical) parameters. In our companion paper, we rewrote activation to individual muscles in intact, behaving cockroaches (Blaberus discoidalis L.), revealing a specific muscles potential to control body dynamics in different behaviours. Here, we use those results to provide the biologically relevant parameters for in situ work measurements. We test four hypotheses about how muscle function changes to provide mechanisms for the observed control responses. Under isometric conditions, a graded increase in muscle stress underlies its linear actuation during standing behaviours. Despite typically absorbing energy, this muscle can recruit two separate periods of positive work when controlling running. This functional change arises from mechanical feedback filtering a linear increase in neural activation into nonlinear work output. Changing activation phase again led to positive work recruitment, but at different times, consistent with the muscles ability to also produce a turn. Changes in muscle work required considering the natural sequence of strides and separating swing and stance contributions of work. Both in vivo control potentials and in situ work loops were necessary to discover the neuromechanical coupling enabling control.

Dual dimensionality reduction reveals independent encoding of motor features in a muscle synergy for insect flight control.

What are the features of movement encoded by changing motor commands? Do motor commands encode movement independently or can they be represented in a reduced set of signals (i.e. synergies)? Motor encoding poses a computational and practical challenge because many muscles typically drive movement, and simultaneous electrophysiology recordings of all motor commands are typically not available. Moreover, during a single locomotor period (a stride or wingstroke) the variation in movement may have high dimensionality, even if only a few discrete signals activate the muscles. Here, we apply the method of partial least squares (PLS) to extract the encoded features of movement based on the cross-covariance of motor signals and movement. PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest. We use this approach to explore how the main downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode torque during yaw turns. We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli. We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity) consistent with their hypothesized function of producing a left-right power differential. Alternatively, each muscle might individually encode variation in movement. We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke. At first, the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque. However, when we consider the PLS features, the muscles reveal independent encoding of torque. Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation. PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in high dimensional sensory or motor transformations.