A multi-differential neuromorphic approach to motion detection

This paper presents a multi-differential neuromorphic approach to motion detection. The model is based evidence for a differential operators interpretation of the properties of the cortical motion pathway. We discuss how this strategy, which provides a robust measure of speed for a range of types of image motion using a single computational mechanism, forms a useful framework in which to develop future neuromorphic motion systems. We also discuss both our approaches to developing computational motion models, and constraints in the design strategy for transferring motion models to other domains of early visual processing.     

Self versus Environment Motion in Postural Control

To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results.     

Nutritional state and collective motion: from individuals to mass migration

In order to move effectively in unpredictable or heterogeneous environments animals must make appropriate decisions in response to internal and external cues. Identifying the link between these components remains a challenge for movement ecology and is important in understanding the mechanisms driving both individual and collective motion. One accessible way of examining how internal state influences an individual''s motion is to consider the nutritional state of an animal. Our experimental results reveal that nutritional state exerts a relatively minor influence on the motion of isolated individuals, but large group-level differences emerge from diet affecting inter-individual interactions. This supports the idea that mass movement in locusts may be driven by cannibalism. To estimate how these findings are likely to impact collective migration of locust hopper bands, we create an experimentally parametrized model of locust interactions and motion. Our model supports our hypothesis that nutrient-dependent social interactions can lead to the collective motion seen in our experiments and predicts a transition in the mean speed and the degree of coordination of bands with increasing insect density. Furthermore, increasing the interaction strength (representing greater protein deprivation) dramatically reduces the critical density at which this transition occurs, demonstrating that individuals'' nutritional state could have a major impact on large-scale migration.     

Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures.

We investigate a novel approach for studying the kinetics of protein folding. Our framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs) that have been applied in many diverse fields with great success. In our previous work, we presented our PRM-based technique and obtained encouraging results studying protein folding pathways for several small proteins. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our PRM-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics that are not captured by other theoretical techniques.     

A Computational Model for Collective Cellular Motion in Three Dimensions: General Framework and Case Study for Cell Pair Dynamics

Cell migration in healthy and diseased systems is a combination of single and collective cell motion. While single cell motion has received considerable attention, our understanding of collective cell motion remains elusive. A new computational framework for the migration of groups of cells in three dimensions is presented, which focuses on the forces acting at the microscopic scale and the interactions between cells and their extracellular matrix (ECM) environment. Cell-cell adhesion, resistance due to the ECM and the factors regulating the propulsion of each cell through the matrix are considered. In particular, our approach emphasizes the role of receptors that mediate cell-cell and cell-matrix interactions, and examines how variation in their properties induces changes in cellular motion. As an important case study, we analyze two interacting cells. Our results show that the dynamics of cell pairs depends on the magnitude and the stochastic nature of the forces. Stronger intercellular stability is generally promoted by surface receptors that move. We also demonstrate that matrix resistance, cellular stiffness and intensity of adhesion contribute to migration behaviors in different ways, with memory effects present that can alter pair motility. If adhesion weakens with time, our findings show that cell pair break-up depends strongly on the way cells interact with the matrix. Finally, the motility for cells in a larger cluster (size 50 cells) is examined to illustrate the full capabilities of the model and to stress the role of cellular pairs in complex cellular structures. Overall, our framework shows how properties of cells and their environment influence the stability and motility of cellular assemblies. This is an important step in the advancement of the understanding of collective motility, and can contribute to knowledge of complex biological processes involving migration, aggregation and detachment of cells in healthy and diseased systems.     

In Silico Reconstitution of Listeria Propulsion Exhibits Nano-Saltation

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein–protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our “in silico reconstitution” produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes, in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.     

In silico reconstitution of Listeria propulsion exhibits nano-saltation

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein–protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our “in silico reconstitution” produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes, in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.     

Dynamic control of allosteric antagonism of leukocyte function antigen-1 and intercellular adhesion molecule-1 interaction

Leukocyte function associated antigen-1 (LFA-1) plays a critical role in T cell migration and has been recognized as a therapeutic target for immune disorders. Several classes of small molecule antagonists have been developed to block LFA-1 interaction with intercellular adhesion molecule-1 (ICAM-1). Recent structural studies show that the antagonists bind to an allosteric site in the I-domain of LFA-1. However, it is not yet clear how these small molecules work as antagonists since no significant conformational change is observed in the I-domain-antagonist complex structures. Here we present a computational study suggesting how these allosteric antagonists affect the dynamics of the I-domain. The lowest frequency vibrational mode calculated from an LFA-1 I-domain structure shows large scale "coil-down" motion of the C-terminal alpha7 helix, which may lead to the open form of the I-domain. The presence of an allosteric antagonist greatly reduces this motion of the alpha7 helix as well as other parts of the I-domain. Thus, our study suggests that allosteric antagonists work by eliminating breathing motion that leads to the open conformation of the I-domain.     

Swinging into art with pendulum paintings

Children are captivated with how things work and they like to build things and in many ways, engineering comes naturally for them. Progress does not come from technology alone but from the melding of technology and creative thinking through art and design. There has been a push for STEAM-based curricula to be included in science classrooms and the Next Generation Science Standards (NGSS) provides the framework for integrating engineering design into the structure of science education. The push for the STEAM platform is derived from the lack of creativity and innovation in recent college graduates in the United States. This STEAM-based unit meshes engineering design, representing and interpreting data, visual arts, and motion/stability. As students investigated and analyzed pendulum motion, they also created unique pendulum paintings. Throughout this unit our students applied their content knowledge across several disciplines and in turn allowed them to gain a better understanding and retention of these concepts. Through creating their own pendulum paintings, the students learned about pendulums and how they work, designed and constructed their own pendulums, and applied prior knowledge of forces and motion in a controlled experiment.     

Simulation of the response of the inner hair cell stereocilia bundle to an acoustical stimulus

Mammalian hearing relies on a cochlear hydrodynamic sensor embodied in the inner hair cell stereocilia bundle. It is presumed that acoustical stimuli induce a fluid shear-driven motion between the tectorial membrane and the reticular lamina to deflect the bundle. It is hypothesized that ion channels are opened by molecular gates that sense tension in tip-links, which connect adjacent stepped rows of stereocilia. Yet almost nothing is known about how the fluid and bundle interact. Here we show using our microfluidics model how each row of stereocilia and their associated tip links and gates move in response to an acoustical input that induces an orbital motion of the reticular lamina. The model confirms the crucial role of the positioning of the tectorial membrane in hearing, and explains how this membrane amplifies and synchronizes the timing of peak tension in the tip links. Both stereocilia rotation and length change are needed for synchronization of peak tip link tension. Stereocilia length change occurs in response to accelerations perpendicular to the oscillatory fluid shear flow. Simulations indicate that nanovortices form between rows to facilitate diffusion of ions into channels, showing how nature has devised a way to solve the diffusive mixing problem that persists in engineered microfluidic devices.     

The neural encoding of self-motion

As we move through the world, information can be combined from multiple sources in order to allow us to perceive our self-motion. The vestibular system detects and encodes the motion of the head in space. In addition, extra-vestibular cues such as retinal-image motion (optic flow), proprioception, and motor efference signals, provide valuable motion cues. Here I focus on the coding strategies that are used by the brain to create neural representations of self-motion. I review recent studies comparing the thresholds of single versus populations of vestibular afferent and central neurons. I then consider recent advances in understanding the brain's strategy for combining information from the vestibular sensors with extra-vestibular cues to estimate self-motion. These studies emphasize the need to consider not only the rules by which multiple inputs are combined, but also how differences in the behavioral context govern the nature of what defines the optimal computation.     

Kinetic relationship between the voltage sensor and the activation gate in spHCN channels

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are activated by membrane hyperpolarizations that cause an inward movement of the positive charges in the fourth transmembrane domain (S4), which triggers channel opening. The mechanism of how the motion of S4 charges triggers channel opening is unknown. Here, we used voltage clamp fluorometry (VCF) to detect S4 conformational changes and to correlate these to the different activation steps in spHCN channels. We show that S4 undergoes two distinct conformational changes during voltage activation. Analysis of the fluorescence signals suggests that the N-terminal region of S4 undergoes conformational changes during a previously characterized mode shift in HCN channel voltage dependence, while a more C-terminal region undergoes an additional conformational change during gating charge movements. We fit our fluorescence and ionic current data to a previously proposed 10-state allosteric model for HCN channels. Our results are not compatible with a fast S4 motion and rate-limiting channel opening. Instead, our data and modeling suggest that spHCN channels open after only two S4s have moved and that S4 motion is rate limiting during voltage activation of spHCN channels.     

Looking at Op Art from a computational viewpoint

Arts history tells an exciting story about repeated attempts to represent features that are crucial for the understanding of our environment and which, at the same time, go beyond the inherently two-dimensional nature of a flat painting surface: depth and motion. In the twentieth century, Op artists such as Bridget Riley began to experiment with simple black and white patterns that do not represent motion in an artistic way but actually create vivid dynamic illusions in static pictures. The cause of motion illusions in such paintings is still a matter of debate. The role of involuntary eye movements in this phenomenon is studied here with a computational approach. The possible consequences of shifting the retinal image of synthetic wave gratings, dubbed as 'riloids', were analysed by a two-dimensional array of motion detectors (2DMD model), which generates response maps representing the spatial distribution of motion signals generated by such a stimulus. For a two-frame sequence reflecting a saccadic displacement, these motion signal maps contain extended patches in which local directions change only little. These directions, however, do not usually precisely correspond to the direction of pattern displacement that can be expected from the geometry of the curved gratings as an instance of the so-called 'aperture problem'. The patchy structure of the simulated motion detector response to the displacement of riloids resembles the motion illusion, which is not perceived as a coherent shift of the whole pattern but as a wobbling and jazzing of ill-defined regions. Although other explanations are not excluded, this might support the view that the puzzle of Op Art motion illusions could potentially have an almost trivial solution in terms of small involuntary eye movement leading to image shifts that are picked up by well-known motion detectors in the early visual system. This view can have further consequences for our understanding of how the human visual system usually compensates for eye movements, in order to let us perceive a stable world despite continuous image shifts generated by gaze instability.     

Actin filament elasticity and retrograde flow shape the force-velocity relation of motile cells

Cells migrate through a crowded environment during processes such as metastasis or wound healing, and must generate and withstand substantial forces. The cellular motility responses to environmental forces are represented by their force-velocity relation, which has been measured for fish keratocytes but remains unexplained. Even pN opposing forces slow down lamellipodium motion by three orders of magnitude. At larger opposing forces, the retrograde flow of the actin network accelerates until it compensates for polymerization, and cell motion stalls. Subsequently, the lamellipodium adapts to the stalled state. We present a mechanism quantitatively explaining the cell's force-velocity relation and its changes upon application of drugs that hinder actin polymerization or actomyosin-based contractility. Elastic properties of filaments, close to the lamellipodium leading edge, and retrograde flow shape the force-velocity relation. To our knowledge, our results shed new light on how these migratory responses are regulated, and on the mechanics and structure of the lamellipodium.     

A kinetic model describing the processivity of myosin-V

The precise details of how myosin-V coordinates the biochemical reactions and mechanical motions of its two head elements to engineer effective processive molecular motion along actin filaments remain unresolved. We compare a quantitative kinetic model of the myosin-V walk, consisting of five basic states augmented by two further states to allow for futile hydrolysis and detachments, with experimental results for run lengths, velocities, and dwell times and their dependence on bulk nucleotide concentrations and external loads in both directions. The model reveals how myosin-V can use the internal strain in the molecule to synchronize the motion of the head elements. Estimates for the rate constants in the reaction cycle and the internal strain energy are obtained by a computational comparison scheme involving an extensive exploration of the large parameter space. This scheme exploits the fact that we have obtained analytic results for our reaction network, e.g., for the velocity but also the run length, diffusion constant, and fraction of backward steps. The agreement with experiment is often reasonable but some open problems are highlighted, in particular the inability of such a general model to reproduce the reported dependence of run length on ADP concentration. The novel way that our approach explores parameter space means that any confirmed discrepancies should give new insights into the reaction network model.     

Activated membrane patches guide chemotactic cell motility

Many eukaryotic cells are able to crawl on surfaces and guide their motility based on environmental cues. These cues are interpreted by signaling systems which couple to cell mechanics; indeed membrane protrusions in crawling cells are often accompanied by activated membrane patches, which are localized areas of increased concentration of one or more signaling components. To determine how these patches are related to cell motion, we examine the spatial localization of RasGTP in chemotaxing Dictyostelium discoideum cells under conditions where the vertical extent of the cell was restricted. Quantitative analyses of the data reveal a high degree of spatial correlation between patches of activated Ras and membrane protrusions. Based on these findings, we formulate a model for amoeboid cell motion that consists of two coupled modules. The first module utilizes a recently developed two-component reaction diffusion model that generates transient and localized areas of elevated concentration of one of the components along the membrane. The activated patches determine the location of membrane protrusions (and overall cell motion) that are computed in the second module, which also takes into account the cortical tension and the availability of protrusion resources. We show that our model is able to produce realistic amoeboid-like motion and that our numerical results are consistent with experimentally observed pseudopod dynamics. Specifically, we show that the commonly observed splitting of pseudopods can result directly from the dynamics of the signaling patches.     

Mantisbot is a robotic model of visually guided motion in the praying mantis

Insects use highly distributed nervous systems to process exteroception from head sensors, compare that information with state-based goals, and direct posture or locomotion toward those goals. To study how descending commands from brain centers produce coordinated, goal-directed motion in distributed nervous systems, we have constructed a conductance-based neural system for our robot MantisBot, a 29 degree-of-freedom, 13.3:1 scale praying mantis robot. Using the literature on mantis prey tracking and insect locomotion, we designed a hierarchical, distributed neural controller that establishes the goal, coordinates different joints, and executes prey-tracking motion. In our controller, brain networks perceive the location of prey and predict its future location, store this location in memory, and formulate descending commands for ballistic saccades like those seen in the animal. The descending commands are simple, indicating only 1) whether the robot should walk or stand still, and 2) the intended direction of motion. Each joint's controller uses the descending commands differently to alter sensory-motor interactions, changing the sensory pathways that coordinate the joints' central pattern generators into one cohesive motion. Experiments with one leg of MantisBot show that visual input produces simple descending commands that alter walking kinematics, change the walking direction in a predictable manner, enact reflex reversals when necessary, and can control both static posture and locomotion with the same network.     

How long does DNA keep the memory of its conformation? A time-dependent canonical correlation analysis of molecular dynamics simulation

The time dependence of the correlation between motions of different parts of DNA is analyzed from a 200 ps molecular dynamics simulation of the double-stranded self-complementary d(CTGATCAG) in the B form. Each nucleotide is decomposed into three subunits corresponding to the furanose ring (SU), the base (BA), and the backbone (SK). The motion of each subunit is considered as the superimposition of rigid body translation, rigid body rotation, and internal deformation. Canonical time-dependent correlation functions calculated with coordinates describing the different components of the subunits motion are defined and computed. This allows us to probe how long a particular type of motion of one subunit influences the other types of motions of other subunits (cross correlation functions) or how long a particular subunit keeps the memory of its own conformation or location (autocorrelation functions). From auto-correlation analysis it is found that deformation decorrelates within a few tenths of picoseconds, rotational correlation times are on the order of 8 ps, while translational motions are long-time correlated. The deformation of a subunit is not correlated to the deformation of another one (at the 200 ps time scale of our simulation), but influences slightly their translation and orientation as time increases. © 1996 John Wiley & Sons, Inc.