Environmental Clutter Elicits Behavioral Adaptations During Natural Behaviors in Echolocating Bats

The natural environment is filled with clutter that creates challenges for animals participating in routine tasks like orientation, foraging, and communication. For echolocating bats that primarily rely on an active sensory system, the effects of acoustic and physical clutter become more prominent with the potential to completely degrade the individuals’ ability effectively navigate their environment or engage in prey capture. From sonar jamming moths and competitive conspecifics to rapid prey pursuit in dense forests, bats must quickly adapt their sensory-guided flight behaviors in real-time to remain effective aerial predators. To explore the sources of acoustic clutter and their effects on natural bat behaviors, a literature review is presented on the historical and current perspectives on sonar jamming and the underlying mechanisms of the jamming avoidance response. This is followed by experimental evidence of bats making use of a jamming avoidance response when presented with playback of heterospecific bat calls thought to decrease foraging efficacy. Bats were found to significantly alter their individual echolocation call features in a manner that is thought to improve the signal-to-noise ratio, which would aid in the increased detection of their own echoes. The remaining chapters explore how bats might make use of multisensory cues when environmental conditions are unfavorable for echolocation alone and how bats adjust their flight strategies when navigating in a novel environment filled with physical clutter. These chapters respectively report that multimodal cues comprised of visual and acoustic information lead to enhancement of responses during an obstacle avoidance task and that significant changes in flight kinematics can be observed in cluttered vs. open environments

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent ergodic motion control guided by a gradient of a potential field. A finite element method is hereby implemented to obtain a solution of Helmholtz partial differential equation, which models the potential field for surveying motion control. This allows us to survey arbitrarily shaped domains and to include obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental idea. For a simple kinematic motion, the obstacles and boundary avoidance constraints are successfully handled by directing the agent motion with the gradient of the potential. However, including additional constraints, such as the minimal clearance dsitance from stationary and moving obstacles and the minimal path curvature radius, requires further alternations of the control algorithm. We introduce a relatively simple yet robust approach for handling these constraints by formulating a straightforward optimization problem based on collision-free escapes route maneuvers. This approach provides a guaranteed collision avoidance mechanism, while being computationally inexpensive as a result of the optimization problem partitioning. The proposed motion control is evaluated in three realistic surveying scenarios simulations, showing the effectiveness of the surveying and the robustness of the control algorithm. Furthermore, potential maneuvering difficulties due to improperly defined surveying scenarios are highlighted and we provide guidelines on how to overpass them. The results are promising and indiacate real-world applicability of proposed constrained multi-agent motion control for autonomous surveying and potentially other HEDAC utilizations.Comment: Revised manuscrip

Spatial processing of conspecific signals in weakly electric fish: from sensory image to neural population coding

In this dissertation, I examine how an animal’s nervous system encodes spatially realistic conspecific signals in their environment and how the encoding mechanisms support behavioral sensitivity. I begin by modeling changes in the electrosensory signals exchanged by weakly electric fish in a social context. During this behavior, I estimate how the spatial structure of conspecific stimuli influences sensory responses at the electroreceptive periphery. I then quantify how space is represented in the hindbrain, specifically in the primary sensory area called the electrosensory lateral line lobe. I show that behavioral sensitivity is influenced by the heterogeneous properties of the pyramidal cell population. I further demonstrate that this heterogeneity serves to start segregating spatial and temporal information early in the sensory pathway. Lastly, I characterize the accuracy of spatial coding in this network and predict the role of network elements, such as correlated noise and feedback, in shaping the spatial information. My research provides a comprehensive understanding of spatial coding in the first stages of sensory processing in this system and allows us to better understand how network dynamics shape coding accuracy

Distributed sensing in flexible robotic fins: propulsive force prediction and underwater contact sensing

There is recent biological evidence that the pectoral fins of bluegill sunfish are innervated with nerves that respond to bending, and these fish contact obstacles with their fins. However, it is not known how fin-intrinsic sensing could be used to mediate propulsion and touch in engineered fins. The objective of this thesis is to understand the use of distributed sensing in robotic fins, inspired by bony fish fins, for the prediction of propulsive forces and for the discrimination between fluidic loading and contact loading during underwater touch. The research integrates engineering and biology and builds an understanding of fin-intrinsic sensing through study of swimming fish and robotic models of fish fins and sensors. Multiple studies identify which sensor types, sensor placement locations, and model conditions are best for predicting fin propulsive forces and for predicting the state of contact. Comparisons are made between linear and nonlinear Volterra-series convolution models to represent the mapping from sensory data to forces. Best practices for instrumentation and model selection are extracted for a broad range of swimming conditions on a complex, multi-DOF, flexible fin. This knowledge will guide the development of multi-functional systems to navigate and propel through complex, occluded, underwater environments and for sensing and responding to environmental perturbations and obstacles.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

MECHANOSENSORY FEEDBACK FOR FLIGHT CONTROL AND PREY CAPTURE IN THE ECHOLOCATING BAT

Throughout the animal kingdom, organisms have evolved neural systems that process biologically relevant stimuli to guide a wide range of species-specific behaviors. Bats, comprising 25% of mammalian species, rely on diverse sensory modalities to carry out tasks such as foraging, obstacle avoidance and social communication. While it is well known that many bat species use echolocation to find food and steer around obstacles, they also depend on other senses. For instance, some bats predominantly use vision to navigate, and others use olfaction to find food sources. In addition, bats rely on airflow sensors to stabilize their flight, primarily through signals carried by microscopic hairs embedded in their wings and tail membranes. Studies have shown that bats performing an obstacle avoidance task show changes in their flight behavior when dorsal wing hairs are removed. Additionally, electrophysiological studies have shown that wing hairs are involved in airflow sensing, but little is known about the contribution of sensory hairs on the ventral surfaces of the wing and tail membranes to their flight control and other complex behaviors, such as prey handling. Chapter 1 of my dissertation presents a general introduction to bat echolocation, flight kinematics, and airflow sensing for flight control. In Chapter 2, I review sensory hairs across the animal kingdom, from invertebrates to vertebrates. I discuss the role of sensory hairs for functions ranging from detection to locomotion and propose the use and benefit of mechanosensors in biologically-inspired technology. In Chapter 3, I devised an experiment to evaluate changes in capture success, as well changes in flight kinematics and adaptive sonar behavior, before and after depilation of sensory hairs in order to ascertain if these sensory hairs have a functional role in both airflow sensing for flight control and tactile sensing for prey handling. In Chapter 4, I designed an experiment aimed at determining if firing patterns of S1 neurons change with airflow speed and angle of attack and if wing hair depilation affects S1 responses to whole wing stimulation. To answer these questions, I record neural activity in S1 of sedated big brown bats while the entire contralateral wing is systematically exposed to naturalistic airflow in a wind tunnel. Finally, in Chapter 5, I address open questions that remain, present experiments aimed at filling these gaps, and consider key points important for future work

Capacitive Sensing and Communication for Ubiquitous Interaction and Environmental Perception

During the last decade, the functionalities of electronic devices within a living environment constantly increased. Besides the personal computer, now tablet PCs, smart household appliances, and smartwatches enriched the technology landscape. The trend towards an ever-growing number of computing systems has resulted in many highly heterogeneous human-machine interfaces. Users are forced to adapt to technology instead of having the technology adapt to them. Gathering context information about the user is a key factor for improving the interaction experience. Emerging wearable devices show the benefits of sophisticated sensors which make interaction more efficient, natural, and enjoyable. However, many technologies still lack of these desirable properties, motivating me to work towards new ways of sensing a user's actions and thus enriching the context. In my dissertation I follow a human-centric approach which ranges from sensing hand movements to recognizing whole-body interactions with objects. This goal can be approached with a vast variety of novel and existing sensing approaches. I focused on perceiving the environment with quasi-electrostatic fields by making use of capacitive coupling between devices and objects. Following this approach, it is possible to implement interfaces that are able to recognize gestures, body movements and manipulations of the environment at typical distances up to 50cm. These sensors usually have a limited resolution and can be sensitive to other conductive objects or electrical devices that affect electric fields. The technique allows for designing very energy-efficient and high-speed sensors that can be deployed unobtrusively underneath any kind of non-conductive surface. Compared to other sensing techniques, exploiting capacitive coupling also has a low impact on a user's perceived privacy. In this work, I also aim at enhancing the interaction experience with new perceptional capabilities based on capacitive coupling. I follow a bottom-up methodology and begin by presenting two low-level approaches for environmental perception. In order to perceive a user in detail, I present a rapid prototyping toolkit for capacitive proximity sensing. The prototyping toolkit shows significant advancements in terms of temporal and spatial resolution. Due to some limitations, namely the inability to determine the identity and fine-grained manipulations of objects, I contribute a generic method for communications based on capacitive coupling. The method allows for designing highly interactive systems that can exchange information through air and the human body. I furthermore show how human body parts can be recognized from capacitive proximity sensors. The method is able to extract multiple object parameters and track body parts in real-time. I conclude my thesis with contributions in the domain of context-aware devices and explicit gesture-recognition systems

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong