ORIENTING IN 3D SPACE: BEHAVIORAL AND NEUROPHYSIOLOGICAL STUDIES IN BIG BROWN BATS

In their natural environment, animals engage in a wide range of behavioral tasks that require them to orient to stimuli in three-dimensional space, such as navigating around obstacles, reaching for objects and escaping from predators. Echolocating bats, for example, have evolved a high-resolution 3D acoustic orienting system that allows them to localize and track small moving targets in azimuth, elevation and range. The bat’s active control over the features of its echolocation signals contributes directly to the information represented in its sonar receiver, and its adaptive adjustments in sonar signal design provide a window into the acoustic features that are important for different behavioral tasks. When bats inspect sonar objects and require accurate 3D localization of targets, they produce sonar sound groups (SSGs), which are clusters of sonar calls produced at short intervals and flanked by long interval calls. SSGs are hypothesized to enhance the bat’s range resolution, but this hypothesis has not been directly tested. We first, in Chapter 2, provide a comprehensive comparison of SSG production of bats flying in the field and in the lab under different environmental conditions. Further, in Chapter 3, we devise an experiment to specifically compare SSG production under conditions when target motion is predictable and unpredictable, with the latter mimicking natural conditions where bats chase erratically moving prey. Data from both of these studies are consistent with the hypothesis that SSGs improve the bat’s spatio-temporal resolution of target range, and provide a behavioral foundation for the analysis and interpretation of neural recording data in chapters 4 and 6. The complex orienting behaviors exhibited by animals can be understood as a feedback loop between sensing and action. A primary brain structure involved in sensorimotor integration is the midbrain superior colliculus (SC). The SC is a widely studied brain region and has been implicated in species-specific orienting behaviors. However, most studies of the SC have investigated its functional organization using synthetic 2D (azimuth and elevation) stimuli in restrained animals, leaving gaps in our knowledge of how 3D space (azimuth, elevation and distance) is represented in the CNS. In contrast, the representation of stimulus distance in the auditory systems of bats has been widely studied. Almost all of these studies have been conducted in passively listening bats, thus severing the loop between sensing and action and leaving gaps in our knowledge regarding how target distance is represented in the auditory system of actively echolocating bats. In chapters 4, 5 and 6, we attempt to fill gaps in our knowledge by recording from the SC of free flying echolocating bats engaged in a naturalistic navigation task where bats produce SSGs. In chapter 4, we provide a framework to compute time-of-arrival and direction of the instantaneous echo stimuli received at the bats ears. In chapters 5 and 6, we provide an algorithm to classify neural activity in the SC as sensory, sensorimotor and premotor and then compute spatial receptive fields of SC neurons. Our results show that neurons in the SC of the free-flying echolocating bat respond selectively to stimulus azimuth, elevation and range. Importantly, we find that SC neuron response profiles are modulated by the bat’s behavioral state, indicated by the production of SSG. Broadly, we use both behavior and electrophysiology to understand the action-perception loop that supports spatial orientation by echolocation. We believe that the results and methodological advances presented here will open doors to further studies of sensorimotor integration in freely behaving animals

Integration of Clostridium thermocellum Consolidated Bioprocessing With Thermochemical Pretreatments for Fuel Ethanol Production From Switchgrass

There is an urgent need to replace petroleum-based transportation fuels with renewable and sustainable fuels to reduce the deteriorating impact of greenhouse gas emissions on climate change. Biofuels would not only provide a sustainable energy source but also help countries reduce their dependence on imported petroleum. Ethanol made from corn starch and cane sugar is presently the largest biotechnology-based product and commands a large share of the alternative fuels market. However, it is important to move towards making ethanol from lignocellulosic biomass that, unlike corn starch and cane sugar, does not have an important alternative use as food. However, biological conversion of this plentiful material suffers from high enzyme costs that stymie competitiveness. Clostridium thermocellum is a multifunctional ethanol producer capable of enzyme production, enzymatic saccharification, and fermentation that is fundamental to the consolidated bioprocessing (CBP) approach of ethanol production from lignocellulosic biomass. CBP eliminates the supplementation of expensive enzymes that are required in the traditional approach of ethanol production. However, the recalcitrance of lignocellulosic biomass is still a hindrance to effective ethanol production. C. thermocellum is unable to achieve complete biomass digestion and sugar release without pretreatment of lignocellulosic biomass. This work focuses on extensive process development for effective integration of CBP with four different thermochemical pretreatments of switchgrass. First, cellulose loading for C. thermocellum flask fermentations was optimized to understand the impacts of substrate structural features on digestion by C. thermocellum under non-inhibitory conditions. Next, the impact of various cellulose properties including, but not limited to, crystallinity, surface area, pore size, and degree of polymerization, was studied on C. thermocellum digestion of model cellulosic substrates compared to fungal enzymatic hydrolysis. With an extensive understanding of C. thermocellum fermentations on model substrates the interdependency of switchgrass structural features with thermochemical and biological digestion was studied. Process configurations to achieve complete cellulose solubilization and total sugar release from switchgrass were finally defined. The comprehensive nature of integration of four different thermochemical and two different biological approaches in this work is unparalleled and could provide a platform to systematically develop cost effective ethanol production from lignocellulosic biomass in the future

Action Enhances Acoustic Cues for 3-D Target Localization by Echolocating Bats

<div><p>Under natural conditions, animals encounter a barrage of sensory information from which they must select and interpret biologically relevant signals. Active sensing can facilitate this process by engaging motor systems in the sampling of sensory information. The echolocating bat serves as an excellent model to investigate the coupling between action and sensing because it adaptively controls both the acoustic signals used to probe the environment and movements to receive echoes at the auditory periphery. We report here that the echolocating bat controls the features of its sonar vocalizations in tandem with the positioning of the outer ears to maximize acoustic cues for target detection and localization. The bat’s adaptive control of sonar vocalizations and ear positioning occurs on a millisecond timescale to capture spatial information from arriving echoes, as well as on a longer timescale to track target movement. Our results demonstrate that purposeful control over sonar sound production and reception can serve to improve acoustic cues for localization tasks. This finding also highlights the general importance of movement to sensory processing across animal species. Finally, our discoveries point to important parallels between spatial perception by echolocation and vision.</p></div

Action Enhances Acoustic Cues for 3-D Target Localization by Echolocating Bats - Fig 4

<p>(A) Top, normalized inter-pinna separations for one-target simple motion (blue) versus two-target simple motions (green) combined across bats. Plotted are the mean +/- s.e.m. of the inter-pinna separation distances as a function of target distance. Bottom, dʹ calculation, or discriminability index, between one-target simple motion and two-target simple motion inter-pinna separation. Red shaded region indicates dʹ values above the 95% confidence interval as determined by a permutation test, indicating time points of significant differences in inter-pinna separation. (B) Top, normalized inter-pinna separations for one-target simple motion (blue) versus two-target pass motions (black) combined across bats. Bottom, dʹ calculation between one-target simple and two-target pass motion conditions. Red shaded region indicates significant differences. (C) Top, normalized inter-pinna separations for two-target simple motion (green) versus two-target pass motions (black) combined across bats. Bottom, dʹ calculation between two-target simple and two-target pass motion conditions. Red shaded region indicates significant differences. Data for this figure can be found at <a href="http://dx.doi.org/10.7281/T1W66HPZ" target="_blank">http://dx.doi.org/10.7281/T1W66HPZ</a>.</p

Changes in pinna separation with target distance for each target motion condition (colors as in Fig 1).

<p>(A) Top left, change in inter-pinna separation as a function of target distance for one-target simple motion. Plotted is the mean +/- s.e.m. for all trials. Values are normalized to zero for the starting position at the beginning of each trial. Top right, normalized data for two-target same motions; bottom left, normalized data for two-target pass motions (asterisks indicate when the second target begins to move, and when it overtakes the first target); bottom right, normalized data for one-target complex motions (asterisks indicate times when the target changes motion direction). (B) Normalized inter-pinna separation as a function of target distance for all varieties of target motion. There is a significant correlation between decreasing target distance and increasing pinna separation (Pearson’s correlation, r = 0.45, <i>p</i> < 0.0001). Inset details inter-pinna separation measurement. Data for this figure can be found at <a href="http://dx.doi.org/10.7281/T1W66HPZ" target="_blank">http://dx.doi.org/10.7281/T1W66HPZ</a>.</p