Yossi Yovel

Axcaliber: A method for measuring axon diameter distribution from diffusion MRI

The diameter of a myelinated nerve axon is directly proportional to its conduction velocity, so the axon diameter distribution helps determine the channel capacity of nervous transmission along fascicles in the central (CNS) and peripheral nervous systems (PNS). Previously, this histological information could only be obtained using invasive tissue biopsies. Here we propose a new NMR‐based approach that employs a model of water diffusion within “restricted” cylindrical axons to estimate their diameter distribution within a nerve bundle. This approach can be combined with MRI to furnish an estimate of the axon diameter distribution within each voxel. This method is validated by comparing the diameter distributions measured using the NMR and histological techniques on sciatic and optic nerve tissue specimens. The axon diameter distribution measured in each voxel of porcine spinal cord using MRI and using histological methods were similar. Applications are expected in longitudinal studies designed to follow nerve growth in normal and abnormal development, as well as in diagnosing disorders and diseases affecting specific populations of axons in the CNS and PNS. Magn Reson Med 59:1347–1354, 2008.

Plant Classification from Bat-Like Echolocation Signals

Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.

Optimal Localization by Pointing Off Axis

Detection Versus Localization Active-sensing systems allow an animal some control over the information acquired from the environment. For example, echolocating bats are known to control many aspects of their sonar signal design. However, their sensory data-acquisition strategies are still not fully understood. Yovel et al. (p. 701) found that Egyptian fruit bats (Rousettus aegyptiacus) did not aim their sonar beams directly on target in a standard localization task. Instead, the bats pointed the main lobe of their sonar beam off-axis. Bats approaching the target emitted sounds to the left and right of its localization, in an alternating manner, so that the maximum slope of the change in intensity of the sonar beam was often positioned close to the target. This echolocation strategy is ideal for target localization but occurs at the cost of target detection. Echolocating Egyptian fruit bats do not center their sonar clicks on a target, thereby maximizing localization of the target. Is centering a stimulus in the field of view an optimal strategy to localize and track it? We demonstrated, through experimental and computational studies, that the answer is no. We trained echolocating Egyptian fruit bats to localize a target in complete darkness, and we measured the directional aim of their sonar clicks. The bats did not center the sonar beam on the target, but instead pointed it off axis, accurately directing the maximum slope (“edge”) of the beam onto the target. Information-theoretic calculations showed that using the maximum slope is optimal for localizing the target, at the cost of detection. We propose that the tradeoff between detection (optimized at stimulus peak) and localization (optimized at maximum slope) is fundamental to spatial localization and tracking accomplished through hearing, olfaction, and vision.

The Voice of Bats: How Greater Mouse-eared Bats Recognize Individuals Based on Their Echolocation Calls

Echolocating bats use the echoes from their echolocation calls to perceive their surroundings. The ability to use these continuously emitted calls, whose main function is not communication, for recognition of individual conspecifics might facilitate many of the social behaviours observed in bats. Several studies of individual-specific information in echolocation calls found some evidence for its existence but did not quantify or explain it. We used a direct paradigm to show that greater mouse-eared bats (Myotis myotis) can easily discriminate between individuals based on their echolocation calls and that they can generalize their knowledge to discriminate new individuals that they were not trained to recognize. We conclude that, despite their high variability, broadband bat-echolocation calls contain individual-specific information that is sufficient for recognition. An analysis of the call spectra showed that formant-related features are suitable cues for individual recognition. As a model for the bats decision strategy, we trained nonlinear statistical classifiers to reproduce the behaviour of the bats, namely to repeat correct and incorrect decisions of the bats. The comparison of the bats with the model strongly implies that the bats are using a prototype classification approach: they learn the average call characteristics of individuals and use them as a reference for classification.

It's not black or white—on the range of vision and echolocation in echolocating bats

Around 1000 species of bats in the world use echolocation to navigate, orient, and detect insect prey. Many of these bats emerge from their roost at dusk and start foraging when there is still light available. It is however unclear in what way and to which extent navigation, or even prey detection in these bats is aided by vision. Here we compare the echolocation and visual detection ranges of two such species of bats which rely on different foraging strategies (Rhinopoma microphyllum and Pipistrellus kuhlii). We find that echolocation is better than vision for detecting small insects even in intermediate light levels (1–10 lux), while vision is advantageous for monitoring far-away landscape elements in both species. We thus hypothesize that, bats constantly integrate information acquired by the two sensory modalities. We suggest that during evolution, echolocation was refined to detect increasingly small targets in conjunction with using vision. To do so, the ability to hear ultrasonic sound is a prerequisite which was readily available in small mammals, but absent in many other animal groups. The ability to exploit ultrasound to detect very small targets, such as insects, has opened up a large nocturnal niche to bats and may have spurred diversification in both echolocation and foraging tactics.

On-board recordings reveal no jamming avoidance in wild bats

Animals often deal with situations in which vast sensory input is received simultaneously. They therefore must possess sophisticated mechanisms to select important input and ignore the rest. In bat echolocation, this problem is at its extreme. Echolocating bats emit sound signals and analyse the returning echoes to sense their environment. Bats from the same species use signals with similar frequencies. Nearby bats therefore face the difficulty of distinguishing their own echoes from the signals of other bats, a problem often referred to as jamming. Because bats commonly fly in large groups, jamming might simultaneously occur from numerous directions and at many frequencies. Jamming is a special case of the general phenomenon of sensory segregation. Another well-known example is the human problem of following conversation within a crowd. In both situations, a flood of auditory incoming signals must be parsed into important versus irrelevant information. Here, we present a novel method, fitting wild bats with a miniature microphone, which allows studying jamming from the bats ‘point of view’. Previous studies suggested that bats deal with jamming by shifting their echolocation frequency. On-board recordings suggest otherwise. Bats shifted their frequencies, but they did so because they were responding to the conspecifics as though they were nearby objects rather than avoiding being jammed by them. We show how bats could use alternative measures to deal with jamming instead of shifting their frequency. Despite its intuitive appeal, a spectral jamming avoidance response might not be the prime mechanism to avoid sensory interference from conspecifics.

What a Plant Sounds Like: The Statistics of Vegetation Echoes as Received by Echolocating Bats

A critical step on the way to understanding a sensory system is the analysis of the input it receives. In this work we examine the statistics of natural complex echoes, focusing on vegetation echoes. Vegetation echoes constitute a major part of the sensory world of more than 800 species of echolocating bats and play an important role in several of their daily tasks. Our statistical analysis is based on a large collection of plant echoes acquired by a biomimetic sonar system. We explore the relation between the physical world (the structure of the plant) and the characteristics of its echo. Finally, we complete the story by analyzing the effect of the sensory processing of both the echolocation and the auditory systems on the echoes and interpret them in the light of information maximization. The echoes of all different plant species we examined share a surprisingly robust pattern that was also reproduced by a simple Poisson model of the spatial reflector arrangement. The fine differences observed between the echoes of different plant species can be explained by the spatial characteristics of the plants. The bats emitted signal enhances the most informative spatial frequency range where the species-specific information is large. The auditory system filtering affects the echoes in a similar way, thus enhancing the most informative spatial frequency range even more. These findings suggest how the bats sensory system could have evolved to deal with complex natural echoes.

Calling louder and longer: how bats use biosonar under severe acoustic interference from other bats

Active-sensing systems such as echolocation provide animals with distinct advantages in dark environments. For social animals, however, like many bat species, active sensing can present problems as well: when many individuals emit bio-sonar calls simultaneously, detecting and recognizing the faint echoes generated by ones own calls amid the general cacophony of the group becomes challenging. This problem is often termed ‘jamming’ and bats have been hypothesized to solve it by shifting the spectral content of their calls to decrease the overlap with the jamming signals. We tested bats’ response in situations of extreme interference, mimicking a high density of bats. We played-back bat echolocation calls from multiple speakers, to jam flying Pipistrellus kuhlii bats, simulating a naturally occurring situation of many bats flying in proximity. We examined behavioural and echolocation parameters during search phase and target approach. Under severe interference, bats emitted calls of higher intensity and longer duration, and called more often. Slight spectral shifts were observed but they did not decrease the spectral overlap with jamming signals. We also found that pre-existing inter-individual spectral differences could allow self-call recognition. Results suggest that the bats’ response aimed to increase the signal-to-noise ratio and not to avoid spectral overlap.

Virtual definition of neuronal tissue by cluster analysis of multi-parametric imaging (virtual-dot-com imaging)

Individual mapping of cerebral, morphological, functionally related structures using MRI was carried out using a new multi-contrast acquisition and analysis framework, called virtual-dot-com imaging. So far, conventional anatomical MRI has been able to provide gross segmentation of gray/white matter boundaries and a few sub-cortical structures. By combining a handful of imaging contrasts mechanisms (T1, T2, magnetization transfer, T2* and proton density), we were able to further segment sub-cortical tissue to its sub-nuclei arrangement, a segmentation that is difficult based on conventional, single-contrast MRI. Using an automatic four-step image and signal processing algorithm, we segmented the thalamus to at least 7 sub-nuclei with high similarity across subjects and high statistical significance within subjects (p<0.0001). The identified sub-nuclei resembled the known anatomical arrangement of the thalamus given in various atlases. Each cluster was characterized by a unique MRI contrast fingerprint. With this procedure, the weighted proportions of the different cellular compartments could be estimated, a property available to date only by histological analysis. Each sub-nucleus could be characterized in terms of normalized MRI contrast and compared to other sub-nuclei. The different weights of the contrasts (T1/T2/T2*/PD/MT, etc.) for each sub-nuclei cluster might indicate the intra-cluster morphological arrangement of the tissue that it represents. The implications of this methodology are far-ranging, from non-invasive, in vivo, individual mapping of histologically distinct brain areas to automatic identification of pathological processes.

Bats adjust their mouth gape to zoom their biosonar field of view

Significance This study shows how an animal actively and functionally controls its sensory field of view by means of changing its emitter aperture. This would be similar to a human ability to zoom in on a visual scene. The study was performed in a natural situation with wild bats. To perform the study, we developed tools to record and analyze bats’ beams and faces simultaneously. We show that bats rapidly and dramatically alter their biosonar field of view to functionally adjust sensory acquisition. They do so by changing their mouth gape. Hence, we show that bats change the shape of their emitter for active sensing. Active sensing, where sensory acquisition is actively modulated, is an inherent component of almost all sensory systems. Echolocating bats are a prime example of active sensing. They can rapidly adjust many of their biosonar parameters to optimize sensory acquisition. They dynamically adjust pulse design, pulse duration, and pulse rate within dozens of milliseconds according to the sensory information that is required for the task that they are performing. The least studied and least understood degree of freedom in echolocation is emission beamforming—the ability to change the shape of the sonar sound beam in a functional way. Such an ability could have a great impact on the bat’s control over its sensory perception. On the one hand, the bat could direct more energy into a narrow sector to zoom its biosonar field of view, and on the other hand, it could widen the beam to increase the space that it senses. We show that freely behaving bats constantly control their biosonar field of view in natural situations by rapidly adjusting their emitter aperture—the mouth gape. The bats dramatically narrowed the beam when entering a confined space, and they dramatically widened it within dozens of milliseconds when flying toward open space. Hence, mouth-emitting bats dynamically adjust their mouth gape to optimize the area that they sense with their echolocation system.