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An increasingly popular animal model for studying the neural basis of social behavior, cognition, and communication is the common marmoset (Callithrix jacchus). Interest in this New World primate across neuroscience is now being driven by their proclivity for prosociality across their repertoire, high volubility, and rapid development, as well as their amenability to naturalistic testing paradigms and freely moving neural recording and imaging technologies. The complement of these characteristics set marmosets up to be a powerful model of the primate social brain in the years to come. Here, we focus on vocal communication because it is the area that has both made the most progress and illustrates the prodigious potential of this species. We review the current state of the field with a focus on the various brain areas and networks involved in vocal perception and production, comparing the findings from marmosets to other animals, including humans.

[EN] Current methods used to quantify brain size and compartmental scaling relationships in studies of social insect brain evolution involve manual annotations of images from histological samples, confocal microscopy or other sources. This process is susceptible to human bias and error and requires time-consuming effort by expert annotators. Standardized brain atlases, constructed through 3D registration and automatic segmentation, surmount these issues while increasing throughput to robustly sample diverse morphological and behavioral phenotypes. Here we design and evaluate three strategies to construct statistical brain atlases, or templates, using ants as a model taxon. The first technique creates a template by registering multiple brains of the same species. Brain regions are manually annotated on the template, and the labels are transformed back to each individual brain to obtain an automatic annotation, or to any other brain aligned with the template. The second strategy also creates a template from multiple brain images but obtains labels as a consensus from multiple manual annotations of individual brains comprising the template. The third technique is based on a template comprising brains from multiple species and the consensus of their labels. We used volume similarity as a metric to evaluate the automatic segmentation produced by each method against the inter- and intra-individual variability of human expert annotators. We found that automatic and manual methods are equivalent in volume accuracy, making the template technique an extraordinary tool to accelerate data collection and reduce human bias in the study of the evolutionary neurobiology of ants and other insects.

[EN] Current methods used to quantify brain size and compartmental scaling relationships in studies of social insect brain evolution involve manual annotations of images from histological samples, confocal microscopy or other sources. This process is susceptible to human bias and error and requires time-consuming effort by expert annotators. Standardized brain atlases, constructed through 3D registration and automatic segmentation, surmount these issues while increasing throughput to robustly sample diverse morphological and behavioral phenotypes. Here we design and evaluate three strategies to construct statistical brain atlases, or templates, using ants as a model taxon. The first technique creates a template by registering multiple brains of the same species. Brain regions are manually annotated on the template, and the labels are transformed back to each individual brain to obtain an automatic annotation, or to any other brain aligned with the template. The second strategy also creates a template from multiple brain images but obtains labels as a consensus from multiple manual annotations of individual brains comprising the template. The third technique is based on a template comprising brains from multiple species and the consensus of their labels. We used volume similarity as a metric to evaluate the automatic segmentation produced by each method against the inter- and intra-individual variability of human expert annotators. We found that automatic and manual methods are equivalent in volume accuracy, making the template technique an extraordinary tool to accelerate data collection and reduce human bias in the study of the evolutionary neurobiology of ants and other insects.

Insects have evolved diverse and remarkable strategies for navigating in various ecologies all over the world. In particular, central place foragers, such as bees and ants, have become renowned for their fascinating navigational capabilities. At the heart of insect navigation lies a brain area known as the central complex (CX). Functionally, the CX integrates world-centric sensory information with self-motion cues to generate an internal map of angular position. It plays a role in driving motor commands and has been suggested as the neural substrate for encoding travel direction as well as navigational vectors theorized to be involved during path integration. Interestingly, the CX appears to have been highly anatomically conserved, even across insect species that diverged hundreds of millions of years ago. The conserved nature of the CX stands in juxtaposition to the fascinating diversity of insect behavior. How does a highly conserved brain area give rise to such diverse navigational behavior? Using block-face electron microscopy combined with neuron segmentation and synapse annotation, I analyzed CX circuits in six species of bees and ants: the honeybee, the bumblebee (Paper 1), the sweat bee, the army ant, the desert ant, and the bull ant. Our data suggests that there are core circuits that have been exceptionally well preserved across evolutionary time. Namely, the head direction circuit (Paper 2) which contains neurons that share total numbers, projectivity, and connectivity motifs from flies to bees and ants. In contrast, inputs from sensory areas vary to a much larger degree. Our data suggests that the relative contribution of parallel input pathways depends strongly on the information available in the habitat of a species. Also variable are the circuits that encode self-motion, something which is fundamental for building navigationally relevant internal representations (Paper 3). Altogether, these neuroanatomical maps provide the framework for future functio

[EN] Current methods used to quantify brain size and compartmental scaling relationships in studies of social insect brain evolution involve manual annotations of images from histological samples, confocal microscopy or other sources. This process is susceptible to human bias and error and requires time-consuming effort by expert annotators. Standardized brain atlases, constructed through 3D registration and automatic segmentation, surmount these issues while increasing throughput to robustly sample diverse morphological and behavioral phenotypes. Here we design and evaluate three strategies to construct statistical brain atlases, or templates, using ants as a model taxon. The first technique creates a template by registering multiple brains of the same species. Brain regions are manually annotated on the template, and the labels are transformed back to each individual brain to obtain an automatic annotation, or to any other brain aligned with the template. The second strategy also creates a template from multiple brain images but obtains labels as a consensus from multiple manual annotations of individual brains comprising the template. The third technique is based on a template comprising brains from multiple species and the consensus of their labels. We used volume similarity as a metric to evaluate the automatic segmentation produced by each method against the inter- and intra-individual variability of human expert annotators. We found that automatic and manual methods are equivalent in volume accuracy, making the template technique an extraordinary tool to accelerate data collection and reduce human bias in the study of the evolutionary neurobiology of ants and other insects.

Specific mate communication and recognition underlies reproduction and hence speciation. Our study provides new insights in Drosophila melanogaster premating olfactory communication. Mate communication evolves during adaptation to ecological niches and makes use of social signals and habitat cues. Female-produced, species-specific volatile pheromone (Z)-4-undecenal (Z4-11Al) and male pheromone (Z)-11-octadecenyl acetate (cVA) interact with food odour in a sex-specific manner. Furthermore, Z4-11Al, which mediates upwind flight attraction in both sexes, also elicits courtship in experienced males. Two isoforms of the olfactory receptor Or69a are co-expressed in the same olfactory sensory neurons. Z4-11Al is perceived via Or69aB, while the food odorant (R)-linalool is a main ligand for the other variant, Or69aA. However, only Z4-11Al mediates courtship in experienced males, not (R)-linalool. Behavioural discrimination is reflected by calcium imaging of the antennal lobe, showing distinct glomerular activation patterns by these two compounds. Male sex pheromone cVA is known to affect male and female courtship at close range, but does not elicit upwind flight attraction as a single compound, in contrast to Z4-11Al. A blend of the food odour vinegar and cVA attracted females, while a blend of vinegar and female pheromone Z4-11Al attracted males, instead. Sex-specific upwind flight attraction to blends of food volatiles and male and female pheromone, respectively, adds a new element to Drosophila olfactory premating communication and is an unambiguous paradigm for identifying the behaviourally active components, towards a more complete concept of food-pheromone odour objects.

Our interactions with the world unfold over time. Whether it's speaking, where one word follows the next, or walking, where each step follows another, the organization of our behaviors in time tends to follow a predictable pattern. Those patterns are dictated by a multitude of underlying factors, influenced both by endogenous physiological factors like the rhythmic nature of our gait as well as by exogenous factors, like the social dynamics underlying turn-taking while speaking. Despite decades of research studying the temporal organization of behavior, dating back to the work of influential biologists like Tinbergen, Lashley, and Dawkins, little is known about the physiological substrates that underlie either the production of the sequential organization of most aspects of behavior. Despite widespread acknowledgment that physiological motor programs and many non-linguistic behaviors are hierarchical, for example, few physiological investigations into the dynamics of behavior extend beyond low-order (Markovian) transition statistics. In this thesis, I build onto the emerging field of computational neuroethology to further our understanding of what structure underlies the sequential organization of behavior, what physiological mechanisms might be involved in producing, perceiving, and representing sequential behavioral organization, and how sequential behavioral organization might have emerged developmentally and evolutionarily. Throughout the thesis, I draw primarily upon birdsong and human speech, developing methods to analyze the acoustic and temporal structure in vocal signals and then behaviorally and physiologically probing the underpinnings of sequential organization in the songbird. This work advances the field of computational neuroethology in several ways.I uncover novel acoustic structure in vocal signals separating avian and mammalian vocalizations along a spectrum of vocal stereotypy. I observe that both human speech and birdsong are characterized by a c

Altres ajuts: This work was funded by UAB-GE-260408 to L.G.-L.It also received financial support from Memorial Mercedes Llort Sender 2021/80/09241941.6., The marble burying (MB) test, a classical test based on the natural tendency of rodents to dig in diverse substrates and to bury small objects, is sensitive to some intrinsic and extrinsic factors. Here, under emerging neuroethological quantitative and qualitative analysis, the MB performance of 12-month-old male and female 3xTg-AD mice for Alzheimer's disease and age-matched counterparts of gold-standard C57BL6 strain with normal aging unveiled sex-dependent signatures. In addition, three temporal analyses, through the (1) time course of the performance, and (2) a repeated test schedule, identified the optimal time frames and schedules to detect sex-and genotype-dependent differences. Besides, a (3) longitudinal design from 12 to 16 months of age monitored the changes in the performance with aging, worsening in AD-mice, and modulation through the repeated test. In summary, the present results allow us to conclude that (1) the marble burying test is responsive to genotype, sex, aging, and its interactions; (2) the male sex was more sensitive to showing the AD-phenotype; (3) longitudinal assessment shows a reduction in females with AD pathology; (4) burying remains stable in repeated testing; (5) the time-course of marbles burying is useful; and (6) burying behavior most likely represents perseverative and/or stereotyped-like behavior rather than anxiety-like behavior in 3xTg-AD mice.

Our interactions with the world unfold over time. Whether it's speaking, where one word follows the next, or walking, where each step follows another, the organization of our behaviors in time tends to follow a predictable pattern. Those patterns are dictated by a multitude of underlying factors, influenced both by endogenous physiological factors like the rhythmic nature of our gait as well as by exogenous factors, like the social dynamics underlying turn-taking while speaking. Despite decades of research studying the temporal organization of behavior, dating back to the work of influential biologists like Tinbergen, Lashley, and Dawkins, little is known about the physiological substrates that underlie either the production of the sequential organization of most aspects of behavior. Despite widespread acknowledgment that physiological motor programs and many non-linguistic behaviors are hierarchical, for example, few physiological investigations into the dynamics of behavior extend beyond low-order (Markovian) transition statistics. In this thesis, I build onto the emerging field of computational neuroethology to further our understanding of what structure underlies the sequential organization of behavior, what physiological mechanisms might be involved in producing, perceiving, and representing sequential behavioral organization, and how sequential behavioral organization might have emerged developmentally and evolutionarily. Throughout the thesis, I draw primarily upon birdsong and human speech, developing methods to analyze the acoustic and temporal structure in vocal signals and then behaviorally and physiologically probing the underpinnings of sequential organization in the songbird. This work advances the field of computational neuroethology in several ways.I uncover novel acoustic structure in vocal signals separating avian and mammalian vocalizations along a spectrum of vocal stereotypy. I observe that both human speech and birdsong are characterized by a c

Due to the restructuring of activities in response to the COVID-19 pandemic, student mobility around the world is being delivered online. On one hand, the management of internationalization between higher education institutions to carry out research internships predominates. On the other hand, there is little reflection on the experience of these virtual internships. The aim of this paper is to highlight, through the analysis of the case of the visiting students program of Universidad Veracruzana (Mexico), delivered online for the first time in summer 2020, the importance of the virtual multidisciplinary research internship carried out by Colombian students from different fields of study, who shared a common interest in behavioral neuroscience. Connected with the Institute of Neuroethology in Mexico, which focuses on the biological mechanisms underlying behavior, the future professionals in nursing, accounting and law demonstrate through research activities the contribution of neuroscientific knowledge in taking care of others, in promoting financial culture, and in the practice of litigation, respectively. Additionally, these students bring to the program their personal experience. The paper concludes that although student mobility is virtual, it is still possible to do research, exchange cultural and personal experiences, without diminishing the learning challenges that experimental disciplines online present, but strengthening meaningful learning practices., La movilidad estudiantil en el mundo se realiza en modalidad virtual debido a la restructuración de actividades por la COVID-19. Predomina la gestión de la internacionalización entre instituciones de educación superior para realizar estancias de investigación, sin embargo, es escasa la reflexión sobre la experiencia de estas estancias. Así, con el caso del programa de alumnos visitantes de la Universidad Veracruzana de México, realizado por primera vez en modalidad virtual en el verano 2020, el objetivo de este artículo es resaltar la importancia de la estancia virtual de investigación multidisciplinar realizada entre estudiantes colombianos de diferentes profesiones cuyos intereses convergieron en las neurociencias de la conducta. Conectados con el Instituto de Neuroetología en México, donde se estudian las bases biológicas de la conducta, los futuros profesionales en enfermería, contabilidad y derecho, a través de actividades de investigación, evidencian la contribución del conocimiento neurocientífico en el cuidado de otros, en el fomento de la cultura financiera, y en el ejercicio del litigio, respectivamente, y aportan su experiencia personal. Se concluye que, aunque la movilidad estudiantil sea virtual, es posible hacer investigación, intercambiar experiencias culturales y personales, sin desconocer los retos de aprendizaje para las disciplinas experimentales, pero fortaleciendo el aprendizaje significativo.

Our primate Order is known for the expansion of the neocortex relative to other mammals. This distinction is coupled with a characteristic complex society that is facilitated by dynamic social cognitive mechanisms and systems of communication. Because of this intricate relationship, investigating the neural basis of communication within primates affords the opportunity to better understand how different dimensions of sociality are supported by the structures of the brain itself. Much of the research on the neuroscience of communication in primates has hinged on studies of vocalization processing in head-restrained monkeys either passively listening to stimuli or engaged in a conditional behavioral task. But the information communicated by social signals are heavily influenced by the natural contexts they occur in, and auditory processing of vocalizations within the brain may likewise be heavily affected by the context in which conspecific vocalizations are heard; thus, the experiments may not fully capture the neural basis of communication. I hypothesize that the traditional experimental contexts typical of nonhuman primate neuroscience research has divorced the signal from its natural context, and, consequently, limited our understanding of how various neocortical structures support these processes. Here I sought to address this critical gap in our knowledge by implementing novel experimental paradigms designed to explicate the neurobiology and behavior of natural communication in freely-moving marmoset monkeys (Callithrix jacchus). In this dissertation, I detail the results of new insights gained from the innovative experiments that support my hypothesis. Chapter 1 shows how broad ‘states’ of neural populations in frontal cortex during natural, untrained behavior of antiphonal conversations in the marmoset predicts whether subjects respond to a conspecific call. Chapter 2 shows robust within-neuron differences in how prefrontal cortex neurons respond to vocalizati

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user's behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house (N = 4) and out-house (N = 12) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer lea

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user's behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house (N = 4) and out-house (N = 12) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer lea

Our primate Order is known for the expansion of the neocortex relative to other mammals. This distinction is coupled with a characteristic complex society that is facilitated by dynamic social cognitive mechanisms and systems of communication. Because of this intricate relationship, investigating the neural basis of communication within primates affords the opportunity to better understand how different dimensions of sociality are supported by the structures of the brain itself. Much of the research on the neuroscience of communication in primates has hinged on studies of vocalization processing in head-restrained monkeys either passively listening to stimuli or engaged in a conditional behavioral task. But the information communicated by social signals are heavily influenced by the natural contexts they occur in, and auditory processing of vocalizations within the brain may likewise be heavily affected by the context in which conspecific vocalizations are heard; thus, the experiments may not fully capture the neural basis of communication. I hypothesize that the traditional experimental contexts typical of nonhuman primate neuroscience research has divorced the signal from its natural context, and, consequently, limited our understanding of how various neocortical structures support these processes. Here I sought to address this critical gap in our knowledge by implementing novel experimental paradigms designed to explicate the neurobiology and behavior of natural communication in freely-moving marmoset monkeys (Callithrix jacchus). In this dissertation, I detail the results of new insights gained from the innovative experiments that support my hypothesis. Chapter 1 shows how broad ‘states’ of neural populations in frontal cortex during natural, untrained behavior of antiphonal conversations in the marmoset predicts whether subjects respond to a conspecific call. Chapter 2 shows robust within-neuron differences in how prefrontal cortex neurons respond to vocalizati

Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user's behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house (N = 4) and out-house (N = 12) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer lea

Animals can perform complex and purposeful behaviors by executing simpler movements in flexible sequences. It is particularly challenging to analyze behavior sequences when they are highly variable, as is the case in language production, certain types of birdsong and, as in our experiments, flies grooming. High sequence variability necessitates rigorous quantification of large amounts of data to identify organizational principles and temporal structure of such behavior. To cope with large amounts of data, and minimize human effort and subjective bias, researchers often use automatic behavior recognition software. Our standard grooming assay involves coating flies in dust and videotaping them as they groom to remove it. The flies move freely and so perform the same movements in various orientations. As the dust is removed, their appearance changes. These conditions make it difficult to rely on precise body alignment and anatomical landmarks such as eyes or legs and thus present challenges to existing behavior classification software. Human observers use speed, location, and shape of the movements as the diagnostic features of particular grooming actions. We applied this intuition to design a new automatic behavior recognition system (ABRS) based on spatiotemporal features in the video data, heavily weighted for temporal dynamics and invariant to the animal's position and orientation in the scene. We use these spatiotemporal features in two steps of supervised classification that reflect two time-scales at which the behavior is structured. As a proof of principle, we show results from quantification and analysis of a large data set of stimulus-induced fly grooming behaviors that would have been difficult to assess in a smaller dataset of human-annotated ethograms. While we developed and validated this approach to analyze fly grooming behavior, we propose that the strategy of combining alignment-invariant features and multi-timescale analysis may be generally useful fo

Animals can perform complex and purposeful behaviors by executing simpler movements in flexible sequences. It is particularly challenging to analyze behavior sequences when they are highly variable, as is the case in language production, certain types of birdsong and, as in our experiments, flies grooming. High sequence variability necessitates rigorous quantification of large amounts of data to identify organizational principles and temporal structure of such behavior. To cope with large amounts of data, and minimize human effort and subjective bias, researchers often use automatic behavior recognition software. Our standard grooming assay involves coating flies in dust and videotaping them as they groom to remove it. The flies move freely and so perform the same movements in various orientations. As the dust is removed, their appearance changes. These conditions make it difficult to rely on precise body alignment and anatomical landmarks such as eyes or legs and thus present challenges to existing behavior classification software. Human observers use speed, location, and shape of the movements as the diagnostic features of particular grooming actions. We applied this intuition to design a new automatic behavior recognition system (ABRS) based on spatiotemporal features in the video data, heavily weighted for temporal dynamics and invariant to the animal's position and orientation in the scene. We use these spatiotemporal features in two steps of supervised classification that reflect two time-scales at which the behavior is structured. As a proof of principle, we show results from quantification and analysis of a large data set of stimulus-induced fly grooming behaviors that would have been difficult to assess in a smaller dataset of human-annotated ethograms. While we developed and validated this approach to analyze fly grooming behavior, we propose that the strategy of combining alignment-invariant features and multi-timescale analysis may be generally useful fo

Leeches in the wild are often found on smooth surfaces, such as vegetation, smooth rocks or human artifacts such as bottles and cans, thus exhibiting what appears to be a "substrate texture preference". Here, we have reproduced this behavior under controlled circumstances, by allowing leeches to step about freely on a range of silicon carbide substrates (sandpaper). To begin to understand the neural mechanisms underlying this texture preference behavior, we have determined relevant parameters of leech behavior both on uniform substrates of varying textures, and in a behavior choice paradigm in which the leech is confronted with a choice between rougher and smoother substrate textures at each step. We tested two non-exclusive mechanisms which could produce substrate texture preference: (1) a Differential Diffusion mechanism, in which a leech is more likely to stop moving on a smooth surface than on a rough one, and (2) a Smoothness Selection mechanism, in which a leech is more likely to attach its front sucker (prerequisite for taking a step) to a smooth surface than to a rough one. We propose that both mechanisms contribute to the texture preference exhibited by leeches.

While the auditory cortex is necessary for sound localization, we do not yet know the underlying mechanisms that lead to the behavior. To address this gap, we have designed and executed a series of experiments aimed at describing the neuroethology of sound localization in the pallid bat, an animal which relies on passive hearing of prey-generated noise when foraging. We have made three primary discoveries. First, we have discovered how the external ear filters incoming sounds before they reach the sensory epithelium. Low frequency spectral notches, important for the perception of sound source elevation, depend on the presence of the conspicuous tragus. Second, we developed a sound localization task to test the behavior of the pallid bat. Pallid bats demonstrate remarkable sound localization acuity of ~4° near the midline, and we show that the high frequency range of prey-generated noise (20-30 kHz) is the predominant bandwidth used for azimuth localization. Last, we describe the 2D spatial selectivity of auditory cortex neurons and propose a population code for 2D sound localization. Our data suggests that the extent of active cortex is constrained systematically by the horizontal and elevational sound source direction. We describe preliminary results that indicate the chemogenetic inhibitory DREADDs system is a viable method for answering questions about the necessity of these auditory cortex populations to sound localization behavior. Collectively, the research contained in this dissertation provides insight into the way the auditory system is able to compute representations of external space from sound filtered by the external ear and supports the notion of the pallid bat as a viable model from which basic understandings of mammalian sound localization can be obtained.

Song is a learned motor behavior where each vocal element (syllable) has its own distinct acoustic structure. Syllable features, like pitch and amplitude, have been linked to the activity of the forebrain nucleus RA; but whether these features are directly controlled by the level of RA activity remains to be shown. Natural variation in RA activity may be produced by a dynamic interaction between RA projection neurons and interneurons; but a role for local inhibition in RA for shaping vocal output is also unknown. Here, we address whether changing RA activity by modulating the influence of RA inhibition has a direct effect on syllable structure. We find that increasing or decreasing RA inhibition drives a corresponding increase and decrease in syllable pitch and amplitude. These results provide evidence that local inhibition within RA actively contributes to song production and suggest that maintaining pitch and amplitude at a desired level requires a careful balance of excitation and inhibition acting within RA. Song also has a specific temporal structure that is linked to activity in the forebrain nucleus HVC. Previous studies have found that manipulating the temporal dynamics of HVC activity drives changes to song timing and sequencing. This supports a hierarchical model of song control in which temporal and spectral properties of song (such as syllable sequencing and timing versus syllable pitch and amplitude) are separately controlled by HVC and RA. However, the downstream targets of RA in the brainstem also make ascending projections back to HVC, raising the possibility that the output of RA may recurrently influence HVC activity to shape song timing and sequencing. Here, we find that natural variation in RA activity is correlated with variation in song timing; specifically, higher firing in RA is associated with shorter durations of the gaps between syllables. We also find that increasing or decreasing the influence of RA inhibition drives bidirectional change

Semiology observation and characterization play a major role in the presurgical evaluation of epilepsy. However, the interpretation of patient movements has subjective and intrinsic challenges. In this paper, we develop approaches to attempt to automatically extract and classify semiological patterns from facial expressions. We address limitations of existing computer-based analytical approaches of epilepsy monitoring, where facial movements have largely been ignored. This is an area that has seen limited advances in the literature. Inspired by recent advances in deep learning, we propose two deep learning models, landmark-based and region-based, to quantitatively identify changes in facial semiology in patients with mesial temporal lobe epilepsy (MTLE) from spontaneous expressions during phase I monitoring. A dataset has been collected from the Mater Advanced Epilepsy Unit (Brisbane, Australia) and is used to evaluate our proposed approach. Our experiments show that a landmark-based approach achieves promising results in analyzing facial semiology, where movements can be effectively marked and tracked when there is a frontal face on visualization. However, the region-based counterpart with spatiotemporal features achieves more accurate results when confronted with extreme head positions. A multifold cross-validation of the region-based approach exhibited an average test accuracy of 95.19% and an average AUC of 0.98 of the ROC curve. Conversely, a leave-one-subject-out cross-validation scheme for the same approach reveals a reduction in accuracy for the model as it is affected by data limitations and achieves an average test accuracy of 50.85%. Overall, the proposed deep learning models have shown promise in quantifying ictal facial movements in patients with MTLE. In turn, this may serve to enhance the automated presurgical epilepsy evaluation by allowing for standardization, mitigating bias, and assessing key features. The computer-aided diagnosis may help to supp

While the auditory cortex is necessary for sound localization, we do not yet know the underlying mechanisms that lead to the behavior. To address this gap, we have designed and executed a series of experiments aimed at describing the neuroethology of sound localization in the pallid bat, an animal which relies on passive hearing of prey-generated noise when foraging. We have made three primary discoveries. First, we have discovered how the external ear filters incoming sounds before they reach the sensory epithelium. Low frequency spectral notches, important for the perception of sound source elevation, depend on the presence of the conspicuous tragus. Second, we developed a sound localization task to test the behavior of the pallid bat. Pallid bats demonstrate remarkable sound localization acuity of ~4° near the midline, and we show that the high frequency range of prey-generated noise (20-30 kHz) is the predominant bandwidth used for azimuth localization. Last, we describe the 2D spatial selectivity of auditory cortex neurons and propose a population code for 2D sound localization. Our data suggests that the extent of active cortex is constrained systematically by the horizontal and elevational sound source direction. We describe preliminary results that indicate the chemogenetic inhibitory DREADDs system is a viable method for answering questions about the necessity of these auditory cortex populations to sound localization behavior. Collectively, the research contained in this dissertation provides insight into the way the auditory system is able to compute representations of external space from sound filtered by the external ear and supports the notion of the pallid bat as a viable model from which basic understandings of mammalian sound localization can be obtained.

Song is a learned motor behavior where each vocal element (syllable) has its own distinct acoustic structure. Syllable features, like pitch and amplitude, have been linked to the activity of the forebrain nucleus RA; but whether these features are directly controlled by the level of RA activity remains to be shown. Natural variation in RA activity may be produced by a dynamic interaction between RA projection neurons and interneurons; but a role for local inhibition in RA for shaping vocal output is also unknown. Here, we address whether changing RA activity by modulating the influence of RA inhibition has a direct effect on syllable structure. We find that increasing or decreasing RA inhibition drives a corresponding increase and decrease in syllable pitch and amplitude. These results provide evidence that local inhibition within RA actively contributes to song production and suggest that maintaining pitch and amplitude at a desired level requires a careful balance of excitation and inhibition acting within RA. Song also has a specific temporal structure that is linked to activity in the forebrain nucleus HVC. Previous studies have found that manipulating the temporal dynamics of HVC activity drives changes to song timing and sequencing. This supports a hierarchical model of song control in which temporal and spectral properties of song (such as syllable sequencing and timing versus syllable pitch and amplitude) are separately controlled by HVC and RA. However, the downstream targets of RA in the brainstem also make ascending projections back to HVC, raising the possibility that the output of RA may recurrently influence HVC activity to shape song timing and sequencing. Here, we find that natural variation in RA activity is correlated with variation in song timing; specifically, higher firing in RA is associated with shorter durations of the gaps between syllables. We also find that increasing or decreasing the influence of RA inhibition drives bidirectional change

To evade their predators, animals must quickly detect potential threats, gauge risk, and mount a response. Putative neural circuits responsible for these tasks have been isolated in laboratory studies. However, it is unclear whether and how these circuits combine to generate the flexible, dynamic sequences of evasion behavior exhibited by wild, freely moving animals. Here, we report that evasion behavior of wild fish on a coral reef is generated through a sequence of well-defined decision rules that convert visual sensory input into behavioral actions. Using an automated system to present visual threat stimuli to fish in situ, we show that individuals initiate escape maneuvers in response to the perceived size and expansion rate of an oncoming threat using a decision rule that matches dynamics of known loom-sensitive neural circuits. After initiating an evasion maneuver, fish adjust their trajectories using a control rule based on visual feedback to steer away from the threat and toward shelter. These decision rules accurately describe evasion behavior of fish from phylogenetically distant families, illustrating the conserved nature of escape decision-making. Our results reveal how the flexible behavioral responses required for survival can emerge from relatively simple, conserved decision-making mechanisms.

Plants and the insects that feed on them dominate diversity in terrestrial ecosystems: half of all named species are contained within these two groups. Herbivorous insects (herbivores) are abundant and diverse, yet paradoxically, two thirds of insect orders contain no major lineages of herbivores, implying barriers to the evolution of this trophic interaction. How herbivory evolves and why herbivores are so diverse are questions that are key to understanding the processes that have shaped global biodiversity. Yet, most lineages of herbivores are ancient with sister groups either absent or too divergent for a comparative genomic analysis to yield a mechanistic understanding of both their origin and diversification. Many of the exceptions to this pattern are among the Diptera, where lineages such as the leaf-mining drosophilids in the genus Scaptomyza have emerged within the last 10 million years. Scaptomyza is particularly well-suited for identifying the adaptations associated with the evolution of herbivory because it is embedded within the paraphyletic genus Drosophila, which contains species with 25 sequenced genomes, and is closely related to D. melanogaster, the genetic model, and a taxon with one of the most well-studied nervous systems. Behavior is thought to be one of the earliest adaptations during the evolution of herbivory and niche shifts in general. Insects undergoing a niche shift likely lose their preferences for their ancestral diet, and also evolve an attraction to novel cues indicative of their new oviposition substrate. Once females lay eggs in a new environment, herbivores must consume the new diet, despite the fact that it may contain aversive chemicals and a different balance of macronutrients compared to the ancestral diet. Using the herbivorous Scaptomyza flava as a model system, the primary aim of my dissertation was to use methods in comparative genomics, chemical ecology, ethology, and neural imaging to characterize the mechanistic basis of

Recently, yawn duration was shown to be a robust predictor of brain size and complexity across a diverse sample of mammalian species. In particular, mammals with larger brains and more cortical neurons have longer yawns on average. Here, we investigated whether this relationship between yawn duration and brain size, which was previously at the taxonomic rank of class, is also present within a more restricted scale: a family of mammals. Using previously published data on brain weight and endocranial volumes among various field species within the family Felidae, we ran correlations with yawn durations obtained from openly accessible videos on the Internet. Consistent with previous findings, we show a strong linear relationship between yawn duration and both brain weight and brain volumes among wild cats. However, yawn duration was not significantly correlated with species body weight. Although limited to a small sample, these results provide convergent evidence for an important and general neurophysiologic function to yawning and highlight the utility of measuring yawn duration in comparative research.

Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest. We simplified a looming stimuli procedure using the Backyard Brains SpikerBox bioamplifier, an open-source and low-cost electrophysiology rig, to extracellularly record activity of the descending contralateral movement detector (DCMD) neuron from the grasshopper’s neck. The DCMD activity underlies the grasshopper's motor responses to looming monocular visual cues and can easily be recorded and analyzed on an open-source iOS oscilloscope app, Spike Recorder. Visual stimuli are presented to the grasshopper by this same mobile application allowing for synchronized recording of stimuli and neural activity. An in-app spike-sorting algorithm is described that allows a quick way for students to record, sort, and analyze their data at the bench. We also describe a way for students to export these data to other analysis tools. With the protocol described, students will be able to prepare the grasshopper, find and record from the DCMD neuron, and visualize the DCMD responses to quantitatively investigate the escape system by adjusting the speed and size of simulated approaching objects. We describe the results from 22 grasshoppers, where 50 of the 57 recording sessions (87.7%) had a reliable DCMD response. Finally, we field-tested our experiment in an undergraduate neuroscience laboratory and found that a majority of students (67%) could perform this exercise in one two-hour lab setting, and had an increase in interest for studying the neural systems that drive behavior.

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and non human primates (i.e.,virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between non human primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and non human primates (i.e.,virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between non human primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and non human primates (i.e.,virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between non human primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.

Author Posting. © Faculty for Undergraduate Neuroscience, 2017. This article is posted here by permission of Faculty for Undergraduate Neuroscience for personal use, not for redistribution. The definitive version was published in Journal of Undergraduate Neuroscience Education 15 (2017): A162-A173., Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest. We simplified a looming stimuli procedure using the Backyard Brains SpikerBox bioamplifier, an open-source and low-cost electrophysiology rig, to extracellularly record activity of the descending contralateral movement detector (DCMD) neuron from the grasshopper’s neck. The DCMD activity underlies the grasshopper's motor responses to looming monocular visual cues and can easily be recorded and analyzed on an open-source iOS oscilloscope app, Spike Recorder. Visual stimuli are presented to the grasshopper by this same mobile application allowing for synchronized recording of stimuli and neural activity. An in-app spike-sorting algorithm is described that allows a quick way for students to record, sort, and analyze their data at the bench. We also describe a way for students to export these data to other analysis tools. With the protocol described, students will be able to prepare the grasshopper, find and record from the DCMD neuron, and visualize the DCMD responses to quantitatively investigate the escape system by adjusting the speed and size of simulated approaching objects. We describe the results from 22 grasshoppers, where 50 of the 57 recording sessions (87.7%) had a reliable DCMD response. Finally, we field-tested our experiment in an undergraduate neuroscience laboratory and found that a majority of students (67%) could perform this exercise in one two-hour lab setting, and had an increase in interest for studying the neural systems that drive behavior., Funding for this project was supported by the National Institute of Mental Health Small Business Innovation Research grant #2R44MH093334: “Backyard Brains: Bringing Neurophysiology into Secondary Schools.”

Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest. We simplified a looming stimuli procedure using the Backyard Brains SpikerBox bioamplifier, an open-source and low-cost electrophysiology rig, to extracellularly record activity of the descending contralateral movement detector (DCMD) neuron from the grasshopper’s neck. The DCMD activity underlies the grasshopper's motor responses to looming monocular visual cues and can easily be recorded and analyzed on an open-source iOS oscilloscope app, Spike Recorder. Visual stimuli are presented to the grasshopper by this same mobile application allowing for synchronized recording of stimuli and neural activity. An in-app spike-sorting algorithm is described that allows a quick way for students to record, sort, and analyze their data at the bench. We also describe a way for students to export these data to other analysis tools. With the protocol described, students will be able to prepare the grasshopper, find and record from the DCMD neuron, and visualize the DCMD responses to quantitatively investigate the escape system by adjusting the speed and size of simulated approaching objects. We describe the results from 22 grasshoppers, where 50 of the 57 recording sessions (87.7%) had a reliable DCMD response. Finally, we field-tested our experiment in an undergraduate neuroscience laboratory and found that a majority of students (67%) could perform this exercise in one two-hour lab setting, and had an increase in interest for studying the neural systems that drive behavior.

Recently, yawn duration was shown to be a robust predictor of brain size and complexity across a diverse sample of mammalian species. In particular, mammals with larger brains and more cortical neurons have longer yawns on average. Here, we investigated whether this relationship between yawn duration and brain size, which was previously at the taxonomic rank of class, is also present within a more restricted scale: a family of mammals. Using previously published data on brain weight and endocranial volumes among various field species within the family Felidae, we ran correlations with yawn durations obtained from openly accessible videos on the Internet. Consistent with previous findings, we show a strong linear relationship between yawn duration and both brain weight and brain volumes among wild cats. However, yawn duration was not significantly correlated with species body weight. Although limited to a small sample, these results provide convergent evidence for an important and general neurophysiologic function to yawning and highlight the utility of measuring yawn duration in comparative research.&nbsp

Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest. We simplified a looming stimuli procedure using the Backyard Brains SpikerBox bioamplifier, an open-source and low-cost electrophysiology rig, to extracellularly record activity of the descending contralateral movement detector (DCMD) neuron from the grasshopper’s neck. The DCMD activity underlies the grasshopper's motor responses to looming monocular visual cues and can easily be recorded and analyzed on an open-source iOS oscilloscope app, Spike Recorder. Visual stimuli are presented to the grasshopper by this same mobile application allowing for synchronized recording of stimuli and neural activity. An in-app spike-sorting algorithm is described that allows a quick way for students to record, sort, and analyze their data at the bench. We also describe a way for students to export these data to other analysis tools. With the protocol described, students will be able to prepare the grasshopper, find and record from the DCMD neuron, and visualize the DCMD responses to quantitatively investigate the escape system by adjusting the speed and size of simulated approaching objects. We describe the results from 22 grasshoppers, where 50 of the 57 recording sessions (87.7%) had a reliable DCMD response. Finally, we field-tested our experiment in an undergraduate neuroscience laboratory and found that a majority of students (67%) could perform this exercise in one two-hour lab setting, and had an increase in interest for studying the neural systems that drive behavior.

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and nonhuman primates (i.e., virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between nonhuman primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.SIGNIFICANCE STATEMENT One primary factor constraining memory capacity is the sparsity of the engram, the proportion of neurons that encode a single experience. Investigating sparsity in humans is hampered by the lack of single-cell resolution and differences in behavioral protocols. Sparsity can be quantified in freely moving rodents, but extrapolating these data to humans assumes that information storage is comparable across species and is robust to restraint-induced reduction in sensory input. Here, we test these assumptions and show that species differences in brain size

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and non human primates (i.e.,virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between non human primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and non human primates (i.e.,virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between non human primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.

Decades of research identify the hippocampal formation as central to memory storage and recall. Events are stored via distributed population codes, the parameters of which (e.g., sparsity and overlap) determine both storage capacity and fidelity. However, it remains unclear whether the parameters governing information storage are similar between species. Because episodic memories are rooted in the space in which they are experienced, the hippocampal response to navigation is often used as a proxy to study memory. Critically, recent studies in rodents that mimic the conditions typical of navigation studies in humans and non human primates (i.e.,virtual reality) show that reduced sensory input alters hippocampal representations of space. The goal of this study was to quantify this effect and determine whether there are commonalities in information storage across species. Using functional molecular imaging, we observe that navigation in virtual environments elicits activity in fewer CA1 neurons relative to real-world conditions. Conversely, comparable neuronal activity is observed in hippocampus region CA3 and the dentate gyrus under both conditions. Surprisingly, we also find evidence that the absolute number of neurons used to represent an experience is relatively stable between non human primates and rodents. We propose that this convergence reflects an optimal ensemble size for episodic memories.

Sexual dimorphism is exhibited across all cricket species and is a central aspect of the mating processes of these insects. Only male crickets possess wing structures and pattern generators in the central nervous system that allow them to produce a mating call that is unique to their species in order to attract conspecific females. Conspecific females possess an auditory feature detection circuit in the central nervous system that is capable of detecting the species-specific frequency and temporal pattern of sound pulses within a male call. In order for dimorphic differences in mating behavior to result in successful continuation of a species, it would seem that genetic control of the neurological mechanisms underlying the production and perception of mating behaviors in both sexes of a species must occur. Accordingly, previously conducted behavioral research on auditory production and perception in crickets has suggested that the neural mechanisms underlying these processes are genetically controlled in both males and females. A genetic analysis of male pattern generators in the central nervous system has been previously explored, however a genetic analysis of the feature detection circuit in female crickets has yet to be conducted. Hybridization studies, where male and female crickets belonging to two similar yet distinct species are bred to produce hybrid offspring, have shown that within one generation the hybrid male offspring produce a mating call that is an intermediate hybrid between the calls of the two original parental males. They also demonstrated that within one generation hybrid females show a behavioral preference for these hybrid male calls over those of males from either of the two original parental species. Both of these findings strongly suggest that the mechanisms of production and perception of mating calls are completely genetically controlled, with no possibility of learned behavior playing a role in this phenomenon. The ability for conspecifi

Jumping spiders (Salticidae) are famous for their visually driven behaviors [1]. Here, however, we present behavioral and neurophysiological evidence that these animals also perceive and respond to airborne acoustic stimuli, even when the distance between the animal and the sound source is relatively large (∼3 m) and with stimulus amplitudes at the position of the spider of ∼65 dB sound pressure level (SPL). Behavioral experiments with the jumping spider Phidippus audax reveal that these animals respond to low-frequency sounds (80 Hz; 65 dB SPL) by freezing-a common anti-predatory behavior characteristic of an acoustic startle response. Neurophysiological recordings from auditory-sensitive neural units in the brains of these jumping spiders showed responses to low-frequency tones (80 Hz at ∼65 dB SPL)-recordings that also represent the first record of acoustically responsive neural units in the jumping spider brain. Responses persisted even when the distances between spider and stimulus source exceeded 3 m and under anechoic conditions. Thus, these spiders appear able to detect airborne sound at distances in the acoustic far-field region, beyond the near-field range often thought to bound acoustic perception in arthropods that lack tympanic ears (e.g., spiders) [2]. Furthermore, direct mechanical stimulation of hairs on the patella of the foreleg was sufficient to generate responses in neural units that also responded to airborne acoustic stimuli-evidence that these hairs likely play a role in the detection of acoustic cues. We suggest that these auditory responses enable the detection of predators and facilitate an acoustic startle response. VIDEO ABSTRACT.

The present paper emerges from the research conducted between the Microelectronics Research Center (CIME) and the Faculty of Biology of Universidad de la Habana in order to studying the behavior of small mammals. The design and analysis of three ECG acquisition channels is made, which are going to be used in applications of Neuroethology. These measurement channels belong to the higher system that manages to record, store and process the signal in real time. The theoretical computations are compared with the results of simulations using PROTEUS ProSPICE 7.10 SP0; showing the advantages and disadvantages between each topology., El presente trabajo surge de las investigaciones realizadas entre el Centro de Investigaciones en Microelectrónica (CIME) y la Facultad de Biología de la Universidad de la Habana (FBIO-UH) para el estudio del comportamiento conductual de pequeños mamíferos. Se realiza el diseño y análisis de tres canales de adquisición de la señal de electrocardiografía (ECG) a utilizarse en aplicaciones de Neuroetología. Los canales pertenecen al bloque acondicionador de un sistema de adquisición de datos encargado de registrar, almacenar y procesar las señales en tiempo real. Se comparan los cálculos teóricos realizados de las tres topologías diseñadas con los resultados obtenidos de las simulaciones utilizando la herramienta ProSPICE de PROTEUS 7.10 SP0 con el objetivo de mostrar las ventajas y desventajas de una topología respecto a las restantes.

Jumping spiders (Salticidae) are famous for their visually driven behaviors [1]. Here, however, we present behavioral and neurophysiological evidence that these animals also perceive and respond to airborne acoustic stimuli, even when the distance between the animal and the sound source is relatively large (∼3 m) and with stimulus amplitudes at the position of the spider of ∼65 dB sound pressure level (SPL). Behavioral experiments with the jumping spider Phidippus audax reveal that these animals respond to low-frequency sounds (80 Hz; 65 dB SPL) by freezing-a common anti-predatory behavior characteristic of an acoustic startle response. Neurophysiological recordings from auditory-sensitive neural units in the brains of these jumping spiders showed responses to low-frequency tones (80 Hz at ∼65 dB SPL)-recordings that also represent the first record of acoustically responsive neural units in the jumping spider brain. Responses persisted even when the distances between spider and stimulus source exceeded 3 m and under anechoic conditions. Thus, these spiders appear able to detect airborne sound at distances in the acoustic far-field region, beyond the near-field range often thought to bound acoustic perception in arthropods that lack tympanic ears (e.g., spiders) [2]. Furthermore, direct mechanical stimulation of hairs on the patella of the foreleg was sufficient to generate responses in neural units that also responded to airborne acoustic stimuli-evidence that these hairs likely play a role in the detection of acoustic cues. We suggest that these auditory responses enable the detection of predators and facilitate an acoustic startle response. VIDEO ABSTRACT.

The present paper emerges from the research conducted between the Microelectronics Research Center (CIME) and the Faculty of Biology of Universidad de la Habana in order to studying the behavior of small mammals. The design and analysis of three ECG acquisition channels is made, which are going to be used in applications of Neuroethology. These measurement channels belong to the higher system that manages to record, store and process the signal in real time. The theoretical computations are compared with the results of simulations using PROTEUS ProSPICE 7.10 SP0; showing the advantages and disadvantages between each topology., El presente trabajo surge de las investigaciones realizadas entre el Centro de Investigaciones en Microelectrónica (CIME) y la Facultad de Biología de la Universidad de la Habana (FBIO-UH) para el estudio del comportamiento conductual de pequeños mamíferos. Se realiza el diseño y análisis de tres canales de adquisición de la señal de electrocardiografía (ECG) a utilizarse en aplicaciones de Neuroetología. Los canales pertenecen al bloque acondicionador de un sistema de adquisición de datos encargado de registrar, almacenar y procesar las señales en tiempo real. Se comparan los cálculos teóricos realizados de las tres topologías diseñadas con los resultados obtenidos de las simulaciones utilizando la herramienta ProSPICE de PROTEUS 7.10 SP0 con el objetivo de mostrar las ventajas y desventajas de una topología respecto a las restantes.

Odors that are essential to the survival and reproduction of a species take the form of complex mixtures of volatiles. Often, an odor source such as food or a potential mate releases a mixture with characteristic ratios between the components. Here, the encoding of the characteristic ratio between components of the pheromone released by a female moth is investigated in the antennal lobe (AL) of a male moth (Manduca sexta). The mechanisms by which olfactory systems of diverse insect species process odors are adapted to the particular environment and olfactory behavior of the animal. In the moth, innately attractive odors produce patterns of synchrony in the output of the AL, the projection neurons (PNs). Male moths exhibited attraction to synthetic mixtures of pheromone components that was selective for ratios at or near the natural ratio released by females. Selectivity increased as the moth neared the odor source and initiated mating behaviors. PNs in the macroglomerular complex (MGC) did not exhibit an effect of component ratio on their firing rate responses. However, pairs of PNs exhibited increased synchrony in response to the behaviorally effective ratios of pheromone components. Individual pairs exhibited selectivity for ratios within 1 order of magnitude from the natural ratio. Synchrony in PN spiking was not phase-locked to the network oscillations in the AL. A model for ratio-selective enhancement of synchronous PN output in the AL is proposed.

Odors that are essential to the survival and reproduction of a species take the form of complex mixtures of volatiles. Often, an odor source such as food or a potential mate releases a mixture with characteristic ratios between the components. Here, the encoding of the characteristic ratio between components of the pheromone released by a female moth is investigated in the antennal lobe (AL) of a male moth (Manduca sexta). The mechanisms by which olfactory systems of diverse insect species process odors are adapted to the particular environment and olfactory behavior of the animal. In the moth, innately attractive odors produce patterns of synchrony in the output of the AL, the projection neurons (PNs). Male moths exhibited attraction to synthetic mixtures of pheromone components that was selective for ratios at or near the natural ratio released by females. Selectivity increased as the moth neared the odor source and initiated mating behaviors. PNs in the macroglomerular complex (MGC) did not exhibit an effect of component ratio on their firing rate responses. However, pairs of PNs exhibited increased synchrony in response to the behaviorally effective ratios of pheromone components. Individual pairs exhibited selectivity for ratios within 1 order of magnitude from the natural ratio. Synchrony in PN spiking was not phase-locked to the network oscillations in the AL. A model for ratio-selective enhancement of synchronous PN output in the AL is proposed.