Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child’s behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child’s behavior.

Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila

Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.

Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

Multisensor biologging provides a powerful tool for ecological research, enabling fine-scale observation of animals to directly link physiology and movement to behavior across ecological contexts. However, applied research into behavioral disturbance and recovery following human interventions (e.g., capture and translocation) has mostly relied on coarse location-based tracking or unidimensional approaches (e.g., dive profiles and activity/energetic metrics) that may not resolve behaviors and recovery processes. Biologging can improve insights into both disturbed and natural behavior, which is critical for management and conservation initiatives, although challenges remain in objectively identifying distinct behavioral modes from complex multisensor datasets. Using white sharks (Carcharodon carcharias) released from a non-lethal catch-and-release shark bite mitigation program, we explored how combining multisensor biologging (video, depth, accelerometers, gyroscopes, and magnetometers), track reconstruction and behavioral state modeling using hidden Markov models (HMMs) can improve our understanding of behavioral processes and recovery. Biologging tags were deployed on eight white sharks, recording their continuous behaviors, movements, and environmental context (habitat, interactions with other organisms/objects) for periods of 10–87 h post-release. Dive profiles and tailbeat analysis (as a standard, activity-based method for assessing recovery) indicated an immediate “disturbed” period of offshore movement, displaying rapid tailbeats and an average tailbeat-derived recovery period of 9.7 h, with evidence of smaller individuals having longer recoveries. However, further integrating magnetometer-derived headings, track reconstruction and HMM modeling revealed a cryptic shift to diurnal clockwise-counterclockwise circling behavior, which we argue represents compelling new evidence for hypothesized unihemispheric sleep amongst elasmobranchs. By simultaneously providing critical information toward conservation-focused shark management and understudied aspects of shark behavior, our study highlights how integrating multisensor information through HMMs can improve our understanding of both post-release and natural behavior, especially in species that are difficult to observe directly.

Locus Coeruleus Optogenetic Modulation: Lessons Learned from Temporal Patterns

After reviewing seminal studies using optogenetics to interrogate the functional role of the locus coeruleus in behavior, we conclude that differences in firing rates and firing patterns of locus coeruleus neurons contribute to locus coeruleus nucleus heterogeneity by recruiting different output circuitry, and differentially modifying behavior. The outcomes initiated by different optogenetic input activation patterns and frequencies can have opposite consequences for behavior, activate different neurons in the same target structure, be supported by distinct adrenoceptors and vary with behavioral state.

Sleep Pathophysiology

Sleep is a natural, reversible, and periodic behavioral state characterized by perceptual inattention and decreased responsiveness to external stimuli. The processes governing sleep, sleep-wake transitions, and maintenance of wakefulness are mediated by complex physiologic mechanisms, the primary neurobiological substrates of which include the neocortex, basal forebrain, thalamus, hypothalamus, pontine tegmentum, and brainstem monoaminergic nuclei. Moreover, the integrity of brainstem autonomic respiratory control networks becomes critical in the maintenance of ventilation during sleep. Pathologic insults to these systems may result in a broad constellation of clinical deficits.

A persistent behavioral state enables sustained predation of humans by mosquitoes

Predatory animals first detect, then pursue, and ultimately capture prey. Sensory cues, including scent emitted by prey, are detected by the predator and used to guide pursuit. Because the pursuit phase can last for extended periods of time, it is critical for predators to persist in the chase even when prey is difficult to detect in a noisy sensory land-scape. It is equally important for predators to abandon pursuit if enough time has elapsed that prey capture is unlikely to occur. We studied prey detection and sustained pursuit in the mosquito Aedes aegypti, a micropredator of humans. These animals first detect hu-mans through sensory cues that are emitted at a distance such as carbon dioxide in breath and odor from skin. As the mosquito approaches a human, additional cues such as body heat and visual contrast signal the promise of a blood meal, which females need to produce eggs. To study how initial prey detection influences the duration of pursuit, we developed optogenetic tools to induce a brief fictive sensation of carbon dioxide and used machine learning-based classification of behavior to investigate how mosquitoes respond to subsequent human cues. We found that a 5-second optogenetic pulse of fictive carbon dioxide induced a persistent behavioral state in female mosquitoes that lasted for more than 10 minutes. This state is highly specific to females searching for a blood meal and was not induced in recently blood-fed females or in males, who do not feed on blood. In males that lack the gene fruitless, which controls persistent social behaviors in other insects, fictive carbon dioxide induced a long-lasting behavior response resembling the predatory state of females. Finally, we show that the persistent state triggered by detection of fictive carbon dioxide enabled females to engorge on a blood meal mimic offered up to 14 minutes after the initial stimulus. Our results demonstrate that a persistent internal state allows female mosquitoes to integrate multiple human sensory cues over long timescales, an ability that is key to their success as an apex micropredator of humans

An artificial neural network for automated behavioral state classification in rats

Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.

Behavioral state tunes mouse vision to ethological features through pupil dilation

Across animal species, sensory processing dynamically adapts to behavioral context. In the mammalian visual system, sensory neural responses and behavioral performance increase during an active behavioral state characterized by locomotion activity and pupil dilation, whereas preferred stimuli of individual neurons typically remain unchanged. Here, we address how behavioral states modulate stimulus selectivity in the context of colored natural scenes using a combination of large-scale population imaging, behavior, pharmacology, and deep neural network modeling. In visual cortex of awake mice, we identified a consistent shift of individual neuron color preferences towards ultraviolet stimuli during active behavioral periods that was particularly pronounced in the upper visual field. We found that the spectral shift in neural tuning is mediated by pupil dilation, resulting in a dynamic switch from rod- to cone-driven visual responses for constant ambient light levels. We further showed that this shift selectively enhances the discriminability of ultraviolet objects and facilitates the detection of ethological stimuli, such as aerial predators against the ultraviolet background of the twilight sky. Our results suggest a novel functional role for pupil dilation during active behavioral states as a bottom-up mechanism that, together with top-down neuromodulatory mechanisms, dynamically tunes visual representations to different behavioral demands.