behavioral states
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 621
Author(s):  
Chris Lytridis ◽  
Vassilis G. Kaburlasos ◽  
Christos Bazinas ◽  
George A. Papakostas ◽  
George Sidiropoulos ◽  
...  

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.


SLEEP ◽  
2022 ◽  
Author(s):  
Christopher W Jones ◽  
Mathias Basner ◽  
Daniel J Mollicone ◽  
Christopher M Mott ◽  
David F Dinges

Abstract Astronauts are required to maintain optimal neurobehavioral functioning despite chronic exposure to the stressors and challenges of spaceflight. Sleep of adequate quality and duration is fundamental to neurobehavioral functioning, however astronauts commonly experience short sleep durations in spaceflight (<6 h). As humans embark on long-duration space exploration missions, there is an outstanding need to identify the consequences of sleep deficiency in spaceflight on neurobehavioral functions. Therefore, we conducted a longitudinal study that examined the sleep-wake behaviors, neurobehavioral functions, and ratings of stress and workload of N=24 astronauts before, during, and after 6-month missions aboard the International Space Station (ISS). The computerized, Reaction SelfTest (RST), gathered astronaut report of sleep-wake behaviors, stress, workload, and somatic behavioral states; the RST also objectively assessed vigilant attention (i.e., Psychomotor Vigilance Test-Brief). Data collection began 180 days before launch, continued every 4 days in-flight aboard the ISS, and up to 90 days post-landing, which produced N=2,856 RSTs. Consistent with previous ISS studies, astronauts reported sleeping ~6.5 h in-flight. The adverse consequences of short sleep were observed across neurobehavioral functions, where sleep durations <6 h were associated with significant reductions in psychomotor response speed, elevated stress, and higher workload. Sleep durations <5 h were associated with elevated negative somatic behavioral states. Furthermore, longer sleep durations had beneficial effects on astronaut neurobehavioral functions. Taken together, our findings highlight the importance of sleep for the maintenance of neurobehavioral functioning and as with humans on Earth, astronauts would likely benefit from interventions that promote sleep duration and quality.


eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ni Ji ◽  
Gurrein K Madan ◽  
Guadalupe I Fabre ◽  
Alyssa Dayan ◽  
Casey M Baker ◽  
...  

To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely-moving animals, we identify stereotyped changes in circuit activity corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the neural activity patterns observed in each state. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.


2021 ◽  
Vol 17 (9) ◽  
pp. e1009329
Author(s):  
Erik Saberski ◽  
Antonia K. Bock ◽  
Rachel Goodridge ◽  
Vitul Agarwal ◽  
Tom Lorimer ◽  
...  

Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans, where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called “eigenworms”). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create “interaction profiles” to represent an individual’s behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons.


2021 ◽  
Author(s):  
Katrin Franke ◽  
Konstantin F. Willeke ◽  
Kayla Ponder ◽  
Mario Galdamez ◽  
Taliah Muhammad ◽  
...  

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.


2021 ◽  
Vol 7 (30) ◽  
pp. eabh2059
Author(s):  
Aaron C. Koralek ◽  
Rui M. Costa

The balance between exploiting known actions and exploring alternatives is critical for survival and hypothesized to rely on shifts in neuromodulation. We developed a behavioral paradigm to capture exploitative and exploratory states and imaged calcium dynamics in genetically identified dopaminergic and noradrenergic neurons. During exploitative states, characterized by motivated repetition of the same action choice, dopamine neurons in SNc encoding movement vigor showed sustained elevation of basal activity that lasted many seconds. This sustained activity emerged from longer positive responses, which accumulated during exploitative action-reward bouts, and hysteretic dynamics. Conversely, noradrenergic neurons in LC showed sustained inhibition of basal activity due to the accumulation of longer negative responses in LC. Chemogenetic manipulation of these sustained dynamics revealed that dopaminergic activity mediates action drive, whereas noradrenergic activity modulates choice diversity. These data uncover the emergence of sustained neural states in dopaminergic and noradrenergic networks that mediate dissociable aspects of exploitative bouts.


Author(s):  
Blake Scott Porter ◽  
Kristin L Hillman

When performing a physically demanding behavior, sometimes the optimal choice is to quit the behavior rather than persist in order to minimize energy expenditure for the benefits gained. The dorsomedial prefrontal cortex (dmPFC), consisting of the anterior cingulate cortex and secondary motor area, likely contributes towards such utility assessments. Here, we examined how male rat dmPFC single unit and ensemble level activity corresponded to changes in motivation and quitting in an effortful weight lifting task. Rats carried out two task paradigms: one that became progressively more physically demanding over time and a second fixed effort version. Rats could quit the task at any time. Dorsomedial PFC neurons were highly responsive to each behavioral stage of the task, consisting of rope pulling, reward retrieval, and reward area leaving. Activity was highest early in sessions, commensurate with the highest relative task utility, then decreased until the point of quitting. Neural ensembles consistently represented the sequential behavioral phases of the task. However, these representations were modified over time and became more distinct over the course of the session. These results suggest that dmPFC neurons represent behavioral states that are dynamically modified as behaviors lose their utility, culminating in task quitting.


2021 ◽  
Author(s):  
Maitreyi Sur ◽  
Brian Woodbridge ◽  
Todd C. Esque ◽  
Jim R. Belthoff ◽  
Peter H. Bloom ◽  
...  

2021 ◽  
Author(s):  
Alexander Berne ◽  
Tom Zhang ◽  
Joseph Shomar ◽  
Anggie J. Ferrer ◽  
Aaron Valdes ◽  
...  

AbstractHow animals respond to repeatedly applied stimuli, and how animals respond to mechanical stimuli in particular, are important questions in behavioral neuroscience. We study adaptation to repeated mechanical agitation using theDrosophilalarva. Vertical vibration stimuli elicit a discrete set of responses in crawling larvae: continuation, pause, turn, and reversal. Through high-throughput larva tracking, we characterize how the likelihood of each response depends on vibration intensity and on the timing of repeated vibration pulses. By examining transitions between behavioral states at the population and individual levels, we investigate how the animals habituate to the stimulus patterns. We identify time constants associated with desensitization to prolonged vibration, with re-sensitization during removal of a stimulus, and additional layers of habituation that operate in the overall response. Known memory-deficient mutants exhibit distinct behavior profiles and habituation time constants. An analogous simple electrical circuit suggests possible neural and molecular processes behind adaptive behavior.


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