The spatial and temporal structure of neural activity across the fly brain

Population coding of predator imminence in the hypothalamus

Hypothalamic VMHdm SF1 neurons are activated by predator cues and are necessary and sufficient for instinctive defensive responses. However, such data do not distinguish which features of a predator encounter are encoded by VMHdm SF1 neural activity. To address this issue, we imaged VMHdm SF1 neurons at single-cell resolution in freely behaving mice exposed to a natural predator in varying contexts. Our results reveal that VMHdm SF1 neurons do not represent different defensive behaviors, but rather encode predator identity and multiple predator-evoked internal states, including threat-evoked fear/anxiety; neophobia or arousal; predator imminence; and safety. Notably, threat and safety are encoded bi-directionally by anti-correlated subpopulations. Finally, individual differences in predator defensiveness are correlated with differences in VMHdm SF1 response dynamics. Thus, different threat-related internal state variables are encoded by distinct neuronal subpopulations within a genetically defined, anatomically restricted hypothalamic cell class.

Highlights

Distinct subsets of VMHdm SF1 neurons encode multiple predator-evoked internal states. Anti-correlated subsets encode safety vs. threat in a bi-directional mannerA population code for predator imminence is identified using a novel assay VMHdm SF1 dynamics correlate with individual variation in predator defensiveness.

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms

Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.

The influence of emotion on temporal context models

Temporal context models (TCMs) have been influential in understanding episodic memory and its neural underpinnings. Recently, TCMs have been extended to explain emotional memory effects, one of the most clinically important findings in the field of memory research. This review covers recent advances in hypotheses for the neural representation of spatiotemporal context through the lens of TCMs, including their ability to explain the influence of emotion on episodic and temporal memory. In recent years, simplifying assumptions of "classical" TCMs - with exponential trace decay and the mechanism by which temporal context is recovered - have become increasingly clear. The review also outlines how recent advances could be incorporated into a future TCM, beyond classical assumptions, to integrate emotional modulation.

Building and integrating brain-wide maps of nervous system function in invertebrates

The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.

Coordination and persistence of aggressive visual communication in Siamese fighting fish

Animals coordinate their behavior with each other during both cooperative and agonistic social interactions. Such coordination often adopts the form of "turn taking", in which the interactive partners alternate the performance of a behavior. Apart from acoustic communication, how turn taking between animals is coordinated is not well understood. Furthermore, the neural substrates that regulate persistence in engaging in social interactions are poorly studied. Here, we use Siamese fighting fish ( Betta splendens ), to study visually-driven turn-taking aggressive behavior. Using encounters with conspecifics and with animations, we characterize the dynamic visual features of an opponent and the behavioral sequences that drive turn taking. Through a brain-wide screen of neuronal activity during coordinated and persistent aggressive behavior, followed by targeted brain lesions, we find that the caudal portion of the dorsomedial telencephalon, an amygdala-like region, promotes persistent participation in aggressive interactions, yet is not necessary for coordination. Our work highlights how dynamic visual cues shape the rhythm of social interactions at multiple timescales, and points to the pallial amygdala as a region controlling engagement in such interactions. These results suggest an evolutionarily conserved role of the vertebrate pallial amygdala in regulating the persistence of emotional states.

Visually guided and context-dependent spatial navigation in the translucent fish Danionella cerebrum

Danionella cerebrum (DC) is a promising vertebrate animal model for systems neuroscience due to its small adult brain volume and inherent optical transparency, but the scope of their cognitive abilities remains an area of active research. In this work, we established a behavioral paradigm to study visual spatial navigation in DC and investigate their navigational capabilities and strategies. We initially observed that adult DC exhibit strong negative phototaxis in groups but less so as individuals. Using their dark preference as a motivator, we designed a spatial navigation task inspired by the Morris water maze. Through a series of environmental cue manipulations, we found that DC utilize visual cues to anticipate a reward location and found evidence for landmark-based navigational strategies wherein DC could use both proximal and distal visual cues. When subsets of proximal visual cues were occluded, DC were capable of using distant contextual visual information to solve the task, providing evidence for allocentric spatial navigation. Without proximal visual cues, DC tended to seek out a direct line of sight with at least one distal visual cue while maintaining a positional bias toward the reward location. In total, our behavioral results suggest that DC can be used to study the neural mechanisms underlying spatial navigation with cellular resolution imaging across an adult vertebrate brain.