@article {70035, title = {Cell type-specific contributions to a persistent aggressive internal state in female Drosophila}, journal = {Elife}, volume = {12}, year = {2025}, month = {2025 Jul 25}, abstract = {

Persistent internal states are important for maintaining survival-promoting behaviors, such as aggression. In female\ Drosophila melanogaster, we have previously shown that individually activating either aIPg or pC1d cell types can induce aggression. Here we investigate further the individual roles of these cholinergic, sexually dimorphic cell types, and the reciprocal connections between them, in generating a persistent aggressive internal state. We find that a brief 30-second optogenetic stimulation of aIPg neurons was sufficient to promote an aggressive internal state lasting at least 10 minutes, whereas similar stimulation of pC1d neurons did not. While we previously showed that stimulation of pC1e alone does not evoke aggression, persistent behavior could be promoted through simultaneous stimulation of pC1d and pC1e, suggesting an unexpected synergy of these cell types in establishing a persistent aggressive state. Neither aIPg nor pC1d show persistent neuronal activity themselves, implying that the persistent internal state is maintained by other mechanisms. Moreover, inactivation of pC1d did not significantly reduce aIPg-evoked persistent aggression, arguing that the aggressive state did not depend on pC1d-aIPg recurrent connectivity. Our results suggest the need for alternative models to explain persistent female aggression.

Preprint:\ https://www.biorxiv.org/content/10.1101/2023.06.07.543722v2

}, keywords = {Aggression, Animals, Behavior, Animal, Drosophila melanogaster, Female, Neurons, Optogenetics}, issn = {2050-084X}, doi = {10.7554/eLife.88598}, url = {https://doi.org/10.7554/elife.88598}, author = {Chiu, Hui and Robie, Alice A and Branson, Kristin and Vippa, Tanvi and Epstein, Samantha and Rubin, Gerald M and Anderson, David J and Schretter, Catherine E} } @article {69783, title = {Cortical control of innate behavior from subcortical demonstration}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 13}, abstract = {

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1{\textendash}3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5{\textendash}7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Na{\"\i}ve mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.

}, doi = {10.1101/2025.02.12.637930}, url = {https://www.biorxiv.org/content/early/2025/02/13/2025.02.12.637930}, author = {Keller, Jason A. and Kwak, Iljung S. and Stark, Alyssa K. and Pachitariu, Marius and Branson, Kristin and Dudman, Joshua T.} } @article {65259, title = {Cortical pattern generation during dexterous movement is input-driven.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 Jan 16}, pages = {386-91}, abstract = {

The motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centres. Local cortical dynamics are thought to shape these patterns throughout movement execution. External inputs have been implicated in setting the initial state of the motor cortex, but they may also have a pattern-generating role. Here we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching or failed to generate this pattern. The difference in these two outcomes was probably a result of external inputs. We directly investigated the role of inputs by inactivating the thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly interacting brain regions.

}, issn = {1476-4687}, doi = {10.1038/s41586-019-1869-9}, author = {Sauerbrei, Britton A and Guo, Jian-Zhong and Cohen, Jeremy D and Mischiati, Matteo and Guo, Wendy and Kabra, Mayank and Verma, Nakul and Mensh, Brett and Branson, Kristin and Hantman, Adam W} } @article {65179, title = {Computational neuroethology: A call to action.}, journal = {Neuron}, volume = {104}, year = {2019}, month = {2019 Oct 09}, pages = {11-24}, abstract = {

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2019.09.038}, author = {Datta, Sandeep Robert and Anderson, David J and Branson, Kristin and Perona, Pietro and Leifer, Andrew} } @article {47433, title = {Computational Analysis of Behavior.}, journal = {Annual Review of Neuroscience}, volume = {39}, year = {2016}, month = {2016 Apr 18}, pages = {217-36}, abstract = {

In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with. Expected final online publication date for the Annual Review of Neuroscience Volume 39 is July 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-070815-013845}, author = {Egnor, S E Roian and Branson, Kristin} } @article {47277, title = {Cortex commands the performance of skilled movement.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Dec 3}, abstract = {

Mammalian cerebral cortex is accepted as being critical for voluntary motor control, but what functions depend on cortex is still unclear. Here we used rapid, reversible optogenetic inhibition to test the role of cortex during a head-fixed task in which mice reach, grab, and eat a food pellet. Sudden cortical inhibition blocked initiation or froze execution of this skilled prehension behavior, but left untrained forelimb movements unaffected. Unexpectedly, kinematically normal prehension occurred immediately after cortical inhibition even during rest periods lacking cue and pellet. This {\textquoteright}rebound{\textquoteright} prehension was only evoked in trained and food-deprived animals, suggesting that a motivation-gated motor engram sufficient to evoke prehension is activated at inhibition{\textquoteright}s end. These results demonstrate the necessity and sufficiency of cortical activity for enacting a learned skill.

}, issn = {2050-084X}, doi = {10.7554/eLife.10774}, author = {Guo, Jian-Zhong and Graves, Austin R and Guo, Wendy W and Zheng, Jihong and Lee, Allen and Rodr{\'\i}guez-Gonz{\'a}lez, Juan and Li, Nuo and Macklin, John J and Phillips, James W and Mensh, Brett D and Branson, Kristin and Hantman, Adam W} }