@article {67821, title = {A genetically defined insula-brainstem circuit selectively controls motivational vigor.}, journal = {Cell}, year = {2021}, month = {2021 Dec 07}, abstract = {

The anterior insular cortex (aIC) plays a critical role in cognitive and motivational control of behavior, but the underlying neural mechanism remains elusive. Here, we show that aIC neurons expressing Fezf2 (aIC), which are the pyramidal tract neurons, signal motivational vigor and invigorate need-seeking behavior through projections to the brainstem nucleus tractus solitarii (NTS). aIC neurons and their postsynaptic NTS neurons acquire anticipatory activity through learning, which encodes the perceived value and the vigor of actions to pursue homeostatic needs. Correspondingly, aIC {\textrightarrow} NTS circuit activity controls vigor, effort, and striatal dopamine release but only if the action is learned and the outcome is needed. Notably, aIC neurons do not represent taste or valence. Moreover, aIC {\textrightarrow} NTS activity neither drives reinforcement nor influences total consumption. These results pinpoint specific functions of aIC {\textrightarrow} NTS circuit for selectively controlling motivational vigor and suggest that motivation is subserved, in part, by aIC{\textquoteright}s top-down regulation of dopamine signaling.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.11.019}, author = {Deng, Hanfei and Xiao, Xiong and Yang, Tao and Ritola, Kimberly and Hantman, Adam and Li, Yulong and Huang, Z Josh and Li, Bo} } @article {67765, title = {Genetically identified amygdala-striatal circuits for valence-specific behaviors.}, journal = {Nature Neuroscience}, volume = {24}, year = {2021}, month = {2021 Oct 18}, pages = {1586-1600}, abstract = {

The basolateral amygdala (BLA) plays essential roles in behaviors motivated by stimuli with either positive or negative valence, but how it processes motivationally opposing information and participates in establishing valence-specific behaviors remains unclear. Here, by targeting Fezf2-expressing neurons in the BLA, we identify and characterize two functionally distinct classes in behaving mice, the negative-valence neurons and positive-valence neurons, which innately represent aversive and rewarding stimuli, respectively, and through learning acquire predictive responses that are essential for punishment avoidance or reward seeking. Notably, these two classes of neurons receive inputs from separate sets of sensory and limbic areas, and convey punishment and reward information through projections to the nucleus accumbens and olfactory tubercle, respectively, to drive negative and positive reinforcement. Thus, valence-specific BLA neurons are wired with distinctive input-output structures, forming a circuit framework that supports the roles of the BLA in encoding, learning and executing valence-specific motivated behaviors.

}, issn = {1546-1726}, doi = {10.1038/s41593-021-00927-0}, author = {Zhang, Xian and Guan, Wuqiang and Yang, Tao and Furlan, Alessandro and Xiao, Xiong and Yu, Kai and An, Xu and Galbavy, William and Ramakrishnan, Charu and Deisseroth, Karl and Ritola, Kimberly and Hantman, Adam and He, Miao and Josh Huang, Z and Li, Bo} } @conference {65612, title = {Detecting the Starting Frame of Actions in Video}, booktitle = {2020 IEEE Winter Conference on Applications of Computer Vision (WACV)}, year = {2020}, month = {2020 May 14}, publisher = {IEEE}, organization = {IEEE}, address = {Snowmass Village, CO, USA}, abstract = {

In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of neuroscience, in which we would like to understand the neural activity that produces the start of a bout of an action. To address this problem, we introduce a novel structured loss function that properly weights the types of errors that matter in such applications: it more heavily penalizes extra and missed action start detections over small misalignments. Our structured loss is based on the best matching between predicted and labeled action starts. We train recurrent neural networks (RNNs) to minimize differentiable approximations of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. On this dataset, we demonstrate that our method outperforms related approaches and baseline methods using an unstructured loss.

}, doi = {10.1109/WACV45572.202010.1109/WACV45572.2020.9093405}, url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9087828}, author = {Kwak, Iljung S. and Guo, Jian-Zhong and Hantman, Adam and Branson, Kristin and Kriegman, David} } @article {65739, title = {A genetically defined compartmentalized striatal direct pathway for negative reinforcement.}, journal = {Cell}, volume = {181}, year = {2020}, month = {2020 Oct 1}, pages = {211}, abstract = {

The striosome compartment within the dorsal striatum has been implicated in reinforcement learning and regulation of motivation, but how striosomal neurons contribute to these functions remains elusive. Here, we show that a genetically identified striosomal population, which expresses the Teashirt family zinc finger 1 (Tshz1) and belongs to the direct pathway, drives negative reinforcement and is essential for aversive learning in mice. Contrasting a "conventional" striosomal direct pathway, the Tshz1 neurons cause aversion, movement suppression, and negative reinforcement once activated, and they receive a distinct set of synaptic inputs. These neurons are predominantly excited by punishment rather than reward and represent the anticipation of punishment or the motivation for avoidance. Furthermore, inhibiting these neurons impairs punishment-based learning without affecting reward learning or movement. These results establish a major role of striosomal neurons in behaviors reinforced by punishment and moreover uncover functions of the direct pathway unaccounted for in classic models.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.08.032}, author = {Xiao, Xiong and Deng, Hanfei and Furlan, Alessandro and Yang, Tao and Zhang, Xian and Hwang, Ga-Ram and Tucciarone, Jason and Wu, Priscilla and He, Miao and Palaniswamy, Ramesh and Ramakrishnan, Charu and Ritola, Kimberly and Hantman, Adam and Deisseroth, Karl and Osten, Pavel and Huang, Z Josh and Li, Bo} }