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5 Janelia Publications

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    10/01/20 | A genetically defined compartmentalized striatal direct pathway for negative reinforcement.
    Xiao X, Deng H, Furlan A, Yang T, Zhang X, Hwang G, Tucciarone J, Wu P, He M, Palaniswamy R, Ramakrishnan C, Ritola K, Hantman A, Deisseroth K, Osten P, Huang ZJ, Li B
    Cell. 2020 Oct 1;181(1):211. doi: 10.1016/j.cell.2020.08.032

    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.

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    09/09/20 | Cell-type specific outcome representation in primary motor cortex.
    Lavzin M, Levy S, Benisty H, Dubin U, Brosh Z, Aeed F, Mensh BD, Schiller Y, Meir R, Barak O, Talmon R, Hantman AW, Schiller J
    Neuron. 2020 Sep 9;107(5):954-71. doi: 10.1016/j.neuron.2020.06.006

    Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, “success”- and “failure” related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial’s outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.

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    08/30/20 | Parvalbumin+ and Npas1+ Pallidal neurons have distinct circuit topology and function.
    Pamukcu A, Cui Q, Xenias HS, Berceau BL, Augustine EC, Fan I, Hantman AW, Lerner TN, Boca SM, Chan CS
    Journal of Neuroscience. 2020 Aug 30:
    05/14/20 | Detecting the Starting Frame of Actions in Video
    Kwak IS, Guo J, Hantman A, Branson K, Kriegman D
    2020 IEEE Winter Conference on Applications of Computer Vision (WACV). 2020 May 14:. doi: 10.1109/WACV45572.202010.1109/WACV45572.2020.9093405

    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.

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    01/16/20 | Cortical pattern generation during dexterous movement is input-driven.
    Sauerbrei BA, Guo J, Cohen JD, Mischiati M, Guo W, Kabra M, Verma N, Mensh B, Branson K, Hantman AW
    Nature. 2020 Jan 16;577(7790):386-91. doi: 10.1038/s41586-019-1869-9

    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.

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