Highlights d Firing across hippocampal neurons can regularly ''take turns'' (cycle) every 125 ms d Cycle firing is seen at single-cell, cell-pair, and population levels d Cycle firing encodes hypothetical experience, including multiple possible futures

Abstract. We describe a system that tracks pairs of fruit flies and automatically detects and classifies their actions. We compare experimentally the value of a frame-level feature representation with the more elaborate notion of 'bout features' that capture the structure within actions. Similarly, we compare a simple sliding window classifier architecture with a more sophisticated structured output architecture, and find that window based detectors outperform the much slower structured counterparts, and approach human performance. In addition we test our top performing detector on the CRIM13 mouse dataset, finding that it matches the performance of the best published method. Our Fly-vs-Fly dataset contains 22 hours of video showing pairs of fruit flies engaging in 10 social interactions in three different contexts; it is fully annotated by experts, and published with articulated pose trajectory features.

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Subthalamic nucleus deep brain stimulation (STN DBS) relieves many motor symptoms of Parkinson's Disease (PD), but its underlying therapeutic mechanisms remain unclear. Since its advent, three major theories have been proposed: (1) DBS inhibits the STN and basal ganglia output; (2) DBS antidromically activates motor cortex; and (3) DBS disrupts firing dynamics within the STN. Previously, stimulation-related electrical artifacts limited mechanistic investigations using electrophysiology. We used electrical artifact-free GCaMP fiber photometry to investigate activity in basal ganglia nuclei during STN DBS in parkinsonian mice. To test whether the observed changes in activity were sufficient to relieve motor symptoms, we then combined electrophysiological recording with targeted optical DBS protocols. Our findings suggest that STN DBS exerts its therapeutic effect through the disruption of movement-related STN activity, rather than inhibition or antidromic activation. These results provide insight into optimizing PD treatments and establish an approach for investigating DBS in other neuropsychiatric conditions.

COVID-19 has become a significant burden on the healthcare systems in the United States and around the world, with many patients requiring invasive mechanical ventilation (IMV) to survive. Close monitoring of patients is critical, with total volume per breath (tidal volume) being one of the most important data points. However, ventilators are complex and expensive devices, typically in the range of tens of thousands of US dollars, and are challenging to manufacture, typically requiring months. Solutions which could augment the ventilator supply rapidly and at low cost in the United States and elsewhere would be valuable. In this paper, we present a standalone tidal volume measurement system consisting of a D-Lite spirometer, pressure sensor, microcontroller, and tubing with a cost of parts less than $50 USD. We also provide a model to predict the error in tidal volume measurements based on the pressure sensor used and the flow during ventilation. We validate this system and show that the tidal volume accuracy for flows above 10L/min was within 10%. We envision this system being used to increase the ventilator supply in resource-constrained settings.