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Yartsev, M.

Publications and source records attributed to Yartsev, M..

3 recordsLinked to original sources

Motor Cortical Computations Underlying Natural Dexterous Movement in Freely Flying Bats

Elucidating the neural computations underlying natural, complex movement remains a fundamental challenge in neuroscience. Bat flight presents a formidable motor control challenge, requiring the use of hand-like wings whose many degrees of freedom must be precisely coordinated to enable rapid three-dimensional maneuvers. Here we performed large-scale wireless recordings of neuronal ensembles from the wing motor cortex of freely flying bats using Neuropixels probes, alongside detailed 3D pose tracking of wing kinematics. Despite the complexity of flight control, bats repeatedly executed highly accurate flights through precise adjustments of individual wingbeats. Surprisingly, motor cortical activity was not dominated by the global wingbeat cycle. Instead, individual neurons were sparsely active, exhibiting mixed selectivity for specific flight kinematics combined with variable entrainment to the wingbeat phase reaching millisecond-scale precision. This yielded a high-dimensional population regime driven by low shared variance across wingbeats, with successive wingbeats occupying distinct neural population states. Our findings reveal that during complex natural behavior the mammalian motor cortex operates in a high-dimensional computational regime that challenges prevailing views of motor cortical computation and underscores the importance of studying ethologically relevant behaviors to uncover neural principles governing brain function.

neuroscience↗

Ultrafast Frame-Free Imaging of Neural Activity with Event Cameras

Frame-based fluorescence imaging has long defined how neural activity is optically measured. This approach requires acquiring all pixels within an image, regardless of whether they carry meaningful neural dynamics, thereby intrinsically coupling spatial and temporal resolution while increasing data output. Here, we introduce an entirely different, frame-free approach that leverages the sparse nature of neural activity using event-based cameras, which asynchronously report fluorescence changes as spatiotemporal events. Compared with a frame-based camera, our method preserves signal fidelity while eliminating the fixed trade-off between spatial resolution, temporal resolution and data rate, thereby reducing data output by orders of magnitude. Applied to hippocampal preparations we demonstrate that the frame-free approach can resolve both single action potentials and fast network dynamics over large fields of view at kilohertz rates, enabling scalable, ultrafast optical recordings.

neuroscience↗

A Hierarchical Anti-Hebbian Network Model for the Formation of Spatial Cells in Three-Dimensional Space

Three dimensional (3D) spatial cells in the mammalian hippocampalformationare believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here, we present a hierarchical network model for the formation of 3D spatial cells using anti-hebbian network. Built on empirical data, the model accounts for the natural emergence of 3D place, border and grid-cells as well as a new type of previously undescribed spatial cell type which we call plane cells. It further explains the plausible reason behind the place and grid-cell anisotropic coding that has been observed in rodents and the potential discrepancy with the predicted periodic coding during 3D volumetric navigation. Lastly, it provides evidence for the importance of unsupervised learning rules in guiding the formation of higher dimensional cognitive maps.

neuroscience↗