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X-WR-CALDESC:Events for ARNI
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DTSTART:20230101T000000
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DTSTART;TZID=UTC:20240412T113000
DTEND;TZID=UTC:20240412T130000
DTSTAMP:20260609T191506
CREATED:20240410T180731Z
LAST-MODIFIED:20240410T180759Z
UID:787-1712921400-1712926800@arni-institute.org
SUMMARY:Adam Charles
DESCRIPTION:Title: Micron brain data at scale: computational challenges in imaging and analysis. \nAbstract: Uncovering the principles of neural computation requires 1) new methods to observe micron-level targets at scale and 2) interpretable models of high-dimensional time-series. In this talk I will cover recent advances in leveraging advanced data models based on latent sparsity and low-dimensionality to tackle key challenges in both domains. First I will discuss ongoing work in multi-photon data analysis. This work seeks to expand our capabilities to extract scientifically rich information from large-scale data of sub-micron targets that represent how circuits compute and how those computations adapt over time. Specifically\, I will discuss recent machine learning image enhancement for tracking synaptic strength in-vivo at scale\, and a morphology-independent image segmentation algorithm for identifying geometrically complex fluorescing objects (e.g.\, dendritic and wide-field imaging). Next I will discuss the analysis challenges if inferring meaningful representations of brain-wide activity provided by imaging advances. Specifically\, brain-wide data represents many parallel and distributed computations. I will discuss recent work building on the intuition of the “neural data manifold”\, and present a decomposed linear dynamical systems (dLDS) model that can capture the nonlinear and non-stationary properties of the neural trajectories along this manifold. dLDS learns a concise model of such dynamics by breaking up the system into several independent\, overlapping systems that are each interpretable as linear systems. I will demonstrate how this model finds meaningful trajectories both in synthetic data and in “whole-brain” C. elegans imaging.
URL:https://arni-institute.org/event/adam-charles/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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DTSTART;TZID=UTC:20240412T150000
DTEND;TZID=UTC:20240412T170000
DTSTAMP:20260609T191506
CREATED:20240319T000436Z
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UID:673-1712934000-1712941200@arni-institute.org
SUMMARY:Animal Behavior Video Analysis Working Group
DESCRIPTION:Title: Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning \nAbstract: The body of an animal determines how the nervous system produces behavior. Therefore\, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly {\em Drosophila melanogaster} in the \mujoco physics engine. Our model is general-purpose\, enabling the simulation of diverse fly behaviors\, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion\, both flight and walking. To support these behaviors\, we have extended \mbox{MuJoCo} with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning\, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. With a visually guided flight task\, we demonstrate a neural controller that can use the vision sensors of the body model to control and steer flight. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context. \nJoin Zoom Meeting:\nhttps://columbiauniversity.zoom.us/j/98060956155?pwd=eVJDY0JOdWV4U1R4emt3dnNPbElWdz09  \nMeeting ID: 980 6095 6155\nPasscode: 263132
URL:https://arni-institute.org/event/animal-behavior-video-analysis-working-group-4/
LOCATION:Zuckerman Institute – L3-079\, 3227 Broadway\, New York\, NY\, 10027\, United States
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