Title: Learning Dynamics and Identifying Neurons in Large Neural Populations
Abstract:
We are entering an age where scientists routinely record from thousands of neurons in a single experiment. Analyzing this data presents a challenge both for scaling existing algorithms and designing new ones suited to the increase in complexity. I will discuss two projects aimed at addressing these problems. First, I will discuss joint work with Eftychios Pnevmatikakis on learning low-dimensional dynamical systems with GLM outputs. Our approach combines a nuclear norm regularizer on the dimension of the state space with a generalized linear model output, which makes it possible to recover neural trajectories directly from unsmoothed spike trains, even in the presence of strong rectifying nonlinearities. Secondly, I will discuss joint work with Misha Ahrens and Jeremy Freeman on automatically identifying regions of interest (ROI) from whole-brain calcium recordings. We have developed a pipeline for ROI detection that scales to the very large datasets made possible by light-sheet microscopy that can run on a single GPU-enabled desktop. We automatically extract >2000 ROIs from whole-brain spontaneous activity in the larval zebrafish, which is to our knowledge the largest number of ROIs extracted from a single calcium imaging experiment via an activity-based fully automated method. Applying our nuclear-norm dimensionality reduction technique to the extracted firing rates, we find patterns of activity that more accurately reflect populations-level activity than PCA.
Abstract:
We are entering an age where scientists routinely record from thousands of neurons in a single experiment. Analyzing this data presents a challenge both for scaling existing algorithms and designing new ones suited to the increase in complexity. I will discuss two projects aimed at addressing these problems. First, I will discuss joint work with Eftychios Pnevmatikakis on learning low-dimensional dynamical systems with GLM outputs. Our approach combines a nuclear norm regularizer on the dimension of the state space with a generalized linear model output, which makes it possible to recover neural trajectories directly from unsmoothed spike trains, even in the presence of strong rectifying nonlinearities. Secondly, I will discuss joint work with Misha Ahrens and Jeremy Freeman on automatically identifying regions of interest (ROI) from whole-brain calcium recordings. We have developed a pipeline for ROI detection that scales to the very large datasets made possible by light-sheet microscopy that can run on a single GPU-enabled desktop. We automatically extract >2000 ROIs from whole-brain spontaneous activity in the larval zebrafish, which is to our knowledge the largest number of ROIs extracted from a single calcium imaging experiment via an activity-based fully automated method. Applying our nuclear-norm dimensionality reduction technique to the extracted firing rates, we find patterns of activity that more accurately reflect populations-level activity than PCA.
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