@article {68838, title = {Small-field visual projection neurons detect translational optic flow and support walking control}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 22}, abstract = {

Animals rely on visual motion for navigating the world, and research in flies has clarified how neural circuits extract information from moving visual scenes. However, the major pathways connecting these patterns of optic flow to behavior remain poorly understood. Using a high-throughput quantitative assay of visually guided behaviors and genetic neuronal silencing, we discovered a region in\ Drosophila{\textquoteright}s protocerebrum critical for visual motion following. We used neuronal silencing, calcium imaging, and optogenetics to identify a single cell type, LPC1, that innervates this region, detects translational optic flow, and plays a key role in regulating forward walking. Moreover, the population of LPC1s can estimate the travelling direction, such as when gaze direction diverges from body heading. By linking specific cell types and their visual computations to specific behaviors, our findings establish a foundation for understanding how the nervous system uses vision to guide navigation.

}, doi = {10.1101/2023.06.21.546024}, url = {https://doi.org/10.1101/2023.06.21.546024}, author = {Mathew D. Isaacson and Jessica L. M. Eliason and Aljoscha Nern and Edward M. Rogers and Gus K. Lott and Tanya Tabachnik and William J. Rowell and Austin W. Edwards and Wyatt L. Korff and Gerald M. Rubin and Kristin Branson and Michael B. Reiser} } @article {67223, title = {Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data}, journal = {CoRR}, volume = {abs/1906.03214}, year = {2019}, month = {10/2019}, abstract = {

The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here, we derive importance weighted extensions to Adversarial Variational Bayes (AVB) and Adversarial Autoencoder (AAE). These latent variable models use implicitly defined inference networks whose approximate posterior density qφ(z|x) cannot be directly evaluated, an essential ingredient for importance weighting. We show improved training and inference in latent variable models with our adversarially trained importance weighting method, and derive new theoretical connections between adversarial generative model training criteria and marginal likelihood based methods. We apply these methods to the important problem of inferring spiking neural activity from calcium imaging data, a challenging posterior inference problem in neuroscience, and show that posterior samples from the adversarial methods outperform factorized posteriors used in VAEs.

}, url = {http://arxiv.org/abs/1906.03214}, author = {Daniel Jiwoong Im and Sridhama Prakhya and Jinyao Yan and Srinivas C. Turaga and Kristin Branson} } @proceedings {46519, title = {Understanding classifier errors by examining influential neighbors.}, year = {2015}, month = {06/2015}, publisher = {IEEE}, address = {Boston, MA}, abstract = {

Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has received little attention. To address this problem, we propose a novel method to analyze and understand a classifier{\textquoteright}s errors. Our method centers around a measure of how much influence a training example has on the classifier{\textquoteright}s prediction for a test example. To understand why a classifier is\ mispredicting\ the label of a given test example, the user can find and review the most influential training examples that caused this\ misprediction, allowing them to focus their attention on relevant areas of the data space. This will aid the user in determining if and how the training data is inconsistently labeled or lacking in diversity, or if the feature representation is insufficient. As computing the influence of each training example is computationally impractical, we propose a novel distance metric to approximate influence for boosting classifiers that is fast enough to be used interactively. We also show several novel use paradigms of our distance metric. Through experiments, we show that it can be used to find incorrectly or inconsistently labeled training examples, to find specific areas of the data space that need more training data, and to gain insight into which features are missing from the current representation.\ 

Code is available at\ https://github.com/kristinbranson/InfluentialNeighbors.

}, author = {Mayank Kabra and Alice A. Robie and Kristin Branson} }