Upcoming Events

  • May
    7
    Virtual
    3:30pm - 4:30pm

    ICoN T32 Training Program Open House

    Join Virtual EventInstructions: Please note, Brown authentication is required to attend this event. 

    The Interdisciplinary Training in Computational, Cognitive, and Systems Neuroscience (ICoN) is a pre-doctoral program in computational cognitive neuroscience. Funds from this program will support the training of advanced pre-doctoral candidates who are capable of applying a combination of empirical and theoretical approaches that decisively addresses their scientific questions about the mind and brain. 

    On May 7 at 3:30 p.m., join the PIs and current students to learn about the ICoN training program and how to apply to join the next cohort of students.  

    Applications for this year’s program are due May 21, 2021. 

    More Information Biology, Medicine, Public Health, CCBS, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Join Virtual EventInstructions: Passcode: 795504

    Join Carney’s Center for Computational Brain Science (CCBS) on May 25 for a seminar on “Extracting structure from high-dimensional neural data,” featuring Carsen Stringer, computational neuroscientist and group leader at the Howard Hughes Medical Institute Janelia Research Campus.

    Stringer completed her postdoctoral work with Marius Pachitariu and Karel Svoboda at Janelia, and her Ph.D. work with Kenneth D. Harris and Matteo Carandini at University College London. She develops tools for understanding high-dimensional visual computations and neural representations of behavior.

    Abstract

    Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. Compared to standard algorithms such as t-SNE and UMAP, Rastermap finds finer and higher dimensional patterns of neural variability, as measured by quantitative benchmarks. We applied Rastermap to a variety of datasets, including spontaneous neural activity, neural activity during a virtual reality task, widefield neural imaging data during a 2AFC task, artificial neural activity from a bipedal robot simulation, and neural responses to visual textures. We additionally found that texture identity could be decoded from these neural responses, but that the neural representations of visual texture differed from artificial neural network representations.

    More Information CCBS, Psychology & Cognitive Sciences, Research