on Update: The coupon code has been issued and is no longer available.
One coupon code for 50% off one 2024 student or postdoctoral membership to the Society for Neuroscience is currently available. The first person to make a request will receive the coupon. To redeem the discount, the trainee will enter the unique code on the Shopping Cart page prior to entering their credit card information.
To receive the coupon code, please contact Eric Chudler at chudler@uw.edu.
on Applications to participate in NeuroHackademy 2024 are now available.
This two-week hands-on workshop will be held in a hybrid format, July 29th- August 10th, 2024 at the University of Washington in Seattle, Washington, USA, and online.
NeuroHackademy is an opportunity for participants to learn state-of-the-art methods for the analysis and management of large human neuroscience datasets while also networking and working with domain experts and each other on concrete neuroscience problems. The curriculum emphasizes large datasets of publicly available data (such as the Human Connectome Project, OpenNeuro, etc.), and on the value of making human neuroscience research open and reproducible.
NeuroHackademy sessions in the first week will include lectures and tutorials on data science, machine learning, data visualization, and data resources, as well as extended Q&A sessions. The second week will be devoted primarily to participant-directed activities: guided work on team projects, hackathon sessions, and breakout sessions on topics of interest. Participants will have an opportunity to present their own work in a session that will take place in the second week of the event.
This event will be held in a hybrid format, with options to attend in-person in Seattle, or online. Participants attending online will join the event through multiple online channels, including zoom-casts of lectures and breakout sessions, Slack conversations, and collaboration through GitHub and through the course’s online Juptyerhub.
For more details and a preliminary list of instructors, see: https://neurohackademy.org/
We are now accepting applications to participate at: https://neurohackademy.org/apply/
Ideally, applicants should have some prior experience with programming and with neuroscience data analysis, but we welcome applications from participants with a variety of relevant backgrounds. For frequently asked questions, please refer to this page: https://neurohackademy.org/frequently-asked-questions/
Accepted applicants will be asked to pay a fee of $250 (in person) / $25 (online) upon final registration. Fees cover housing and two meals per day for in person participants.
Important dates:
April 15th, application deadline
May 6th, notification of acceptance
May 20th, final registration deadline
July 29th – August 10th: NeuroHackademy
on Congratulations to four University of Washington students who have received $500 travel awards from the Pacific Cascade Chapter of the Society for Neuroscience:
Rich Henderson will present “Transcutaneous Spinal Stimulation Improves Exoskeleton Assisted Walking in Spinal Cord Injury: A Pilot Study” at the American Physical Therapy Association Combined Sections Meeting (February 15-17, 2024; Boston, MA).
Mara Kapsner-Smith will present “Auditory Feedback Perturbation of Vocal Parameters Does Not Elicit a Laryngeal Stabilization Response” at the Madonna Motor Speech Conference (February 21-24, 2024; San Diego, CA).
Jingyuan Li will present “Self-supervised Behavior Modeling with Dense Keypoint Tracking” at the Computational and Systems Neuroscience — COSYNE 2024 meeting (February 29 – March 5, 2024; Lisbon, Portugal).
Catherine Rasgaitis will present “Investigating the Neural and Ocular Markers of Facial Perception” at the Social & Affective Neuroscience Society Conference (April 10-13, 2024; Toronto, Canada).
on Ulises Pereira-Obilinovic
Scientist, Allen Institute for Neural Dynamics
Dynamics in Networks with Learning Rules Inferred from Data
HSB G328, January 17, 1:30 PM
https://washington.zoom.us/j/99088001918
Attractor networks are an influential theory for memory storage in brain systems. However, this theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. First, I will present a study of a recurrent network model in which both learning rules and the distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in the inferior temporal cortex (ITC) (Pereira & Brunel, 2018, Neuron). We show that there exist two types of retrieval states: one in which firing rates are constant in time and another in which firing rates fluctuate chaotically. We develop a dynamical mean field theory to analyze the network dynamics and compare the theory with simulations of large networks. In the online learning scenario in which the network learns and forgets continuously we show that for a forgetting timescale that optimizes storage capacity, the qualitative features of the network’s memory retrieval dynamics are age-dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations (Pereira-Obilinovic, Aljadeff, Brunel, 2023, PRX). When these learning rules are temporally asymmetric, they transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using a mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of sequences (Gillett, Pereira, Brunel, 2020, PNAS). We found that multiple characteristics of the recalled attractor, chaotic and sequential activity are consistent with experimental observations. If time permits, I would like to give you a glimpse of how we are using similar network models and analytical tools for building multiregional models constrained by anatomy and trained in Neuropixels recordings and behavior.