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Modeling, Analysis, and Theory Intern - Mouse Visual Cortex

Department: 2260-Modeling Analysis and Theory
Location: Seattle


Performance comparisons of supervised learning algorithms with Direction Reversing Neurons in the mouse visual cortex

Project Overview:

We have observed the presence of Direction Reversing Neurons (DRNs) in both calcium imaging data and electrophysiology data in all visually responsive brain areas (cortical areas and LGN) and have developed a mechanistic model to describe the phenomenon. This project leverages datasets collected at the Allen Institute (optical and electrical physiology) to ask specific questions about DRNs, which constitute a large fraction of direction selective neurons (18% based on targeted experiments). In particular we seek to determine if DRNs can improve/worsen the classification accuracy of different classifiers. Are there any

classifiers that perform better in the presence of DRNs? Moreover, since DRN abundance increases up the visual hierarchy, how is decoding effected by DRNs in different cortical areas? Further, for calcium imaging data, are there any differences in classification accuracy across Cre-lines?

The project will primarily entail implementing supervised learning algorithms on our collected datasets to study the aforementioned questions.

This project is a great introductory undertaking for a student that will give them strong exposure to Visual Neuroscience, calcium imaging as well as spiking data, and Machine Learning (supervised learning in particular), with clear questions relevant to a manuscript we are preparing.

Educational Objectives:

  • Introduction to electrophysiological data
  • Introduction to calcium imaging data Ė via the AllenSDK
  • Introduction to Cre-lines and neuronal classes
  • Introduction to the visual processing hierarchy
  • Statistical analysis (parametric and non-parametric)
  • Supervised training algorithms
    • Random forests
    • K-nearest neighbors
    • Linear Discriminant Analysis (LDA)
    • Support Vector Machines (SVM)
    • NaÔve Bayes
    • Logistic regression
  • Python skills:
    • Analysis of data using Pandas, Numpy
    • Visualization with Matplotlib
    • o Machine Learning with Scipy, Skitlearn


  • U.S. Citizen or U.S. Permanent Resident currently enrolled in an accredited U.S. college or university.
  • Enrolled in undergraduate studies or in a Masterís program. You must be enrolled and returning to school Fall quarter.
  • Must be able to start on either June 3rd or June 17th, 2019 and commit to the full 10-12 week internship time period. Interns will be expected to work 35-40 hours a week.
  • Must be 18 years of age or older.


Interns will receive $15.00 per hour which is equivalent to $6,000 less taxes.


Housing is not provided as a part of the internship program. If selected we will provide a list of housing resources for you.


Interns will be provided with an ORCA business passport free of charge for the duration of their internship.

How to Apply

  1. Complete an application through this portal. The application deadline is January 30, 2019.
  2. Submit a resume as well as the following documents using the + supplemental documents button:
    • A personal statement describing your interest in Allen Institute for Brain Science
    • College transcript with Cumulative GPA and Courses taken (can be unofficial)

3. Send one letter of recommendation to

Contact us

If you have any further questions please contact us at

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