Project Overview

Developing a brain-supervised artificial neural network to gain insight into how the brain extracts behaviorally relevant information

Faculty Sponsor

Bruce Hansen (


Psychological and Brain Sciences


The neural representation of visual information is not a static pattern, but instead undergoes multiple transformations over time (Hansen et al., 2021), and supports different feature use with differing task demands (Greene & Hansen, 2020).  However, exactly how task-relevant information is built up by the brain and subsequently used by the observer is only vaguely understood.  My lab is currently developing a novel convolutional neural network (CNN) where the convolutional layers are independently supervised by brain responses at different time points. The network is being desinged to use image information evaluated against neural responses to differentiate between two different tasks performed on identical real-world scenes.  The role of this network is to provide insight into how the brain might extract behaviorally relevant infomration to support task completion.  

Student Qualifications

Must be *highly* motivated and intellectually curious.  Successful completion of NEUR 170 or PSYC 275 is required.  Having completed additional courses in computer science and/or mathematics would be ideal.

Number of Student Researchers

2 students

Project Length

10 weeks

Applications open on 10/03/2023 and close on 02/28/2024

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If you have questions, please contact Karyn Belanger (