[WTI-trainee] APHINE Presents Grace Lindsay, November 27th, 12:30 PM

[Natalia Castelo Branco Matos]

We are thrilled to announce an upcoming session of our neurophilosophy
seminar series featuring *Professor Grace Lindsay* from New York University.

🎙️ Talk Title: "Analyzing artificial neural networks to understand the
brain"
📅 Date: Mon, November 27th, 2023
🕒 Time: 12:30 - 2:00 PM
📍 Location: 100 College Street, Room 1167

*About the Speaker: *Dr. Grace Lindsay is an Assistant Professor of
Psychology and Data Science at New York University, with a background that
includes a BS in Neuroscience from the University of Pittsburgh and a PhD
from Columbia University's Center for Theoretical Neuroscience. Her
research is focused on using artificial neural networks to understand brain
function, particularly in the realms of attention and sensory processing.
Dr. Lindsay also explores the efficacy of current tools in interpreting
neural activity. Additionally, Dr. Lindsay has authored a popular science
book titled "Models of the Mind
<https://yale.us14.list-manage.com/track/click?u=b04ef1c576332f4626a1db32b&id=d630c1badf&e=8691455c16>",
where she delves into the intersection of mathematics, physics,
engineering, and neuroscience. If you are interested in learning more about
her work, please visit Dr. Lindsay's personal webpage
<https://yale.us14.list-manage.com/track/click?u=b04ef1c576332f4626a1db32b&id=ee23d738d5&e=8691455c16>
.

*Talk Abstract:* In the first part of this talk, I will present work
showing that artificial neural networks with recurrent connections can
replicate broad behavioral patterns associated with dynamic visual object
recognition in humans. An analysis of these networks shows that different
types of recurrence use different strategies to solve the object
recognition problem. The analysis of these networks, however, introduces
another question: are the tools of neuroscience suitable for understanding
complex distributed information processing systems? In the second part of
this talk, I will discuss—and solicit feedback on—a research plan for
testing a wide range of analysis tools frequently applied to neural data on
artificial neural networks. I will present the motivation for this approach
as well as the form the results could take and how this would benefit
neuroscience and the field of interpretable AI.
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