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BE Seminar: “A Task-Optimized Approach to Systems Neuroscience” (Aran Nayebi, MIT)

April 4 at 3:30 PM - 4:30 PM

Note that this seminar will be held in Wu & Chen Auditorium (Levine 101).
Humans and animals exhibit a range of interesting behaviors in complex environments, and it is unclear how the brain reformats dense sensory information to enable these behaviors. To gain traction on this problem, new recording paradigms now facilitate the ability to record and manipulate hundreds to thousands of neurons in awake, behaving animals. Consequently, a pressing need arises to distill these data into interpretable insights about how neural circuits give rise to intelligent behaviors.
To engage with these issues, I take a computational approach, known as “task-optimized modeling”, that leverages recent advancements in artificial intelligence (AI) to express hypotheses for the evolutionary constraints of neural circuits. These constraints guide the iterative optimization of artificial neural networks to achieve a specific behavior (“task”). By carefully analyzing the factors that contribute to model fidelity in predicting large-scale neural response patterns, we can gain insight into why certain brain areas respond as they do, and what selective pressures over evolutionary and developmental timescales give rise to the diversity of observed neural responses.
In this talk, I apply this approach to examine the functional constraints of brain areas involved in the perception-action loop across multiple timescales: 1. the role of recurrent processing in rapid object recognition (within 250 ms), and 2. visually-grounded mental simulation of future environmental states (within several seconds). Finally, I conclude with future directions towards closing the perception-action loop by extending task-optimized modeling to build integrative, embodied agents to gain a systems-level understanding of an organism’s brain. These agents would serve as normative accounts of how brain areas collaborate to enable meaningful actions in the physical world. Their design will elucidate the algorithmic principles of natural intelligence conserved across species, and yield safer, more grounded embodied AI algorithms.

Aran Nayebi, Ph.D.

ICoN Postdoctoral Fellow, MIT

Aran Nayebi is an ICoN Postdoctoral Fellow at MIT, currently working with Robert Yang and Mehrdad Jazayeri. He completed his PhD in Neuroscience at Stanford University, co-advised by Daniel Yamins and Surya Ganguli. His interests lie at the intersection of neuroscience and artificial intelligence (AI), where he uses tools from AI and optimization to better understand natural intelligence. His work has been published in top machine learning venues (e.g. NeurIPS, ICML), including 5 NeurIPS spotlight and oral papers (awarded to the top 1-3% of submissions), as well as top neuroscience venues (e.g. Cell Reports, PLOS Computational Biology, PNAS, Neuron). His long-term aim is to focus on the sensorimotor loop essential for survival and physical interaction, in order to produce normative accounts of how brain areas collaborate to give rise to complex embodied behaviors, yielding more physically-grounded, common-sense AI algorithms along the way.

Details

Date:
April 4
Time:
3:30 PM - 4:30 PM
Event Category:
Event Tags:
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Organizer

Bioengineering
Phone
215-898-8501
Email
be@seas.upenn.edu
View Organizer Website

Venue

Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street
Philadelphia, PA 19104 United States + Google Map
View Venue Website