Below are selected invited talk slides (with available recordings) and conference posters. For all talk slides and posters, they are in my CV as embedded links.

Selected Invited Talks

(Caveat lector: These slides summarize what I thought at the time I gave the talk, and therefore can sometimes be outdated. Feel free to refer to my papers for the most up-to-date information, or contact me about any questions or attributions.)

Task-optimized models of the brain. Machine Learning Department Job Talk, Carnegie Mellon University (CMU). 16 April 2024. Pittsburgh, PA.

Using Embodied AI to help answer “why” questions in systems neuroscience. Center for Brains, Minds and Machines (CBMM) Research Meeting, Massachusetts Institute of Technology (MIT). 19 September 2023. Cambridge, MA. [talk recording]

We probably need a new idea…or two?. Conference on Cognitive Computational Neuroscience (CCN) GAC 2023, Oxford University. 25 August 2023. Oxford, UK. [talk recording]

Neural foundations of mental simulation: future prediction of latent representations on dynamic scenes. Massachusetts Institute of Technology (MIT) BCS/MIBR/PILM Retreat. 4 June 2023. North Falmouth, MA.

Principled, goal-driven models to investigate structure and function in neural circuits. 14 December 2022 (Cold Spring Harbor Laboratory); 23 February 2023 (Harvard Medical School); 6 March 2023 (Yale Wu Tsai Institute).

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation. World Wide NeuRise (WWNeuRise). 2 November 2022. Virtual. [talk recording]

A goal-driven approach to systems neuroscience. PhD Dissertation Defense. 15 March 2022. Stanford, CA. [talk recording]

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks. Neuromatch 4.0 Flash Talk (also a NeurIPS 2021 Spotlight Presentation). Longer version presented at Stanford Computational Neuroscience Journal Club (CNJC). 1 December 2021. Virtual. [talk recording]

A model-based approach towards identifying the brain’s learning algorithms. Stanford Mind, Brain, Computation, and Technology Seminar. 25 January 2021. Virtual. [talk recording]

Identifying learning rules from neural network observables. Neural Information Processing Systems (NeurIPS) 2020 Spotlight Presentation. 10 December 2020. Virtual. [talk recording] [Neuromatch 3.0 talk recording w/ Q&A]

Assessing the role of feedback connections in artificial and biological neural networks. Stanford Mind, Brain, Computation, and Technology Seminar. 18 May 2020. Virtual.

Two routes to scalable credit assignment without weight symmetry. Presented with J. Sagastuy-Brena. International Conference on Machine Learning (ICML) 2020. 12-18 July 2020. Virtual. [talk recording]

Task-driven convolutional recurrent neural network models of dynamics in higher visual cortex. Society for Neuroscience (SfN) 2019. Minisymposium on Artificial Intelligence and Neuroscience. 21 October 2019. Chicago, IL.

Task-driven recurrent models & dissecting neural computations in silico. Bernstein Conference 2019. Brain against the Machine Workshop. 18 September 2019. Berlin, Germany.

Measuring and modeling the weight dynamics of many synapses onto diverse cell-types in vivo. Computational and Systems Neuroscience (Cosyne) 2019. Talk T-36. 3 March 2019. Lisbon, Portugal. [talk recording]

Convolutional recurrent neural network models of dynamics in higher visual cortex. Vision Sciences Society (VSS) Meeting 2018. 21 May 2018. St. Pete Beach, FL.

Selected Conference Posters

A. Nayebi, R. Rajalingham, M. Jazayeri, G.R. Yang. Neural foundations of mental simulation: future prediction of latent representations on dynamic scenes. Neural Information Processing Systems (NeurIPS) 2023. Poster #417. 14 December 2023. New Orleans, LA.

A. Nayebi, R. Rajalingham, M. Jazayeri, G.R. Yang. Neural mechanisms of mental simulation in primate frontal cortex. Conference on Cognitive Computational Neuroscience (CCN) 2023. Poster #P-1B.114. 24 August 2023. Oxford, UK.

A. Nayebi*, N.C.L. Kong*, C. Zhuang, J.L. Gardner, A.M. Norcia, D.L.K. Yamins. Mouse visual cortex as a limited-resource system that self-learns a task-general representation. Computational and Systems Neuroscience (Cosyne) 2023. Poster III-002. 11 March 2023. Montreal, Canada.

A. Nayebi*, S. Srivastava*, S. Ganguli, D.L.K. Yamins. Identifying learning rules from neural network observables. Computational and Systems Neuroscience (Cosyne) 2021. Poster I-116. 24 February 2021. Virtual.

H. Tanaka, A. Nayebi, N. Maheswaranathan, L.T. McIntosh, S.A. Baccus, S. Ganguli. From deep learning to mechanistic understanding in neuroscience: revealing computational mechanisms of retinal prediction via model reduction. Computational and Systems Neuroscience (Cosyne) 2020. Poster III-62. 29 February 2020. Denver, CO.

A. Nayebi*, J.B. Melander*, B. Jongbloets, T. Mao**, H. Zhong**, S. Ganguli**. Measuring and modeling the weight dynamics of many synapses onto diverse cell-types in vivo. Computational and Systems Neuroscience (Cosyne) 2019. Talk T-36. 3 March 2019. Lisbon, Portugal.

A. Nayebi*, J. Kubilius*, D.M. Bear, S. Ganguli, J.J. DiCarlo, D.L.K. Yamins. Convolutional recurrent neural network models of dynamics in higher visual cortex. Computational and Systems Neuroscience (Cosyne) 2018. Poster III-83. 3 March 2018. Denver, CO.

L.T. McIntosh*, N. Maheswaranathan*, A. Nayebi, S. Ganguli, S.A. Baccus. Deep convolutional neural network models of the retinal response to natural scenes. Computational and Systems Neuroscience (Cosyne) 2016. Poster III-26. 27 February 2016. Salt Lake City, UT.