Unifying neural population dynamics, manifold geometry, and circuit structure

Adam Manoogian, Asit Pal, Zachary Friedenberger, Tatiana A Engel
Proceedings of the Analytical Connectionism Schools 2023--2024, PMLR 320:113-125, 2026.

Abstract

A central aim of neuroscience is to understand how the dynamics of neural circuits give rise to cognitive functions such as perception, attention, and decision-making. Cortical neural circuits are hierarchical and recurrent, resulting in rich temporal dynamics of individual neurons and distributed selectivity across the population. Classical neural circuit models capable of characterizing cognitive processes struggle to account for this complexity of cortical responses. Recent approaches leveraging heterogeneous neural networks address this complexity by characterizing activity in terms of interactions among latent states. In these lecture notes, we highlight recent work aimed at increasing the interpretability of these models and relating them to classical circuit models. These new analytical approaches connect neural population dynamics, geometry of latent manifolds, and the underlying circuit structure to enable mechanistic insights into cognitive processes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v320-manoogian26a, title = {Unifying neural population dynamics, manifold geometry, and circuit structure}, author = {Manoogian, Adam and Pal, Asit and Friedenberger, Zachary and Engel, Tatiana A}, booktitle = {Proceedings of the Analytical Connectionism Schools 2023--2024}, pages = {113--125}, year = {2026}, editor = {Sarao Mannelli, Stefano and Mignacco, Francesca and Chou, Chi-Ning and Chung, SueYeon and Saxe, Andrew}, volume = {320}, series = {Proceedings of Machine Learning Research}, month = {01 Jan--31 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v320/main/assets/manoogian26a/manoogian26a.pdf}, url = {https://proceedings.mlr.press/v320/manoogian26a.html}, abstract = {A central aim of neuroscience is to understand how the dynamics of neural circuits give rise to cognitive functions such as perception, attention, and decision-making. Cortical neural circuits are hierarchical and recurrent, resulting in rich temporal dynamics of individual neurons and distributed selectivity across the population. Classical neural circuit models capable of characterizing cognitive processes struggle to account for this complexity of cortical responses. Recent approaches leveraging heterogeneous neural networks address this complexity by characterizing activity in terms of interactions among latent states. In these lecture notes, we highlight recent work aimed at increasing the interpretability of these models and relating them to classical circuit models. These new analytical approaches connect neural population dynamics, geometry of latent manifolds, and the underlying circuit structure to enable mechanistic insights into cognitive processes.} }
Endnote
%0 Conference Paper %T Unifying neural population dynamics, manifold geometry, and circuit structure %A Adam Manoogian %A Asit Pal %A Zachary Friedenberger %A Tatiana A Engel %B Proceedings of the Analytical Connectionism Schools 2023--2024 %C Proceedings of Machine Learning Research %D 2026 %E Stefano Sarao Mannelli %E Francesca Mignacco %E Chi-Ning Chou %E SueYeon Chung %E Andrew Saxe %F pmlr-v320-manoogian26a %I PMLR %P 113--125 %U https://proceedings.mlr.press/v320/manoogian26a.html %V 320 %X A central aim of neuroscience is to understand how the dynamics of neural circuits give rise to cognitive functions such as perception, attention, and decision-making. Cortical neural circuits are hierarchical and recurrent, resulting in rich temporal dynamics of individual neurons and distributed selectivity across the population. Classical neural circuit models capable of characterizing cognitive processes struggle to account for this complexity of cortical responses. Recent approaches leveraging heterogeneous neural networks address this complexity by characterizing activity in terms of interactions among latent states. In these lecture notes, we highlight recent work aimed at increasing the interpretability of these models and relating them to classical circuit models. These new analytical approaches connect neural population dynamics, geometry of latent manifolds, and the underlying circuit structure to enable mechanistic insights into cognitive processes.
APA
Manoogian, A., Pal, A., Friedenberger, Z. & Engel, T.A.. (2026). Unifying neural population dynamics, manifold geometry, and circuit structure. Proceedings of the Analytical Connectionism Schools 2023--2024, in Proceedings of Machine Learning Research 320:113-125 Available from https://proceedings.mlr.press/v320/manoogian26a.html.

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