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Yann Lecun

Facebook & NYU

October, 19, 2022

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Julia Steinberg

Princeton University

October, 26, 2022

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Watch the talk on YouTube

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Alexis Dubreuil

CNRS, Bordeaux

November, 2, 2022

The role of population structure in computations through

neural dynamics

Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally com- plementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple subpopulations. Our analyses revealed that such a subpopulation structure enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, for inactivation experiments and for the implication of different neurons in multi-tasking.

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Yonatan Aljadeff

UCSD

November, 9, 2022

Shallow networks run deep:

How peripheral preprocessing facilitates odor classification

Drosophila olfactory sensory hairs ("sensilla") typically house two olfactory receptor neurons (ORNs) which can laterally inhibit each other via electrical ("ephaptic") coupling. ORN pairing is highly stereotyped and genetically determined. Thus, olfactory signals arriving in the Antennal Lobe (AL) have been pre-processed by a fixed and shallow network at the periphery. To uncover the functional significance of this organization, we developed a nonlinear phenomenological model of asymmetrically coupled ORNs responding to odor mixture stimuli. We derived an analytical solution to the ORNs’ dynamics, which shows that the peripheral network can extract the valence of specific odor mixtures via transient amplification. Our model predicts that for efficient read-out of the amplified valence signal there must exist specific patterns of downstream connectivity that reflect the organization at the periphery. Analysis of AL→Lateral Horn (LH) fly connectomic data reveals evidence directly supporting this prediction. We further studied the effect of ephaptic coupling on olfactory processing in the AL→Mushroom Body (MB) pathway. We show that stereotyped ephaptic interactions between ORNs lead to a clustered odor representation of glomerular responses. Such clustering in the AL is an essential assumption of theoretical studies on odor recognition in the MB. Together our work shows that preprocessing of olfactory stimuli by a fixed and shallow network increases sensitivity to specific odor mixtures, and aids in the learning of novel olfactory stimuli.
Work led by Palka Puri, in collaboration with Chih-Ying Su and Shiuan-Tze Wu

November, 16, 2022

Society for Neuroscience Meeting

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Barbara Webb

University of Edinburgh

November, 23, 2022

TBA

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Thibaud Taillefumier

The University of Texas

at Austin

November, 30, 2022

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Grace Lindsey

NYU

December, 7, 2022

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Vladimir Itskov

The Pennsylvania State

University

December, 14, 2022

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DECEMBER, 21, 2022

Christmas Break

DECEMBER, 28, 2022

Happy New Year

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Haim Sompolinsky

The Hebrew University

and Harvard

January, 4, 2023

TBA

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Laureline Laugiaco

MIT

January, 11, 2023

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January, 18, 2023

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Alessandro Sanzeni

Universita Bocconi

Milano

January, 25, 2023

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Soledad Gonzalo Cogno

NTNU,Trondheim

February, 1, 2023

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February, 8, 2023

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German Mato

CONICET, Bariloche

February, 15, 2023

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February, 22, 2023

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Richard Naud

University of Ottawa

March, 1st , 2023

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March, 8 , 2023

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March, 15 , 2023

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March, 22 , 2023

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March, 29 , 2023

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