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Scientific Fields
Diseases
Organisms
Applications
Technique
Starting Date
11
Oct 2018
Ending Date
12
Oct 2018
Time
09:00:00
25 Rue du Docteur Roux, Paris, France
Address
Room: Auditorium Monod
Location
2018-10-11 09:00:00 2018-10-12 18:00:00 Europe/Paris Neural networks – From brains to machines and vice versa Continuous advances in machine learning have enabled groundbreaking progress in diverse fields such as computer vision or strategy games by making use of artificial neural networks. In parallel, novel technologies for recording and manipulating […] 25 Rue du Docteur Roux, Paris, France David DiGregorio david.digregorio@pasteur.fr

Speakers

Larry Abbott
(Columbia University, USA)
Caswell Barry
(University College London)
Eugenia Chiappe
(Champalimaud Research, Portugal)
Kate Jeffery
(University College London)
Jakob Macke
(TU Munich and research center caesar, Germany)
Rémi Monasson
(Ecole Normale Supérieure, France)
Carey Priebe
(Johns Hopkins, USA)
Naftali Tishby
(Hebrew University, Israël)
Srini Turaga
(Janelia Research Campus, USA)
Christian Vestergaard
(Institut Pasteur, France)
Marco Zorzi
(University of Padova, Italy)
Tihana Jovanic
(Janelia Research Campus )
Elad Schneidman
(Weizmann Institute)

About

Continuous advances in machine learning have enabled groundbreaking progress in diverse fields such as computer vision or strategy games by making use of artificial neural networks. In parallel, novel technologies for recording and manipulating biological neural networks allow us to probe the behavioural function of neuronal activity in unprecedented detail. By bringing together leading experts in the fields of biology, applied mathematics and physics, this symposium aims to bridge our current understanding of how biological and artificial neural networks operate.

Program

Download (PDF, 1.66MB)

Registration

Registration is closed. There is no on-site registration.
You will need to have registered via EventBrite and replied to our follow-up email to attend the meeting.
You will not be able to access the Pasteur Campus without registration.

Sponsors

 

Schedule

Thursday 11th October

Physics of biological neural networks (Chair: JB Masson)

09h00
David DiGregorio, Institut Pasteur
Introductory remarks

09h15
Rémi Monasson, Ecole Normale Supérieure
Integration and multiplexing of positional and contextual information by the hippocampal network

10h00
Jakob Macke, TU Munich and research center ceasar
Bridging the gap between mechanistic and statistical models of neural dynamics

10h45 Coffee break

11h15
Christian Vestergaard, Institut Pasteur
Discovery of computational motifs in experimental neural connectomes using nested randomized reference models

12h00
Elad Schneidmann, Weizmann Institute
Learning the code of large neural populations by random projections

12h45 Lunch break

Keynote Lecture (Chair: David DiGregorio)

14h00
Larry Abbott, Columbia University
Lessons for Machine Learning from a Plastic Recurrent Network in Electric Fish

15h00 Coffee break

Machine learning in neuroscience (Chair: Christophe Zimmer)

15h45
Caswell Barry, University College London
Probing the spatial memory system with deep networks

16h30
Srini Turaga, Janelia Research Campus
From biological neural networks to artificial neural networks, with deep learning

17h15
Marco Zorzi, University of Padova
Deep neural networks for modeling perception and cognition

18h00 Poster session & Apéritif

12th October

Measuring neural networks in real life (Chair: Christoph Schmidt-Hieber)

09h00
Eugenia Chiappe, Champalimaud Research
Visuomotor control of walking during exploration in flies

09h45
Tihana Jovanic, Janelia Research Campus
Mapping neural circuits underlying sensorimotor decisions and sequences in Drosophila

10h30 Coffee break

11h15
Kate Jeffery, University College London
How landmarks inform the sense of direction

12h00 Lunch break

Physics of artificial neural networks (Chair: Jean-Baptiste Masson)

13h30
Carey Priebe, Johns Hopkins University
Structure discovery and exploitation in networks

14h15
Naftali Tishby, Hebrew University
The Information Bottleneck theory of Deep Neural Networks: what do the layers represent?

Location

Room: Auditorium Monod
Address: 25 Rue du Docteur Roux, Paris, France