Speakers — GNT20

Keynote speakers

Mate Lengyel Keynote · Day 1

University of Cambridge

Navigation from first principles

Cristina Savin Keynote · Day 1

New York University

The fast and slow of value-based decision making

Ashok Litwin-Kumar Keynote · Day 2

Columbia University

Mechanisms for multi-tasking in recurrent neural networks

Jonathan Pillow Keynote · Day 2

Princeton University

Characterizing the dynamics of learning from animal training data

Session 1 — Network heterogeneity & dynamics

Chair: Matthew Chalk (Institut de la Vision)

Fleur Zeldenrust

Donders Institute

Heterogeneity in the brain: lessons from theory and experiment

Yuxiu Shao

Université Côte d'Azur

Heterogeneous synaptic motifs bridge microscale structure and macroscale nonlinear dynamics

Arthur Pellegrino

ENS Paris

RNNs perform task computations by dynamically warping neural representations

Session 2 — Cortical computations: perception, beliefs & decisions

Chair: Joao Barbosa (Institut de Neuromodulation)

Alex Hyafil

CRM Barcelona

Bistable perception reveals a canonical computation in sensory processing

Francesca Mastrogiuseppe

SISSA Trieste

Input-dependent directionality of interactions between cortical areas

Giulio Bondanelli

Université Claude Bernard Lyon 1

Single-neuron perturbations reveal a dynamical switch in decision computations in parietal cortex

Session 3 — Motor control & preparatory dynamics

Heike Stein

Sorbonne Université

Motor control modelling

Lorenzo Fontolan

INMED Marseille

Preparatory population dynamics of timing and flexible behavior

Mark Humphries

University of Nottingham

Primary and Supplementary Motor cortex implement parallel solutions for the control of rhythmic and discrete arm movements

Session 4 — Inference, learning & efficient computation

Chair: Michael Graupner (Université Paris Cité)

Renaud Jardri

CHU Lille

Computational Modeling of Rigid Beliefs: An illustration with Hallucinations, Delusions and Conspiracy

Viola Priesemann

University of Göttingen

Dendritic contribution to learning efficient codes

Pedro Goncalves

KU Leuven

Machine learning for mechanistic insight in neuroscience