Distributed computations across brain regions

Organizers

João Barbosa  | Ecole Normale Supérieure de Paris, France
Heike Stein| Ecole Normale Supérieure de Paris, France

Abstract

Current recording techniques make it possible to simultaneously record from thousands of neurons in multiple brain regions. Recent work exploiting these techniques shows a broadly distributed representation of task variables across the brain (e.g Steinmetz et al, 2019; Musall et al., 2019). Furthermore, representations within regions appear to be mixed, even at the single-cell level (Fusi et al., 2016). These observations challenge the classical assignment of different computations to specialised brain regions. Instead of a series of compartmentalised computations, neuroscience is developing a new understanding of integrated computations that emerge from brain-wide interactions of different regions within nested feedback loops (Cisek, 2019). Yet, our theoretical understanding of how neural dynamics enable behaviourally relevant computations is still largely limited to individual networks in isolation, but exciting new approaches taking multi-area interactions into account are emerging (Abbott & Svoboda, 2020, for a recent volume of reviews).

This workshop will bring together experts on these emerging experimental and statistical methods, as well as network modelling, that will allow neuroscientists in the coming decade to quantify and analyse interactions between brain regions. It will be divided in five thematic sections:

  • Feedforward interactions,
  • Recurrent interactions,
  • Multi-area interactions for flexible behavior,
  • Latent variable modeling,
  • Neural subspaces

With the help of the audience and the speakers, we would like to address the following questions: What are the computational advantages of engaging multiple areas? What can architecture/structure tell us about a region’s role in distributed computations? What is the basic unit of computation? When are feedforward interactions enough, and what can we learn by studying recurrent interactions? Is there hope for properly estimating causal multi-region interactions statistically (e.g. distinguishing feedforward from feedback)?

Schedule (CEST)

Tuesday, Sept 13

14:00

Introduction

Heike Stein | Ecole Normale Supérieure de Paris, France

Feedforward interactions

14:15

Samuel Muscinelli | Columbia University, USA
Optimal routing to cerebellum-like structures

14:45

Sophie Bagur | Institut Pasteur, France
Transformation of population representations of sounds throughout the auditory system

Recurrent interactions

15:15

Guillaume Bellec | Ecole Polytechnique Fédérale de Lausanne, Switzerland
Fitting recurrent spiking network models to study the interaction between cortical areas

15:45

Tatiana Engel | Cold Spring Harbor Laboratory, USA
A manifold of heterogeneous vigilance states across cortical areas

16:15

Q&A session

16:30

30 min break

Multi-area interactions for flexible behavior

17:00

Cristina Savin| New York University, USA
Making sense of neural responses during complex behavior

17:30

João Barbosa | Ecole Normale Supérieure de Paris, France
Flexible inter-areal computations through low-rank communication subspaces

18:00

Panel discussion

18:30

End of first day

Wednesday, Sept 14

Latent variable models

08:30

Mehrdad Jazayeri | Massachusetts Institute of Technology, USA
Hierarchical reasoning by neural circuits in the ACC and DMFC

09:00

Stephen Keeley | Fordham University, USA
Multi-region Poisson GPFA isolates shared and independent latent structure in sensorimotor tasks

Probing interactions through perturbations

09:30

Maryam Shanechi | University of Southern California, USA
Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation

10:00

Joana Soldado-Magraner | Carnegie Mellon University, USA
Inter-areal patterned microstimulation selectively drives PFC activity and behavior in a memory task

10:30

30 min break

Neural subspaces

11:00

Sue Ann Koay | Janelia Research Campus, USA
Sequential and efficient neural-population coding across posterior cortex

11:30

Ivan Voitov | University College London, UK
Cortical feedback loops bind distributed high-dimensional representations of working memory

12:00

Panel discussion

12:30

End