Distributed computations across brain regions


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


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



Heike Stein | Ecole Normale Supérieure de Paris, France

Feedforward interactions


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


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

Recurrent interactions


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


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


Q&A session


30 min break

Multi-area interactions for flexible behavior


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


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


Panel discussion


End of first day

Wednesday, Sept 14

Latent variable models


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


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

Probing interactions through perturbations


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


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


30 min break

Neural subspaces


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


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


Panel discussion