CNS*2025 Florence

Session description
Cobrawap (Collaborative Brain Wave Analysis Pipeline) [1,2] is an open-source, modular and customizable data analysis tool developed in the context of HBP/EBRAINS, with the aim of enabling standardized quantitative descriptions of cortical wave dynamics observed in heterogeneous data sources, both experimental and simulated. The tool intercepts the increasing demand expressed by the Neuroscience community for reusability and reproducibility, offering a software framework suitable for collecting generalized implementations of established methods and algorithms, and for embracing innovative procedures.
Inspired by FAIR principles and leveraging the latest findings in software engineering, Cobrawap is structured as a collection of modular Python3 building blocks that can be flexibly arranged along sequential stages, implementing data processing steps and analysis methods, directed by workflow managers (Snakemake or CWL). This “Getting started” tutorial [3] provides an introductory exercise to Cobrawap users, i.e. people interested in applying solutions already implemented in the software, for the exemplary scientific use-cases of i) imaging and ii) ECoG recordings from mouse cortex under anesthesia, and iii) spiking simulations from human brain. The value of this exercise goes beyond obtaining results from this specific use-case, since it allows also for “learning by doing” how the Cobrawap approach has been set up, and thus how it can be exploited in other scientific use-cases. Therefore, hints and clues will be given to people interested in developing new functions, i.e. expanding the pipeline to intercept their investigation goals and fit to their data types.

References
[1] https://github.com/NeuralEnsemble/cobrawap, https://cobrawap.readthedocs.io
[2] Gutzen, et al. (2024) https://doi.org/10.1016/j.crmeth.2023.100681
[3] https://github.com/APE-group/hands_on_cobrawap

Acknowledgments
Research co-funded by: European Union’s Horizon Europe Programme under Specific Grant Agreement No. 101147319 (EBRAINS 2.0); European Commission NextGeneration EU (PNRR EBRAINS-Italy MUR-CUP-B51E22000150006). This work is presented on behalf of the Cobrawap core team: C. Lupo, R. Gutzen, M. Denker, P. S. Paolucci, G. De Bonis. Scientific applications, that will be the subject of targeted publications in preparation, will be co-authored by additional collaborators and partners, here acknowledged: G. Gaglioti, T. Nieus, A. Pigorini, S. Sarasso, M. Massimini, C. De Luca, E. Montagni, F. Resta, A. L. Allegra Mascaro, C. Lupascu, M. Migliore.

Requirements for attendees
A personal laptop for installing the software and storing test datasets and results. It is strongly suggested to have a UNIX operative system (e.g. a Linux distribution, or Mac OS) with a recent Python distribution (e.g. >= 3.10); if usually working on Windows OS, please consider the installation of a virtual machine. Check this webpage for all the details about the requirements and some previous Cobrawap tutorials.

Recent advances in machine learning methods make it possible to train recurrent neural networks (RNNs) to perform highly complex and sophisticated tasks. One of the tasks, particularly interesting to neuroscientists, is to generate experimentally recorded neural activities in recurrent neural networks and study the dynamics of trained networks to investigate the underlying neural mechanism.

Here we showcase how a widely-used training method, known as recursive least squares (or FORCE), can be adopted to train spiking RNNs to reproduce spike recordings of cortical neurons. We first give an overview of the original FORCE learning, which trains the outputs of rate-based RNNs to perform tasks, and show how it can be modified to generate arbitrarily complex activity patterns in spiking RNNs. Using this method, we show only a subset of neurons embedded in a network of randomly connected excitatory and inhibitory spiking neurons can be trained to reproduce cortical neural activities.

References:

• Sussillo, D., & Abbott, L. F. (2009). Generating coherent patterns of activity from chaotic neural networks. Neuron, 63(4), 544-557.
• Kim, C. M., & Chow, C. C. (2018). Learning recurrent dynamics in spiking networks. Elife, 7, e37124.
• Kim, C. M., & Chow, C. C. (2021). Training spiking neural networks in the strong coupling regime. Neural computation, 33(5), 1199-1233.
• Kim, C. M., Finkelstein, A., Chow, C. C., Svoboda, K., & Darshan, R. (2023). Distributing task-related neural activity across a cortical network through task-independent connections. Nature Communications, 14(1), 2851.

The full program including abstracts can be found here

https://sites.google.com/view/cns2025workshop-strudynfun/

We are looking forward to seeing you at the workshop.

Wilhelm Braun, Kayson Fakhar and Claus C. Hilgetag