Alessandro Crimi, Joan Falco Roget / a.crimi@sanoscience.org
github alecrimi
Abstract:
The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. Dynamical causal model and Granger causality have been used in this context to define effective connectivity. Despite the success, those tools have received criticisms as being just predictors of temporal correlation (and not really perturbation based). More recently, new models are emerging from chaos theory and attractors representations. Among those causal representations convergent cross mapping (CCM) is the one receiving a lot of interest in biology and zoology. However, CCM is so far limited to couples of signals/behaviors. In this project we want to investigate this approach for multivariate relationships using recurrent neural networks.
List of materials:
[1] Structurally constrained effective brain connectivity, Crimi et al. Neuroimage 2021 https://www.sciencedirect.com/science/article/pii/S10538119210 05644
[2] Detecting Causality in Complex Ecosystems Sugihara et al. Science 2012. https://cdanfort.w3.uvm.edu/csc-reading- group/sugihara-causality-science-2012.pdf
[3] Systematic identification of causal relations in high- dimensional chaotic systems: application to stratosphere- troposphere coupling. Huang et al. Climate Dynamics. 2020 Nov;55(9):2469-81.
List of requirements for taking part in the project:
Participants should be knowledgeable on Python programming. Signal theory, dynamical system is an asset Neuro anatomy and physiology is welcome.
Maximal allowed number of team members: 8
Added as an issue for book keeping
Source: https://www.brainhack-krakow.org/projects
Team Leaders:
Alessandro Crimi, Joan Falco Roget / a.crimi@sanoscience.org github alecrimi
Abstract:
The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. Dynamical causal model and Granger causality have been used in this context to define effective connectivity. Despite the success, those tools have received criticisms as being just predictors of temporal correlation (and not really perturbation based). More recently, new models are emerging from chaos theory and attractors representations. Among those causal representations convergent cross mapping (CCM) is the one receiving a lot of interest in biology and zoology. However, CCM is so far limited to couples of signals/behaviors. In this project we want to investigate this approach for multivariate relationships using recurrent neural networks. List of materials:
[1] Structurally constrained effective brain connectivity, Crimi et al. Neuroimage 2021 https://www.sciencedirect.com/science/article/pii/S10538119210 05644 [2] Detecting Causality in Complex Ecosystems Sugihara et al. Science 2012. https://cdanfort.w3.uvm.edu/csc-reading- group/sugihara-causality-science-2012.pdf [3] Systematic identification of causal relations in high- dimensional chaotic systems: application to stratosphere- troposphere coupling. Huang et al. Climate Dynamics. 2020 Nov;55(9):2469-81. List of requirements for taking part in the project:
Participants should be knowledgeable on Python programming. Signal theory, dynamical system is an asset Neuro anatomy and physiology is welcome. Maximal allowed number of team members: 8