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Found 4 papers, 0 papers with code
The explanatory power of activity flow models of brain function
Tremendous neuroscientific progress has recently been made by mapping brain connectivity, complementing extensive knowledge of task-evoked brain activation patterns.
Compositional generalization through abstract representations in human and artificial neural networks
Our findings give empirical support to the role of compositional generalization in human behavior, implicate abstract representations as its neural implementation, and illustrate that these representations can be embedded into ANNs by designing simple and efficient pretraining procedures.
Efficient state and parameter estimation for high-dimensional nonlinear system identification with application to MEG brain network modeling
Intuitively, our approach consists of solving for the parameters that generate the most accurate state estimator (Extended Kalman Filter).
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis.
