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Found 10 papers, 6 papers with code
Large language models surpass human experts in predicting neuroscience results
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance
Quantifying which neurons are important with respect to the classification decision of a trained neural network is essential for understanding their inner workings.
Cognitive modelling with multilayer networks: Insights, advancements and future challenges
Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing.
Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys
We apply Dynamic Causal Models to electrocorticogram recordings from two macaque monkeys performing a problem-solving task that engages working memory, and induces time-on-task effects.
Brain Modelling as a Service: The Virtual Brain on EBRAINS
The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science.
Bayesian Inference
Code Generation
Computational Engineering, Finance, and Science
Cryptography and Security
Distributed, Parallel, and Cluster Computing
Neurons and Cognition
Quantitative Methods
Quantifying dynamical high-order interdependencies from the O-information: an application to neural spiking dynamics
We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to.
Synergy as a warning sign of transitions: the case of the two-dimensional Ising model
We consider the formalism of information decomposition of target effects from multi-source interactions, i. e. the problem of defining unique, redundant (or shared), and synergistic (or complementary) components of the information that a set of source variables provides about a target, and apply it to the two-dimensional Ising model as a paradigm of a critically transitioning system.
Statistical Mechanics
Multiscale Granger causality analysis by à trous wavelet transform
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach.
Kernel Granger causality and the analysis of dynamical networks
We apply the proposed approach to a network of chaotic maps and to a simulated genetic regulatory network: it is shown that the underlying topology of the network can be reconstructed from time series of node's dynamics, provided that a sufficient number of samples is available.
Disordered Systems and Neural Networks Exactly Solvable and Integrable Systems Quantitative Methods
Kernel method for nonlinear Granger causality
Important information on the structure of complex systems, consisting of more than one component, can be obtained by measuring to which extent the individual components exchange information among each other.
Disordered Systems and Neural Networks Exactly Solvable and Integrable Systems
