Information dynamics of $in\; silico$ EEG Brain Waves: Insights into oscillations and functions
The relation between EEG rhythms, brain functions, and behavioral correlates is well-established. Some mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms $in\; silico$. This allows to explore relations between neural oscillations and specific neuronal circuits, helping to decipher the functional properties of brain waves. Integrated information Decomposition ($\Phi$-ID) framework relates dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of IF neurons with short-term synaptic plasticity producing a diverse range of EEG-like rhythms, from low $\delta$ waves to high-frequency oscillations. Through $\Phi$-ID, we analyze the network's information dynamics elucidating the system's suitability for robust information transfer, storage, and parallel operation. Our study identifies also regimes that may resemble pathological states due to poor informational properties and high randomness. We found that $in\; silico$ $\beta$ and $\delta$ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, and the coexistence of excitatory $\theta$, $\alpha$, and $\beta$ waves associated to information storage. Also, high-frequency oscillations can exhibit either high or poor informational properties, shedding light on discussions regarding physiological versus pathological high-frequency oscillations. Our study demonstrates that dynamical regimes with similar oscillations may exhibit different information dynamics. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGRhythmSimilar Papers 제목 키워드 기반
Plasma Brain Dynamics (PBD): A Mechanism for EEG Waves Under Human Consciousness
EEG signals are records of nonlinear solitary waves in human brains. The waves have several types (e.g., a, b, g, q, d) in response to different levels of consciousness. They are classified into two groups: Group-1 consi…
EEGElectroencephalogram (EEG)Quantum-Inspired Computing: Can it be a Microscopic Computing Model of the Brain?
Quantum computing and the workings of the brain have many aspects in common and have been attracting increasing attention in academia and industry. The computation in both is parallel and non-discrete. Though the underly…
Quantum Machine LearningSimulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse
Thanks to novel, powerful brain activity recording techniques, we can create data-driven models from thousands of recording channels and large portions of the cortex, which can improve our understanding of brain-states n…
AI-Driven Physics-Informed Bio-Silicon Intelligence System: Integrating Hybrid Systems, Biocomputing, Neural Networks, and Machine Learning, for Advanced Neurotechnology
We present the Bio-Silicon Intelligence System (BSIS), an innovative hybrid platform that integrates biological neural networks with silicon-based computing. The BSIS, a Physics-Informed Hybrid Hierarchical Reinforcement…
Hierarchical Reinforcement LearningLearning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyses
Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, its spatial pattern follows large scale brain networks. In this context, a powerful network analysis app…