Speaker
Description
The critical brain hypothesis has been confirmed experimentally many times since the pioneering electrode experiments. Power law (PL) distributed neuronal avalanches were shown in neuronal recordings, in blood-oxygen-level-dependent signals,in voltage imaging, in calcium-imaging, in MEG and EEG recordings and in neuronal long-range temporal correlation among others. Whole brain simulations, based on the largest connectomes have been performed by various methods [1].
Here I show the results of solving the Shinomoto-Kuramoto model on networks of the fruit-fly and two large human connectomes, using an efficient CUDA GPU solver, which provides numerical evidence of near critical scaling in modules of these brains [2]. In particular our Hurst and beta exponents agree with those of recent fMRI data [3].
This provides an important implication for AI systems by optimizing information processing performance.
Furthermore, I show how the asymmetry in the inter-neuronal connections drives the system away from equilibrium, by violating the fluctuation-dissipation relation [4].
[1] Géza Ódor, Michael T. Gastner, Jeffrey Kelling and Gustavo Deco
Modelling on the very large-scale connectome J. Phys. Complex. 2 (2021) 045002.
[2] Géza Ódor, István Papp, Shengfeng Deng and Jeffrey Kelling
Synchronization transitions on connectome graphs with external force.
Front. Phys. 11 (2023) 1150246.
[3] Ochab, et al. Task-dependent fractal patterns of information processing in working memory. Sci Rep 12, 17866 (2022).
[4] Géza Ódor, Istvan Papp and Gustavo Deco, Fluctuation-dissipation of the Kuramoto model on fruit-fly connectomes, arXiv:2503.20708