Estimating the Excitatory-Inhibitory Balance from Electrocorticography Data using Physics-Informed Neural Networks

Understanding the excitatory/inhibitory (E/I) balance in the brain is crucial for elucidating the neural mechanisms underlying various cognitive functions and states of consciousness. Mathematical models have provided significant insights into these mechanisms, but they often face challenges due to high dimensionality, noisy observation signals, and nonlinearities. In this paper, we introduce a novel methodology using Physics-Informed Neural Networks (PINNs) to estimate the E/I balance from electrocorticography (ECoG) data, effectively addressing these limitations. By integrating physical laws via a neural mass model with neural network training, our approach enhances parameter estimation accuracy and robustness. Our analysis reveals a significant reduction in long-range connections (LRCs) and excitatory short-range connections (SRCs) under anesthesia, alongside an increase in inhibitory SRCs, highlighting anesthesia's role in modulating neural dynamics to induce unconsciousness. These findings not only corroborate existing theories on the neural mechanisms of anesthesia but also provide new insights into brain connectivity and its relationship with consciousness.