Functional Neural Activity Mapping Using Spiking Neural Networks and EEG Signals: a Proof of Concept Study

The characterization of the intrinsic neural activity underlying neurophysiological recordings, which can be gathered through noninvasive techniques, is a major goal of computational neuroscience research. While different methods have been proposed to solve the inverse problem from a pure electromagnetic standpoint, literature regarding functional neural reconstruction modeling is limited. This study introduces a novel framework to define and quantify brain activity from a functional perspective, combining spiking neural networks with Electroencephalography (EEG) signal analysis. Single neuron dynamics is described via the Izhikevich model, and each channel activity is modeled as the outcome of a distinct population of cortical inhibitory and excitatory neurons. Functional interactions among distinct populations are also modeled. We validate this framework by testing it with a dataset of real recordings from 30 healthy subjects undergoing a cold pressure test. Our findings reveal a global enhancement of neural spiking activity during the elicitation session, especially in the beta and gamma bands. Results suggest that the proposed model is capable of describing the underlying functional neural activity of brain dynamics and showing significant variability between a resting state session and the cold pressure session. The proposed method paves the way for a functional alternative of brain source localization problems.