The dynamics of cortical neuronal networks and their relationship with neuronal activity are essential for understanding brain functionality. A novel approach using Bayesian statistics, statistical physics, and advanced machine learning is now proposed to infer the effective network structure from neuronal firing data.

• Bayesian Inference: An innovative probabilistic method to deduce network structure and composition.
• Prediction of Activity: The algorithm predicts neuronal spiking activity with great accuracy using the inferred network infrastructure.
• Advance Over Existing Methods: The method shows enhanced performance when compared to currently used techniques in networking inference.

By providing a view into the learning process and neuronal network evolution, this research can lead to the development of neuron-based computational systems. Learn about this statistical physics approach: Inferring Structure of Cortical Neuronal Networks from Firing Data: A Statistical Physics Approach.