Sammendrag
The brain has long been a source of inspiration for the development of novel computational methods and architectures. Most notably, modern machine learning (ML) and deep learning (DL) algorithms in the field of artificial intelligence (AI) are based on an artificial neuron model, and such approaches have enabled us to process data on a previously unimaginable scale. However, current ML and DL approaches are computationally expensive and task-specific, suggesting we have much yet to learn from the efficiency and learning capabilities of the brain.
In this work, we evaluated the dynamics and functional connectivity of networks of cortical neurons as they matured over approximately two months in vitro. The electrophysiological activity of the networks was captured daily using microelectrode arrays (MEAs). To assess the dynamics, we identified patterns of activity termed ‘neuronal avalanches’ and used this activity to compute the branching ratio and complexity of the observed activity. The branching ratio describes how activity is propagated through the network and allows the activity to be classified into different dynamical regimes, while the complexity provides a measure that is maximized when activity indicates a balance between integration and segregation in the network. Graphs representing the functional connectivity of the network before were extracted from their electrophysiological behavior, and these graphs were used to evaluate the relationship between the avalanche dynamics and the connectivity of the network.
This work serves as a starting point for the data-driven development of biologically plausible models to inform AI algorithms. Future work will involve information theoretical analysis of the network behavior as well as investigations into the role of plasticity in determining the dynamical state. By evaluating the interplay between computation and connectivity, we hope to drive the development of novel powerful AI models.
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