field of biological machine learning, promising attempts have been made to use cortical neurons for machine learning tasks. However, these efforts often lack a strong theoretical foundation from a mathematical perspective.
Prof Saad’s talk introduced three mathematical tools to address this gap:
- Neuronal Network Inference: Saad presented an algorithm that uses machine learning and statistical physics to infer the structure and connectivity of neuronal networks from spontaneous activities. This is crucial for understanding neuronal architecture and plasticity.
- Visual Informatics Approach: He showcased methods to study differences in neuronal activities under various conditions using advanced dimensionality reduction techniques, retaining the global structure of high-dimensional data better than traditional methods like PCA and t-SNE.
- Spatial Entropy Measurement: Saad emphasized using a Bayesian approach to measure the spatial entropy of neuronal activities accurately, revealing significant differences between spontaneous and stimulated activities. This provides a reliable metric for studying neuronal behaviour.
Overall, Prof Saad highlighted the potential of mathematical tools to enhance our understanding of neuronal networks and advance biological machine learning.