Brain Organoid Computing

Pioneering research on brain-organoid computing under Prof. Vwani P. Roychowdhury, exploring the computational capabilities of biological neural networks for tasks where traditional deep-learning models struggle.

What we’re doing

• Developing protocols for interfacing brain organoids with microelectrode arrays to harness their inherent computational properties.
• Applying organoid-based reservoir-computing approaches to forecast chaotic time series, with promising preliminary results compared to traditional deep-learning methods.
• Optimizing stimulation parameters and recording techniques for richer dynamics, in collaboration with an interdisciplinary team across electrophysiology and ML.

The long-term goal is a hybrid biological–digital substrate that exploits the rich nonlinear dynamics of organoid networks for low-power, sample-efficient prediction.