EFRI BEGIN OI:Neuron-Soft Organoid-Computer Interfaces for Long-Term Three-Dimensional Neural Network Computing

Harvard University, Cambridge MA

Investigators

Abstract

Current artificial intelligence (AI) and machine learning (ML) technologies do not match the adaptability and efficiency of biological systems. This project will integrate brain organoids with soft, bioelectronics to create a system capable of complex computational tasks. These systems will inspire novel AI algorithms, enhance data processing efficiency, and advance machine learning beyond current capabilities. Ethical considerations will guide all research activities to ensure that the project aligns with the highest standards and complies with current laws and regulations. This project will impact AI, bioelectronics, and bioengineering by enabling precise understanding of cellular activity and scalable bio-computation. The collaborative, multidisciplinary approach integrates expertise from bioengineering, electrical engineering, mechanical engineering, ML, statistics, and control theory. Educational efforts will include seminars, undergraduate research, K-12 outreach, and curriculum development emphasizing interdisciplinary learning and bioethics. Programs at Harvard and UT Austin will recruit and mentor students, focusing on underrepresented communities. K-12 outreach will involve science mentorships and online content sharing on bio-inspired computation and AI integration with biology. This project will develop seamless integration of neuron-soft brain organoid-computer interfaces for long-term 3D neural network computing through following thrusts: (A) developing neuron-soft bioelectronics with over one thousand electrodes for seamless 3D integration with brain organoids. This thrust will implement mechanics-driven soft bioelectronics design and 3D microfabrication to enable long-term, stable recording and stimulation of cellular activities. (B) modeling and facilitating the maturation and specialization of brain organoids with bio-inspired online learning and sensing-AI-actuation loop. This thrust will utilize embedded sensors and actuators, along with reinforcement learning algorithms, to create a closed-loop system that generates spatiotemporally evolved stimulation patterns as feedback to the recorded signals, which can direct the maturation and specialization of brain organoids with precise sub-regional and functional specificity. (C) providing a novel framework that integrates the computational power of ML and brain organoids. This thrust will design task-specific stimulation patterns, deliver stimulations through embedded actuators, and receive the organoids’ responses via embedded sensors, which will be further processed by the downstream ML algorithms for efficient and effective computation. (D) conducting in-depth ethics research, integrated with the science research and experiments, applying the method of Collaborative Ethics throughout the research process. This thrust will include conceptual as well as applied ethics research, ensuring adherence to the highest standards of responsible conduct of science in compliance with current laws and regulations pertaining to all stages of the research process. This project is jointly funded by the Emerging Frontiers in Research and Innovation Program (BEGIN OI) and the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.