Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits

• DOI: 10.48550/arxiv.2403.19724
• Publication Date: 2024-03-28
• Authors: S. J. Ben Yoo; Luis El-Srouji; Suman Datta; Shimeng Yu; Jean Anne C. Inco...; Alberto Salleo; Volker J. Sorger; Juejun Hu; Lionel C. Kimerling; Kristofer E. Bouc...; Joy Geng; Rishidev Chaudhuri; Charan Ranganath; Randall C. O’Reilly
• Keywords: Neuromorphic engineering; Reverse engineering

The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system been able to match. For decades, reverse engineering the one of top priorities science technology research. Despite numerous efforts, conventional electronics-based methods have failed match scalability, efficiency, self-supervised brain. On other hand, very recent progress in development new generations photonic electronic memristive materials, device technologies, 3D electronic-photonic integrated circuits (3D EPIC ) promise realize brain-derived neuromorphic systems with comparable connectivity, density, energy-efficiency, scalability. When combined bio-realistic algorithms architectures, it may be possible an 'artificial brain' prototype general self-learning capabilities. This paper argues possibility reverse-engineering through architecting a computing consisting electronic, ionic, devices, dynamicity resembling bio-plausible molecular, neuro/synaptic, neuro-circuit, multi-structural hierarchical macro-circuits based on well-tested computational models. We further argue importance local applicable capture flexible adaptive unsupervised mechanisms central intelligence. Most importantly, we emphasize unique will enable us understand links between specific neuronal network-level properties system-level functioning behavior.