Embedded Silicon-Organic Integrated Neuromorphic System

TL;DR

This paper presents an integrated neuromorphic system combining silicon and organic materials to emulate neural functions, aiming to enhance brain-inspired AI and neural interfaces.

Contribution

It introduces a novel silicon-organic hybrid neuromorphic system with organic artificial synapses and neurons, advancing hardware-based brain-inspired intelligence.

Findings

Successful fabrication of organic neural devices

Integration of organic synapses with silicon FPGA neurons

Potential for biocompatible neural interfaces

Abstract

The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain. Based on multi-disciplinary research in systems neuroscience, computer architecture, and functional organic materials, we proposed the concept of using AI to simulate the operating principles and materials of the brain in hardware to develop brain-inspired intelligence technology, and realized the preparation of neuromorphic computing devices and basic materials. We simulated neurons and neural networks in terms of material and morphology, using a variety of organic polymers as the base materials for neuroelectronic devices, for building neural interfaces as well as organic neural devices and silicon neural computational modules. We assemble organic artificial synapses with simulated neurons from silicon-based Field-Programmable Gate Array (FPGA) into organic artificial neurons, the basic components of neural networks, and later construct biological neural network models based on the interpreted neural circuits. Finally, we also discuss how to further build neuromorphic devices based on these organic artificial neurons, which have both a neural interface friendly to nervous tissue and interact with information from real biological neural networks.