Collaborative Research: Integrated memristor neural networks for in-situ analysis of intracellular neuronal recordings

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The ability to detect and analyze individual neuron spiking patterns over large areas will have profound impacts on neuroscience and medicine, as it allows the functions of the brain to be directly mapped to underlying neuron activities and enables precise brain disease detection and drug developments. However, although recent advances in neuroprobes such as nanoelectrode arrays make it possible to pick up signals from single neurons, scaling the systems to thousands and possibly millions of neuron sites is not practical due the enormous resources and time required to digitize, store and analyze the vast amount of data. This proposal aims to precisely address these challenges by integrating an artificial neural network on the nanoelectrode array, such that cell signals picked up by the electrodes are directly processed by the artificial neural network, and only the processed, "useful" data need to be amplified and transmitted, allowing real-time analysis with very low power. Undergraduate and graduate students will be trained to obtain state-of-the-art nanotechnology and neuroengineering techniques. Knowledge and techniques developed during research will be incorporated into course materials and other types of publications to allow cross pollination for students among different disciplines and broad dissemination to the general public. The proposed new neural recording system will offer unparalleled spatial resolution and processing capabilities, where high density and highly sensitive nanoelectrode arrays are integrated with a memristor-based artificial neural network that allows real time signal processing. By optimizing and utilizing internal dynamic ionic processes in the memristors, the artificial network can be directly driven by the spike trains from biological neurons without amplification or other pre-processing, where responses from the memristor network can be used to analyze temporal patterns in the neuron spikes and to connect detected neuronal activity with functions of the biological networks. The tightly coupled memristor network with the biological network can further allow functions in the biological system to be directly mapped on the electrical system, and potentially lead to future neural prosthesis and augmentation applications. New materials, devices, and networks will be developed, along with new recording and computing strategies that broaden the impact of the proposed project.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.