Leveraging Stochastic Resonance for High Spatial-Resolution Extracellular Monitoring

San Diego State University Foundation, San Diego CA

Investigators

Abstract

The nervous system is a network of billions of neurons, where several types of signals propagate through. By monitoring the electrical signals propagating in the network, the connectivity of the nervous system and how information is represented in the nervous system can be explored, which can lead to significant advances in a wide spectrum of applications ranging from understanding the disruptions in the network of the nervous system in the case of neurological or psychiatric conditions to decoding intentions. Efforts in electronics design and data analytics over the past decades have led to energy-efficient, real-time, and accurate neural monitoring systems. However, these systems suffer from significantly smaller number of observable neurons per electrode (~ five to ten) compared to the theoretical number (>200) because of (i) missed small-amplitude electrical signals of neurons that are far from the electrode and (ii) signals of neurons with very low activity levels being masked by those of highly-active neurons. The proposed study aims to engineer an energy-efficient and low-complexity neural monitoring system addressing these issues to maximize the number of neurons that can be detected per electrode and therefore improve the scalability. This technology can transform the way extracellular monitoring is performed in neuroscience and neurotechnologies studies. The results can be useful for other weak signal detection applications with limited power budgets such as sensing and communication blocks of Internet of Things (IoT) and sensor-network applications. The proposed interdisciplinary project will be integrated with educational and outreach activities promoting diversity by engaging and training educationally disadvantaged students from K-12 to graduate levels in neural monitoring, weak signal detection, and brain-computer interfaces. This project tackles one of the holy grails of implantable brain-computer interface technologies: How can neural activity of the brain be accurately and energy-efficiently captured and classified at high resolution both spatially and temporally? To address the question, the project will explore and test the following hypothesis: By designing multiple spike detectors each tuned for maximizing the sensitivity to different spike amplitudes and activity-levels of neurons by leveraging stochastic resonance (SR), a phenomenon observed for weak signal detection at various systems including biological neurons, it should be possible to maximize spike detection sensitivity and spike sorting specificity of an extracellular monitoring system. To test the hypothesis, the project will be conducted through three interacting objectives: (1) Demonstrate SR-enhanced spike detection and sorting and design the extracellular monitoring framework. (2) Design energy-efficient electronics for physical implementation of individual blocks. (3) Perform system-level integration and validation. First, theoretical analysis on optimum noise intensity and detector specification will be verified using synthetic datasets of extracellular neural recordings created. Then, physical implementation of the framework will be performed. Lastly, a system-level integration of the physical system will follow benchmark comparisons against the state-of-the-art extracellular monitoring algorithms. 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.