EFRI BRAID: Emulating Cerebellar Temporally Coherent Signaling for Ultraefficient Emergent Prediction

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Although artificial intelligence (AI) has been applied to many computational problems, biological intelligence remains superior to AI for most cognitive tasks. For example, the brain is constantly receiving and ignoring massive volumes of sensory signals yet remains perpetually vigilant to anomalies in order to respond rapidly to unanticipated inputs. In contrast, modern AI performs poorly on similar tasks, requiring extensive training and propagation through multi-layer artificial neural networks. Consequently, robust anomaly detection in AI is slow and energy inefficient, posing challenges for high-value applications such as cybersecurity. Neuroscience research has shown that the cerebellum allows anomaly detection to emerge through contextual prediction, pattern separation, and response actuation. In an effort to emulate cerebellar functions, this project develops electronic devices that switch between asynchronous and synchronous behavior when triggered by sensory inputs. These devices are derived from nanoelectronics materials that realize temporally coherent signaling for diverse applications including cybersecurity, autonomous robotics, and power-delivery control. In addition, this project comprehensively analyzes the ethical, legal, and societal implications of the proposed research in collaboration with multiple stakeholders including college students, educators, and community workers. To ensure that these transformative outcomes are communicated to the most diverse audiences, multiple education and outreach initiatives aim to broaden participation among underrepresented and marginalized sections of society.Neuromorphic hardware chips are emerging as disruptive technologies to process and categorize vast amounts of digital data. The majority of the current implementations are based on well-studied feed-forward and recurrent neuronal architectures of the mammalian cerebrum and are thus optimized to perform only certain types of classification tasks. In contrast, theoretical neuroscience concepts derived from the cerebellum are underrepresented in artificial intelligence hardware even though the cerebellum has evolved to efficiently solve a wide range of problems such as anomaly detection in complex and noisy environments. Cerebellar accuracy and robustness are achieved by a unique neuronal coding architecture based on high firing rates, temporally coherent signaling, and complex spiking. To achieve similar functionality, this project develops electronic hardware that emulates the essential features of cerebellar neuronal coding. The resulting bio-realistic implementations are tested against the use cases of anomaly detection in cybersecurity, autonomous robotics, and power-delivery control. Specifically, this cross-disciplinary project combines ideas from theoretical neuroscience, materials science, and computer engineering to develop hardware prototypes based on two-dimensional semiconductors and van der Waals heterojunctions including synaptic devices based on memtransistors and spiking neurons based on Gaussian heterojunction transistors. In addition, this project comprehensively analyzes the ethical, legal, and societal implications of the proposed research in collaboration with multiple stakeholders including college students, educators, and community workers. To ensure that these transformative outcomes are communicated to the most diverse audiences, multiple education and outreach initiatives aim to broaden participation among underrepresented and marginalized sections of society.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.