The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and store large amounts of raw recording data, followed by extensive processing offline, posing significant challenges to the hardware and preventing real-time analysis and feedback. Here, we demonstrate a memristor-based reservoir computing (RC) system that can potentially analyze neural signals in real-time. We show that the perovskite halide-based memristor can be directly driven by emulated neural spikes, where the memristor state reflects temporal features in the neural spike train. The RC system is successfully used to recognize neural firing patterns, monitor the transition of the firing patterns, and identify neural synchronization states among different neurons. Advanced neuroelectronic systems with such memristor networks can enable efficient neural signal analysis with high spatiotemporal precision, and possibly closed-loop feedback control.

Abstract-A user wants to retrieve a file from a database without revealing the identity of the file retrieved at the database, which is known as the problem of private information retrieval (PIR). If it is further required that the user obtains no information about the database other than the desired file, the concept of symmetric private information retrieval (SPIR) is introduced to guarantee privacy for both parties. In this paper, the problem of SPIR is studied for a database stored among N nodes in a distributed way, by using an (N, M )-MDS storage code. The information-theoretic capacity of SPIR, defined as the maximum number of symbols of the desired file retrieved per downloaded symbol, for the coded database is derived. It is shown that the SPIR capacity for coded database is 1 − M N , when the amount of the shared common randomness of distributed nodes (unavailable at the user) is at least M N−M times the file size. Otherwise, the SPIR capacity for the coded database equals zero.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

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The problem of symmetric private information retrieval (SPIR) from replicated databases with colluding servers and adversaries is studied. Specifically, the database comprises K files, which are replicatively stored among N servers. A user wants to retrieve one file from the database by communicating with the N servers, without revealing the identity of the desired file to any server. Furthermore, the user shall learn nothing about the other K − 1 files in the database. Any T out of N servers may collude, that is, they may communicate their interactions with the user to guess the identity of the requested file. An adversary in the system can tap in on or even try to corrupt the communication. Three types of adversaries are considered: a Byzantine adversary who can overwrite the transmission of any B servers to the user; a passive eavesdropper who can tap in on the incoming and outgoing transmissions of any E servers; and a combination of both -an adversary who can tap in on a set of any E nodes, and overwrite the transmission of a set of any B nodes. The problems of SPIR with colluding servers and the three types of adversaries are named T-BSPIR, T-ESPIR and T-BESPIR respectively. The capacity of the problem is defined as the maximum number of information bits of the desired file retrieved per downloaded bit. We show that the informationtheoretical capacity of the T-BSPIR problem equals 1− 2B+T N , if the servers share common randomness (unavailable at the user) with amount at least 2B+T N−2B−T times the file size. Otherwise, the capacity equals zero. The information-theoretical capacity of the T-ESPIR problem is proved to equal 1 − max(T,E) N , if the servers share common randomness with amount at least max(T,E) N−max(T,E) times the file size. Finally, for the problem of T-BESPIR, the capacity is proved to be 1 − 2B+max(T,E) N , where the common randomness shared by the servers should be at least 2B+max(T,E) N−2B−max(T,E) times the file size. The results resemble those of secure network coding problems with adversaries and eavesdroppers.

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