STP), which modulates the synaptic transmission efficacy dynamically at the conveying transients but leaves the efficacy unchanged during steady-state transmission; and long-term plasticity (LTP), which, in contrast, renders stable changes in the synaptic transmission efficacy. The STP and LTP have different computational uses: STP has profound effects on motor control, speech recognition, and working memory, while LTP is essential to encoding of spatial information. [2] In many if not all cases, a single plasticity is not sufficient to account for the intricate developmental and learning mechanisms, and concomitance of the STP and LTP is considered to support the maximally adaptive behavior and sophisticated cognitive functions. [1,2] Inspired by the neural mechanisms, the semiconductor research community has envisioned new computational capabilities based on neuromorphic computing with the aim of charting a new path beyond the decades-old approach to computing based on the Von Neumann architecture as implemented with transistor-based processors. [3,4] Since the seminal discovery of the memristive behavior, which had been predicted for use in Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short-term (STP) and long-term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three-terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi 2 O 2 Se is used for memristors for the first time, opening up the prospects for ultrathin, high-speed, and low-power neuromorphic devices. The concerted action of STP and LTP allows full-range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate "sleep-wake cycle autoregulation" process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.

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Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use an end-to-end unsupervised neural network to extract comprehensive and discriminative features directly from social behavior video frames for annotation and analysis purposes. Here, we report a self-supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end-to-end way. Visualization and classification of the extracted features (Meta-representations) validate that Selfee processes animal behaviors in a way similar to human perception. We demonstrate that Meta-representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in-depth analysis. Furthermore, time-series analyses of Meta-representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self-supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.