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Artificial Synaptic Devices Based on Two-Dimensional Semiconductors
Published in Mohammad Karbalaei Akbari, Serge Zhuiykov, Ultrathin Two-Dimensional Semiconductors for Novel Electronic Applications, 2020
Mohammad Karbalaei Akbari, Serge Zhuiykov
Biological synapses, as one of the key members of neural system, are the information channels ensuring short-term computation, long-term learning, and memorization by tuning the synaptic weights [9]. As a biological structure, a chemical synapse is a gapped connection between two neurons through which the communication between two individual axons is created by biochemical reactions. Uniqueness of the brain as the main intelligent organ arises from the highly-energetically efficient synaptic data processing. A typical neuron has up to several thousands of synapses where the presynaptic neurons are connected to the postsynaptic neurons via dendrites. A graphical interpretation of the synapse is demonstrated in Figure 7.1 [10]. The synapse is a biological junction where the electrochemical waves (action potentials) are transmitted through the axons of neuron. At the end of the axon, the presynaptic action potential signal reaches the presynaptic terminal and triggers the channel to release the chemical neurotransmitters, which are usually Na+ and K+ ions[11]. The released ionic neurotransmitters travel the synapse distance and bind to the molecular receptors in membrane of the opposite neuron junction. The delivered ionic species generate postsynaptic current on the opposite side of the synaptic cleft to finalize the signal transition process, which is briefly called interneural signaling. The postsynaptic response to a presynaptic signal is classified as being either excitatory or inhibitory. The excitatory postsynaptic current (EPSC) and inhibitory postsynaptic current (IPSC) are two famous postsynaptic outputs [12].
Building memory devices from biocomposite electronic materials
Published in Science and Technology of Advanced Materials, 2020
Xuechao Xing, Meng Chen, Yue Gong, Ziyu Lv, Su-Ting Han, Ye Zhou
Neuromorphic computing is an attractive research area, which will further overcome the von-Neumann bottleneck in the limited data transmission speed of memory and information processing. In order to simulate the synaptic plasticity of the biological nervous system, ultra-small-sized synaptic electronics are emphasized as a basic component of neuromorphic computing systems [97–100]. In addition to data storage devices based on bio-oil cubes, another attractive area of research is studying biological memory systems, and then simulating the structure and memory functions of these systems. In particular, the neural neuromorphic network of spikes consists of an array of interconnected pre-neurons and post-neurons, and synaptic plasticity triggered by chemical transmission is related to the temporal diversity of post-neurons and pre-neurons (Figure 7(a)). Typically, a chemical synapse, consisting of axons, synaptic spaces, and dendrites, is considered a functional connector that allows neurons to pass neurotransmitters to neighboring neurons. Synaptic plasticity and memory (SPM) hypothesis hold that synaptic plasticity is a straightforward decisive factor for acquiring memory and learning ability, synaptic plasticity also refers to increasing or decreasing of synaptic neural activity when it changes. According to the duration, synaptic plasticity can be divided into short-term plasticity (short-term enhancement and short-term inhibition) and long-term plasticity (long-term enhancement and long-term inhibition). In particular, long-term enhancement (LTP) is one of the most studied forms of synaptic plasticity. Therefore, the permanent change in the strength of synaptic connections plays a vital role in the consolidation of memory and the formation of durable memory [101]. Furthermore, development of artificial synapses mimics the activity-dependent long-term potential-controlled synaptic plasticity, paving the way for advanced information exchange inter-connectivity, which will further facilitate the development of highly integrated and low-power memory devices. The main feature of artificial synaptic materials is the realization of functions, including multi-level data storage and indispensable non-volatile properties [102–104]. Biological synaptic systems can establish a link between their response and the history of input stimuli, which should act as a multi-state memory and respond to the same repeated external input stimuli. On the other hand, in order to avoid refreshing or energy dissipation of the memory state during data storage, the non-volatile nature of the memory elements is essential [105]. So far, based on the existing device structures, including two-terminal and three-terminal devices, we have summarized the latest artificial synapses in this research area.