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 3,232 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Neuromorphic computational systems that emulate biological synapses in the human brain are fundamental in the development of artificial intelligence protocols beyond the standard von Neumann architecture. Such systems require new types of building blocks, such as memristors that access a quasi-continuous and wide range of conductive states, which is still an obstacle for the realization of high-efficiency and large-capacity learning in neuromorphoric simulation. Here, we introduce hydrogen and sodium titanate nanobelts, the intermediate products of hydrothermal synthesis of TiO2 nanobelts, to emulate the synaptic behavior. Devices incorporating a single titanate nanobelt demonstrate robust and reliable synaptic functions, including excitatory postsynaptic current, paired pulse facilitation, short term plasticity, potentiation and depression, as well as learning-forgetting behavior. In particular, the gradual modulation of conductive states in the single nanobelt device can be achieved by a large number of identical pulses. The mechanism for synaptic functionality of the titanate nanobelt device is attributed to the competition between an electric field driven migration of oxygen vacancies and a thermally induced spontaneous diffusion. These results provide insight into the potential use of titanate nanobelts in synaptic applications requiring continuously addressable states coupled with high processing efficiency.
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PMID:Oxygen vacancy migration/diffusion induced synaptic plasticity in a single titanate nanobelt. 2954 96

Artificial synapses are the fundamental of building a neuron network for neuromorphic computing to overcome the bottleneck of the von Neumann system. Based on a low-temperature atomic layer deposition process, a flexible electrical synapse was proposed and showed bipolar resistive switching characteristics. With the formation and rupture of ions conductive filaments path, the conductance was modulated gradually. Under a series of pre-synaptic spikes, the device successfully emulated remarkable short-term plasticity, long-term plasticity, and forgetting behaviors. Therefore, memory and learning ability were integrated to the single flexible memristor, which are promising for the next-generation of artificial neuromorphic computing systems.
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PMID:Atomic Layer Deposited Hf0.5Zr0.5O2-based Flexible Memristor with Short/Long-Term Synaptic Plasticity. 3087 93

Spiking Neural Networks (SNNs) have shown favorable performance recently. Nonetheless, the time-consuming computation on neuron level and complex optimization limit their real-time application. Curiosity has shown great performance in brain learning, which helps biological brains grasp new knowledge efficiently and actively. Inspired by this leaning mechanism, we propose a curiosity-based SNN (CBSNN) model, which contains four main learning processes. Firstly, the network is trained with biologically plausible plasticity principles to get the novelty estimations of all samples in only one epoch; secondly, the CBSNN begins to repeatedly learn the samples whose novelty estimations exceed the novelty threshold and dynamically update the novelty estimations of samples according to the learning results in five epochs; thirdly, in order to avoid the overfitting of the novel samples and forgetting of the learned samples, CBSNN retrains all samples in one epoch; finally, step two and step three are periodically taken until network convergence. Compared with the state-of-the-art Voltage-driven Plasticity-centric SNN (VPSNN) under standard architecture, our model achieves a higher accuracy of 98.55% with only 54.95% of its computation cost on the MNIST hand-written digit recognition dataset. Similar conclusion can also be found out in other datasets, i.e., Iris, NETtalk, Fashion-MNIST, and CIFAR-10, respectively. More experiments and analysis further prove that such curiosity-based learning theory is helpful in improving the efficiency of SNNs. As far as we know, this is the first practical combination of the curiosity mechanism and SNN, and these improvements will make the realistic application of SNNs possible on more specific tasks within the von Neumann framework.
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PMID:A Curiosity-Based Learning Method for Spiking Neural Networks. 3235 76

As one of the emerging neuromorphic computing devices, memristors may break through the limitation of traditional computers with a von Neumann architecture. However, the development of flexible memristors is limited by the high-temperature fabrication process, large operating voltage and non-uniform distribution of resistance. The room-temperature process has attracted great attention due to its advantages of low thermal dissipation, low cost and excellent compatibility with flexible electronics. Here, we proposed a fully physical vapour deposition (PVD) process for fabricating a memristor without additional heat treatment. The device showed excellent resistive switching characteristics with ultralow set/reset voltages (0.48 V/-0.39 V), uniform distribution (10%/15%), stable retention characteristic, multilevel storage behavior and reliable flexibility (radius of 10 mm). With continuously modulated conductance, typical synaptic plasticities were simulated by our flexible biomemristor, including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) and learning-forgetting curve. Furthermore, the array learning behavior like that of the human brain was simulated with these trainable biomemristors. This study paves a new way for developing low-cost, wearable, neuromorphic computing electronics at room temperature and expands the applications of artificial synapse arrays.
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PMID:Room-temperature developed flexible biomemristor with ultralow switching voltage for array learning. 3229 83