Bioinspired neuromorphic module based on carbon nanotube/C60/polymer composite
| Autor: | Kyung-hyun Kim, Dongwon Lee, Andrew Tudor, Yong Chen, Alex Ming Shen, Chia-Ling Chen |
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| Rok vydání: | 2015 |
| Předmět: |
Signal processing
Materials science Artificial neural network Mechanical Engineering Nanotechnology Carbon nanotube law.invention Neuromorphic engineering Mechanics of Materials Power consumption law Materials Chemistry Ceramics and Composites Electronic engineering Polymer composites Spike (software development) |
| Zdroj: | Journal of Composite Materials. 49:1809-1822 |
| ISSN: | 1530-793X 0021-9983 |
| Popis: | It is extremely challenging to imitate neural networks with their high-speed parallel signal processing, low power consumption, and intelligent learning capability. In this work, we report a spike neuromorphic module composed of “synapstors” made from carbon nanotube/C60/polyimide composite and “CMOS Somas” made from complementary metal-oxide semiconductor electronic circuits. The “synapstor” emulates a biological synapse with spike signal processing, plasticity, and memory; the “CMOS Soma” emulates a Soma in a biological neuron with analog parallel signal processing and spike generation. Spikes, short potential pulses, and input to the synapstors trigger postsynaptic currents and generate output spikes from the CMOS Somas in a parallel manner with low power consumption. The module can be modified dynamically on the basis of the synapstor plasticity. Spike neuromorphic modules could potentially be scaled up to emulate biologic neural networks and their functions. |
| Databáze: | OpenAIRE |
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