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Scalable excitatory synaptic circuit design using floating gate based leaky integrators.
Kornijcuk, Vladimir; Lim, Hyungkwang; Kim, Inho; Park, Jong-Keuk; Lee, Wook-Seong; Choi, Jung-Hae; Choi, Byung Joon; Jeong, Doo Seok.
Afiliación
  • Kornijcuk V; Center for Electronic Materials, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Lim H; Department of Nanomaterials, University of Science and Technology, Daejeon, 34113, Republic of Korea.
  • Kim I; Center for Electronic Materials, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Park JK; Center for Electronic Materials, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Lee WS; Center for Electronic Materials, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Choi JH; Center for Electronic Materials, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Choi BJ; Center for Electronic Materials, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Jeong DS; Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea.
Sci Rep ; 7(1): 17579, 2017 12 14.
Article en En | MEDLINE | ID: mdl-29242504
ABSTRACT
We propose a scalable synaptic circuit realizing spike timing dependent plasticity (STDP)-compatible with randomly spiking neurons. The feasible working of the circuit was examined by circuit simulation using the BSIM 4.6.0 model. A distinguishable feature of the circuit is the use of floating-gate integrators that provide the compact implementation of biologically plausible relaxation time scale. This relaxation occurs on the basis of charge tunneling that mainly relies upon area-independent tunnel barrier properties (e.g. barrier width and height) rather than capacitance. The circuit simulations feature (i) weight-dependent STDP that spontaneously limits the synaptic weight growth, (ii) competitive synaptic adaptation within both unsupervised and supervised frameworks with randomly spiking neurons. The estimated power consumption is merely 34 pW, perhaps meeting one of the most crucial principles (power-efficiency) of neuromorphic engineering. Finally, a means of fine-tuning the STDP behavior is provided.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2017 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2017 Tipo del documento: Article