PMID- 31519885
OWN - NLM
STAT- MEDLINE
DCOM- 20191230
LR  - 20231014
IS  - 2041-1723 (Electronic)
IS  - 2041-1723 (Linking)
VI  - 10
IP  - 1
DP  - 2019 Sep 13
TI  - Gaussian synapses for probabilistic neural networks.
PG  - 4199
LID - 10.1038/s41467-019-12035-6 [doi]
LID - 4199
AB  - The recent decline in energy, size and complexity scaling of traditional von 
      Neumann architecture has resurrected considerable interest in brain-inspired 
      computing. Artificial neural networks (ANNs) based on emerging devices, such as 
      memristors, achieve brain-like computing but lack energy-efficiency. Furthermore, 
      slow learning, incremental adaptation, and false convergence are unresolved 
      challenges for ANNs. In this article we, therefore, introduce Gaussian synapses 
      based on heterostructures of atomically thin two-dimensional (2D) layered 
      materials, namely molybdenum disulfide and black phosphorus field effect 
      transistors (FETs), as a class of analog and probabilistic computational 
      primitives for hardware implementation of statistical neural networks. We also 
      demonstrate complete tunability of amplitude, mean and standard deviation of the 
      Gaussian synapse via threshold engineering in dual gated molybdenum disulfide and 
      black phosphorus FETs. Finally, we show simulation results for classification of 
      brainwaves using Gaussian synapse based probabilistic neural networks.
FAU - Sebastian, Amritanand
AU  - Sebastian A
AUID- ORCID: 0000-0003-4558-0013
AD  - Department of Engineering Science and Mechanics, Pennsylvania State University, 
      University Park, PA, 16802, USA.
FAU - Pannone, Andrew
AU  - Pannone A
AD  - Department of Engineering Science and Mechanics, Pennsylvania State University, 
      University Park, PA, 16802, USA.
FAU - Subbulakshmi Radhakrishnan, Shiva
AU  - Subbulakshmi Radhakrishnan S
AUID- ORCID: 0000-0003-1136-7425
AD  - Department of Engineering Science and Mechanics, Pennsylvania State University, 
      University Park, PA, 16802, USA.
AD  - Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Ettimadai, 
      Coimbatore, Tamil Nadu, 641112, India.
FAU - Das, Saptarshi
AU  - Das S
AUID- ORCID: 0000-0002-0188-945X
AD  - Department of Engineering Science and Mechanics, Pennsylvania State University, 
      University Park, PA, 16802, USA. sud70@psu.edu.
AD  - Department of Materials Science and Engineering, Pennsylvania State University, 
      University Park, PA, 16802, USA. sud70@psu.edu.
AD  - Materials Research Institute, Pennsylvania State University, University Park, PA, 
      16802, USA. sud70@psu.edu.
LA  - eng
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20190913
PL  - England
TA  - Nat Commun
JT  - Nature communications
JID - 101528555
RN  - 0 (Disulfides)
RN  - 81AH48963U (Molybdenum)
RN  - ZC8B4P503V (molybdenum disulfide)
SB  - IM
MH  - Disulfides/chemistry
MH  - Molybdenum/chemistry
MH  - Nanotechnology
MH  - *Neural Networks, Computer
MH  - Normal Distribution
MH  - Transistors, Electronic
PMC - PMC6744503
COIS- The authors declare no competing interests.
EDAT- 2019/09/15 06:00
MHDA- 2019/12/31 06:00
PMCR- 2019/09/13
CRDT- 2019/09/15 06:00
PHST- 2019/02/06 00:00 [received]
PHST- 2019/08/13 00:00 [accepted]
PHST- 2019/09/15 06:00 [entrez]
PHST- 2019/09/15 06:00 [pubmed]
PHST- 2019/12/31 06:00 [medline]
PHST- 2019/09/13 00:00 [pmc-release]
AID - 10.1038/s41467-019-12035-6 [pii]
AID - 12035 [pii]
AID - 10.1038/s41467-019-12035-6 [doi]
PST - epublish
SO  - Nat Commun. 2019 Sep 13;10(1):4199. doi: 10.1038/s41467-019-12035-6.