PMID- 34648278
OWN - NLM
STAT- MEDLINE
DCOM- 20211102
LR  - 20211102
IS  - 1936-086X (Electronic)
IS  - 1936-0851 (Linking)
VI  - 15
IP  - 10
DP  - 2021 Oct 26
TI  - Demonstration of Stochastic Resonance, Population Coding, and Population Voting 
      Using Artificial MoS(2) Based Synapses.
PG  - 16172-16182
LID - 10.1021/acsnano.1c05042 [doi]
AB  - Fast detection of weak signals at low energy expenditure is a challenging but 
      inescapable task for the evolutionary success of animals that survive in resource 
      constrained environments. This task is accomplished by the sensory nervous system 
      by exploiting the synergy between three astounding neural phenomena, namely, 
      stochastic resonance (SR), population coding (PC), and population voting (PV). In 
      SR, the constructive role of synaptic noise is exploited for the detection of 
      otherwise invisible signals. In PC, the redundancy in neural population is 
      exploited to reduce the detection latency. Finally, PV ensures unambiguous signal 
      detection even in the presence of excessive noise. Here we adopt a similar 
      strategies and experimentally demonstrate how a population of stochastic 
      artificial neurons based on monolayer MoS(2) field effect transistors (FETs) can 
      use an optimum amount of white Gaussian noise and population voting to detect 
      invisible signals at a frugal energy expenditure ( approximately 10s of nano-Joules). Our 
      findings can aid remote sensing in the emerging era of the Internet of things 
      (IoT) that thrive on energy efficiency.
FAU - Dodda, Akhil
AU  - Dodda A
AD  - Department of Engineering Science and Mechanics, Pennsylvania State University, 
      University Park, Pennsylvania 16802, United States.
FAU - Das, Saptarshi
AU  - Das S
AUID- ORCID: 0000-0002-0188-945X
AD  - Department of Engineering Science and Mechanics, Pennsylvania State University, 
      University Park, Pennsylvania 16802, United States.
AD  - Department of Materials Science and Engineering, Pennsylvania State University, 
      University Park, Pennsylvania 16802, United States.
AD  - Materials Research Institute, Pennsylvania State University, University Park, 
      Pennsylvania 16802, United States.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20211014
PL  - United States
TA  - ACS Nano
JT  - ACS nano
JID - 101313589
RN  - 81AH48963U (Molybdenum)
SB  - IM
MH  - Animals
MH  - *Molybdenum
MH  - Neurons
MH  - Politics
MH  - Stochastic Processes
MH  - *Synapses
OTO - NOTNLM
OT  - low-power sensors
OT  - monolayer MoS2 field effect transistors
OT  - population coding
OT  - population voting
OT  - stochastic resonance
OT  - subthreshold signal detection
OT  - two-dimensional materials
EDAT- 2021/10/15 06:00
MHDA- 2021/11/03 06:00
CRDT- 2021/10/14 17:11
PHST- 2021/10/15 06:00 [pubmed]
PHST- 2021/11/03 06:00 [medline]
PHST- 2021/10/14 17:11 [entrez]
AID - 10.1021/acsnano.1c05042 [doi]
PST - ppublish
SO  - ACS Nano. 2021 Oct 26;15(10):16172-16182. doi: 10.1021/acsnano.1c05042. Epub 2021 
      Oct 14.