PMID- 23886551
OWN - NLM
STAT- MEDLINE
DCOM- 20140421
LR  - 20130731
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 45
DP  - 2013 Sep
TI  - Design of silicon brains in the nano-CMOS era: spiking neurons, learning synapses 
      and neural architecture optimization.
PG  - 4-26
LID - S0893-6080(13)00159-7 [pii]
LID - 10.1016/j.neunet.2013.05.011 [doi]
AB  - We present a design framework for neuromorphic architectures in the nano-CMOS 
      era. Our approach to the design of spiking neurons and STDP learning circuits 
      relies on parallel computational structures where neurons are abstracted as 
      digital arithmetic logic units and communication processors. Using this approach, 
      we have developed arrays of silicon neurons that scale to millions of neurons in 
      a single state-of-the-art Field Programmable Gate Array (FPGA). We demonstrate 
      the validity of the design methodology through the implementation of cortical 
      development in a circuit of spiking neurons, STDP synapses, and neural 
      architecture optimization.
CI  - Copyright (c) 2013 Elsevier Ltd. All rights reserved.
FAU - Cassidy, Andrew S
AU  - Cassidy AS
AD  - Department of Electrical and Computer Engineering, Johns Hopkins University, 
      Baltimore, MD 21218, USA. andrewca@us.ibm.com
FAU - Georgiou, Julius
AU  - Georgiou J
FAU - Andreou, Andreas G
AU  - Andreou AG
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20130606
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - Action Potentials/*physiology
MH  - Brain/*cytology/physiology
MH  - Computer Simulation
MH  - Humans
MH  - *Learning
MH  - *Models, Neurological
MH  - Neurons/*physiology
OTO - NOTNLM
OT  - FPGA neural arrays
OT  - Learning in silicon
OT  - Neuromorphic engineering
OT  - Silicon brains
OT  - Silicon neurons
EDAT- 2013/07/28 06:00
MHDA- 2014/04/22 06:00
CRDT- 2013/07/27 06:00
PHST- 2012/08/31 00:00 [received]
PHST- 2013/05/20 00:00 [revised]
PHST- 2013/05/21 00:00 [accepted]
PHST- 2013/07/27 06:00 [entrez]
PHST- 2013/07/28 06:00 [pubmed]
PHST- 2014/04/22 06:00 [medline]
AID - S0893-6080(13)00159-7 [pii]
AID - 10.1016/j.neunet.2013.05.011 [doi]
PST - ppublish
SO  - Neural Netw. 2013 Sep;45:4-26. doi: 10.1016/j.neunet.2013.05.011. Epub 2013 Jun 
      6.