PMID- 25484854
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20141208
LR  - 20200930
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 8
DP  - 2014
TI  - FPGA implementation of a biological neural network based on the Hodgkin-Huxley 
      neuron model.
PG  - 379
LID - 10.3389/fnins.2014.00379 [doi]
LID - 379
AB  - A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) 
      model of a neural network on FPGA (Field Programmable Gate Array) is presented. 
      The central implementation challenge is H-H model complexity that puts limits on 
      the network size and on the execution speed. However, basics of the original 
      model cannot be compromised when effect of synaptic specifications on the network 
      behavior is the subject of study. To solve the problem, we used computational 
      techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and 
      step-by-step integration in the implementation of arithmetic circuits. In 
      addition, we employed different techniques such as sharing resources to preserve 
      the details of model as well as increasing the network size in addition to 
      keeping the network execution speed close to real time while having high 
      precision. Implementation of a two mini-columns network with 120/30 
      excitatory/inhibitory neurons is provided to investigate the characteristic of 
      our method in practice. The implementation techniques provide an opportunity to 
      construct large FPGA-based network models to investigate the effect of different 
      neurophysiological mechanisms, like voltage-gated channels and synaptic 
      activities, on the behavior of a neural network in an appropriate execution time. 
      Additional to inherent properties of FPGA, like parallelism and 
      re-configurability, our approach makes the FPGA-based system a proper candidate 
      for study on neural control of cognitive robots and systems as well.
FAU - Yaghini Bonabi, Safa
AU  - Yaghini Bonabi S
AD  - Cognitive Robotic Lab., School of Electrical and Computer Engineering, College of 
      Engineering, University of Tehran Tehran, Iran.
FAU - Asgharian, Hassan
AU  - Asgharian H
AD  - Research Center of Information Technology, Department of Computer Engineering, 
      Iran University of Science and Technology Tehran, Iran.
FAU - Safari, Saeed
AU  - Safari S
AD  - High Performance Embedded Computing Lab., School of Electrical and Computer 
      Engineering, College of Engineering, University of Tehran Tehran, Iran.
FAU - Nili Ahmadabadi, Majid
AU  - Nili Ahmadabadi M
AD  - Cognitive Robotic Lab., School of Electrical and Computer Engineering, College of 
      Engineering, University of Tehran Tehran, Iran ; School of Cognitive Sciences, 
      Institute for Research in Fundamental Sciences, IPM Tehran, Iran.
LA  - eng
PT  - Journal Article
DEP - 20141121
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC4240168
OTO - NOTNLM
OT  - FPGA
OT  - Hodgkin-Huxley
OT  - digital hardware implementation
OT  - neural network
OT  - neural pool
EDAT- 2014/12/09 06:00
MHDA- 2014/12/09 06:01
PMCR- 2014/01/01
CRDT- 2014/12/09 06:00
PHST- 2014/07/17 00:00 [received]
PHST- 2014/11/05 00:00 [accepted]
PHST- 2014/12/09 06:00 [entrez]
PHST- 2014/12/09 06:00 [pubmed]
PHST- 2014/12/09 06:01 [medline]
PHST- 2014/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2014.00379 [doi]
PST - epublish
SO  - Front Neurosci. 2014 Nov 21;8:379. doi: 10.3389/fnins.2014.00379. eCollection 
      2014.