PMID- 30374283
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201001
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 12
DP  - 2018
TI  - On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights.
PG  - 665
LID - 10.3389/fnins.2018.00665 [doi]
LID - 665
AB  - In computational neuroscience, synaptic plasticity learning rules are typically 
      studied using the full 64-bit floating point precision computers provide. 
      However, for dedicated hardware implementations, the precision used not only 
      penalizes directly the required memory resources, but also the computing, 
      communication, and energy resources. When it comes to hardware engineering, a key 
      question is always to find the minimum number of necessary bits to keep the 
      neurocomputational system working satisfactorily. Here we present some techniques 
      and results obtained when limiting synaptic weights to 1-bit precision, applied 
      to a Spike-Timing-Dependent-Plasticity (STDP) learning rule in Spiking Neural 
      Networks (SNN). We first illustrate the 1-bit synapses STDP operation by 
      replicating a classical biological experiment on visual orientation tuning, using 
      a simple four neuron setup. After this, we apply 1-bit STDP learning to the 
      hidden feature extraction layer of a 2-layer system, where for the second (and 
      output) layer we use already reported SNN classifiers. The systems are tested on 
      two spiking datasets: a Dynamic Vision Sensor (DVS) recorded poker card symbols 
      dataset and a Poisson-distributed spike representation MNIST dataset version. 
      Tests are performed using the in-house MegaSim event-driven behavioral simulator 
      and by implementing the systems on FPGA (Field Programmable Gate Array) hardware.
FAU - Yousefzadeh, Amirreza
AU  - Yousefzadeh A
AD  - Instituto de Microelectronica de Sevilla (IMSE-CNM), CSIC and Universidad de 
      Sevilla, Sevilla, Spain.
FAU - Stromatias, Evangelos
AU  - Stromatias E
AD  - Instituto de Microelectronica de Sevilla (IMSE-CNM), CSIC and Universidad de 
      Sevilla, Sevilla, Spain.
FAU - Soto, Miguel
AU  - Soto M
AD  - Instituto de Microelectronica de Sevilla (IMSE-CNM), CSIC and Universidad de 
      Sevilla, Sevilla, Spain.
FAU - Serrano-Gotarredona, Teresa
AU  - Serrano-Gotarredona T
AD  - Instituto de Microelectronica de Sevilla (IMSE-CNM), CSIC and Universidad de 
      Sevilla, Sevilla, Spain.
FAU - Linares-Barranco, Bernabe
AU  - Linares-Barranco B
AD  - Instituto de Microelectronica de Sevilla (IMSE-CNM), CSIC and Universidad de 
      Sevilla, Sevilla, Spain.
LA  - eng
PT  - Journal Article
DEP - 20181015
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC6196279
OTO - NOTNLM
OT  - feature extraction
OT  - neuromorphic systems
OT  - spike timing dependent plasticity
OT  - spiking neural networks
OT  - stochastic learning
EDAT- 2018/10/31 06:00
MHDA- 2018/10/31 06:01
PMCR- 2018/01/01
CRDT- 2018/10/31 06:00
PHST- 2017/12/05 00:00 [received]
PHST- 2018/09/04 00:00 [accepted]
PHST- 2018/10/31 06:00 [entrez]
PHST- 2018/10/31 06:00 [pubmed]
PHST- 2018/10/31 06:01 [medline]
PHST- 2018/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2018.00665 [doi]
PST - epublish
SO  - Front Neurosci. 2018 Oct 15;12:665. doi: 10.3389/fnins.2018.00665. eCollection 
      2018.