TY - JOUR
T1 - A Model for R(t) Elements and R(t)-Based Spike-Timing-Dependent Plasticity with Basic Circuit Examples
AU - Ivans, Roberts C.
AU - Dahl, Sumedha Gandhereva
AU - Cantley, Kurtis D.
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology that leads to numerous behavioral and cognitive outcomes. Emulating STDP in electronic spiking neural networks with high-density memristive synapses is, therefore, of significant interest. While one popular method involves pulse-shaping the spiking neuron output voltages, an alternative approach is outlined in this article. The proposed STDP implementation uses time-varying dynamic resistance [ R(t) ] elements to achieve local synaptic learning from spike-pair STDP, spike triplet STDP, and firing rates. The R(t) elements are connected to each neuron circuit, thereby maintaining synaptic density and leveraging voltage division as a means of altering synaptic weight (memristor voltage). Example R(t) elements with their corresponding behaviors are demonstrated through simulation. A three-input-two-output network using single-memristor synaptic connections and R(t) elements is also simulated. Network-level effects, such as nonspecific synaptic plasticity, are discussed. Finally, spatiotemporal pattern recognition (STPR) using R(t) elements is demonstrated in simulation.
AB - Spike-timing-dependent plasticity (STDP) is a fundamental synaptic learning rule observed in biology that leads to numerous behavioral and cognitive outcomes. Emulating STDP in electronic spiking neural networks with high-density memristive synapses is, therefore, of significant interest. While one popular method involves pulse-shaping the spiking neuron output voltages, an alternative approach is outlined in this article. The proposed STDP implementation uses time-varying dynamic resistance [ R(t) ] elements to achieve local synaptic learning from spike-pair STDP, spike triplet STDP, and firing rates. The R(t) elements are connected to each neuron circuit, thereby maintaining synaptic density and leveraging voltage division as a means of altering synaptic weight (memristor voltage). Example R(t) elements with their corresponding behaviors are demonstrated through simulation. A three-input-two-output network using single-memristor synaptic connections and R(t) elements is also simulated. Network-level effects, such as nonspecific synaptic plasticity, are discussed. Finally, spatiotemporal pattern recognition (STPR) using R(t) elements is demonstrated in simulation.
KW - Memristor
KW - spike-timing-dependent plasticity (STDP)
KW - spiking neural network
KW - synapse
UR - http://www.scopus.com/inward/record.url?scp=85092680720&partnerID=8YFLogxK
UR - https://scholarworks.boisestate.edu/electrical_facpubs/477
U2 - 10.1109/TNNLS.2019.2952768
DO - 10.1109/TNNLS.2019.2952768
M3 - Article
C2 - 31869804
SN - 2162-237X
VL - 31
SP - 4206
EP - 4216
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 10
M1 - 8935410
ER -