Modeling of Spiking Analog Neural Circuits with Hebbian Learning, Using Amorphous Semiconductor Thin Film Transistors with Silicon Oxide Nitride Semiconductor Split Gates.
Richard WoodIan C. BrucePeter MascherPublished in: ICANN (1) (2012)
Keyphrases
- thin film
- hebbian learning
- high density
- field effect transistors
- plasma etching
- fuzzy cognitive maps
- spiking neurons
- electron microscopy
- room temperature
- silicon nitride
- recurrent neural networks
- circuit design
- spiking neural networks
- neural network
- low density
- semiconductor devices
- short circuit
- floating gate
- neuron model
- solar cell
- event driven
- causal relationships
- chemical vapor deposition
- graph theory
- data center
- complex systems
- multi layer
- high speed
- silicon dioxide
- steady state
- biologically inspired
- spike trains
- decision processes
- decision support system
- motor control
- neural network model
- low power
- feedback loop