Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses

abstract

• A memrsitor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. In this paper, we present a class of memristor-based neural circuits comprising leaky integrate-and-fire (I & F) neurons and memristor-based learning synapses. Employing these neuron circuits and corresponding SPICE models, the properties of a two neurons network are shown to be similar to biology. During correlated spiking of the pre- and post-synaptic neurons, the strength of the synaptic connection increases. Conversely, it is diminished when the spiking is uncorrelated. This synaptic plasticity and associative learning is essential for performing useful computation and adaptation in large scale artificial neural networks. Finally, future circuit design and consideration are discussed with the memristor-based neural networks. 2012 Springer Science+Business Media New York.

authors

• Huang, Tingwen

published proceedings

• NEURAL PROCESSING LETTERS

author list (cited authors)

• Wen, S., Zeng, Z., & Huang, T.

citation count

• 21

complete list of authors

• Wen, Shiping||Zeng, Zhigang||Huang, Tingwen

publication date

• August 2013

publisher

• Springer Nature  Publisher

published in

• Neural Processing Letters  Journal

keywords

• Associative Learning
• Memristor
• Neural Network
• Synaptic Weight

Digital Object Identifier (DOI)

• 10.1007/s11063-012-9263-8

start page

• 69

end page

• 80

volume

• 38

issue

• 1

URL

• http://dx.doi.org/10.1007/s11063-012-9263-8