Memristor-based neuron circuit and method for applying learning algorithm in SPICE?

The learning of nonlinearly separable functions in cascaded memristor crossbar circuits is described and the feasibility of using them to develop low-power neuromorphic processors is demonstrated. This is the first study evaluating the training of memristor crossbars through SPICE simulations. It is important to capture the alternate current paths and wire resistance inherent in these circuits. The simulations show that neural network learning algorithms are able to train in the presence of alternate current paths and wire resistances. The fact that the approach reduces the area by three times and power by two orders of magnitude compared with the existing approaches that use virtual ground opamps to eliminate alternate current paths is demonstrated.

[1]  Bernabé Linares-Barranco,et al.  On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..

[2]  G. Subramanyam,et al.  A Memristor Device Model , 2011, IEEE Electron Device Letters.

[3]  Wei Lu,et al.  Two-terminal resistive switches (memristors) for memory and logic applications , 2011, 16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011).

[4]  M. R. McLean.  Concurrent Learning Algorithm and the Importance Map , 2014, Network Science and Cybersecurity.

[5]  W. Lu,et al.  High-density Crossbar Arrays Based on a Si Memristive System , 2008 .

[6]  L. Chua.  Memristor-The missing circuit element , 1971 .

[7]  Jacques-Olivier Klein,et al.  Robust neural logic block (NLB) based on memristor crossbar array , 2011, 2011 IEEE/ACM International Symposium on Nanoscale Architectures.

[8]  Fabien Alibart,et al.  Pattern classification by memristive crossbar circuits using ex situ and in situ training , 2013, Nature Communications.