STDP-based unsupervised learning of memristive spiking neural network by Morris-Lecar model

These days, there is an increasing interest in implementation of spiking neural systems that can be used to perform complex computations or solve pattern recognition tasks like mammalian neocortex. In this paper, Morris-Lecar neuron neuron is utilized to implement bio-inspired memristive spiking neural network for unsupervised learning applications. The spike timing dependent plasticity learning mechanism has been applied as the learning scheme in the system. The memristive implementation of the Morris-Lecar neuron has been analyzed. Also the memristors are utilized as the synapses for the proposed system to reproduce long term potentiation and long term depression. The proposed platform is tested for pattern classification applications and the results are successfully confirmed its functionality.

[1]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

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

[3]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[4]  Majid Ahmadi,et al.  Optimized implementation of memristor-based full adder by material implication logic , 2014, 2014 21st IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[5]  Majid Ahmadi,et al.  Brain-inspired pattern classification with memristive neural network using the Hodgkin-Huxley neuron , 2016, 2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[6]  Steve Furber,et al.  High-performance computing for systems of spiking neurons , 2006 .

[7]  Leon O. Chua,et al.  Memristive Model of the Barnacle Giant Muscle Fibers , 2016, Int. J. Bifurc. Chaos.

[8]  Richard A. Chapman,et al.  Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Jonathan Touboul,et al.  Dynamics and bifurcations of the adaptive exponential integrate-and-fire model , 2008, Biological Cybernetics.

[10]  Dalibor Biolek,et al.  SPICE Model of Memristor with Nonlinear Dopant Drift , 2009 .

[11]  Eugene M. Izhikevich,et al.  Resonate-and-fire neurons , 2001, Neural Networks.

[12]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[13]  Anthony G. Pipe,et al.  Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach , 2007, IEEE Transactions on Neural Networks.

[14]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[15]  Majid Ahmadi,et al.  Memristor-based linear feedback shift register based on material implication logic , 2015, 2015 European Conference on Circuit Theory and Design (ECCTD).

[16]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[17]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[18]  Peng Li,et al.  Nonvolatile memristor memory: Device characteristics and design implications , 2009, 2009 IEEE/ACM International Conference on Computer-Aided Design - Digest of Technical Papers.

[19]  R. FitzHugh.  Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[20]  J. NAGUMOt,et al.  An Active Pulse Transmission Line Simulating Nerve Axon , 2006 .