Integrated Circuit with Memristor Emulator Array and Neuron Circuits for Biologically Inspired Neuromorphic Pattern Recognition

This paper details an application-specific integrated circuit (ASIC) with an array of switched-resistor-based memristors (resistor with memory) and integrate & fire (I & F) neuron circuits for the development of memristor-based pattern recognition. Since real memristors are not commercially available, a compact memristor emulator is needed for device study. The designed ASIC has five memristor emulators with one having a conductance range from 4.88ns to 4.99μs (200kΩ to 204.8MΩ) and other four having conductance ranging from 195ns to 190μs (5.2kΩ to 5.12MΩ). Signal processing has been planned to be off-chip to get the freedom of programmability of a wide range of memristive behavior. This paper introduces the memristor emulator and the realization of synapse functionalities used in neuromorphic circuits such as long term potentiation (LTP), Long Term depression (LTD) and synaptic plasticity. The ASIC has two I & F neuron circuits which are intended to be used in conjunction with memristors in a multiple chip network for pattern recognition. This paper explains the memristor emulator, I & F neuron circuit and a respective neuromorphic system for pattern recognition simulated in LTspice. The ASIC has been fabricated in AMS 350nm process.

[1]  Siddharth Gaba,et al.  Synaptic behaviors and modeling of a metal oxide memristive device , 2011 .

[2]  M. Ito,et al.  Long-term depression. , 1989, Annual review of neuroscience.

[3]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[4]  Y. V. Pershin,et al.  SPICE Model of Memristive Devices with Threshold , 2012, 1204.2600.

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

[6]  Johannes Schemmel,et al.  Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[7]  Damien Querlioz,et al.  Simulation of a memristor-based spiking neural network immune to device variations , 2011, The 2011 International Joint Conference on Neural Networks.

[8]  J. Yang,et al.  Switching dynamics in titanium dioxide memristive devices , 2009 .

[9]  A. Konnerth,et al.  Long-term potentiation and functional synapse induction in developing hippocampus , 1996, Nature.

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

[11]  Sangho Shin,et al.  Compact Circuit Model and Hardware Emulation for Floating Memristor Devices , 2013, IEEE Circuits and Systems Magazine.

[12]  Y. Dan,et al.  Spike timing-dependent plasticity: from synapse to perception. , 2006, Physiological reviews.

[13]  T. Bliss,et al.  Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.

[14]  Massimiliano Di Ventra,et al.  Practical Approach to Programmable Analog Circuits With Memristors , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  Thomas Mussenbrock,et al.  Pattern recognition with TiO x -based memristive devices , 2015 .

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

[17]  Dietmar Schroeder,et al.  Integrated circuit with memristor emulator array and neuron circuits for neuromorphic pattern recognition , 2016, 2016 39th International Conference on Telecommunications and Signal Processing (TSP).

[18]  T. Bliss,et al.  A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.

[19]  Stephen J. Wolf,et al.  The elusive memristor: properties of basic electrical circuits , 2008, 0807.3994.

[20]  Masud H. Chowdhury,et al.  Memristor emulator based on practical current controlled model , 2015, 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS).

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

[22]  Y. Dan,et al.  Spike timing-dependent plasticity: a Hebbian learning rule. , 2008, Annual review of neuroscience.