Complex Learning in Bio-plausible Memristive Networks

The emerging memristor-based neuromorphic engineering promises an efficient computing paradigm. However, the lack of both internal dynamics in the previous feedforward memristive networks and efficient learning algorithms in recurrent networks, fundamentally limits the learning ability of existing systems. In this work, we propose a framework to support complex learning functions by introducing dedicated learning algorithms to a bio-plausible recurrent memristive network with internal dynamics. We fabricate iron oxide memristor-based synapses, with well controllable plasticity and a wide dynamic range of excitatory/inhibitory connection weights, to build the network. To adaptively modify the synaptic weights, the comprehensive recursive least-squares (RLS) learning algorithm is introduced. Based on the proposed framework, the learning of various timing patterns and a complex spatiotemporal pattern of human motor is demonstrated. This work paves a new way to explore the brain-inspired complex learning in neuromorphic systems.

[1]  H. Sompolinsky,et al.  Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.

[2]  Leon O. Chua,et al.  Memristor Bridge Synapse-Based Neural Network and Its Learning , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[4]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[5]  Wolfgang Maass,et al.  Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. , 2014, Cerebral cortex.

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

[7]  Angela D Friederici,et al.  Two principles of organization in the prefrontal cortex are cognitive hierarchy and degree of automaticity , 2013, Nature Communications.

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  R Stanley Williams,et al.  Sub-100 fJ and sub-nanosecond thermally driven threshold switching in niobium oxide crosspoint nanodevices , 2012, Nanotechnology.

[10]  Eric Pop,et al.  Low-Power Switching of Phase-Change Materials with Carbon Nanotube Electrodes , 2011, Science.

[11]  Peter E. Latham,et al.  Randomly Connected Networks Have Short Temporal Memory , 2013, Neural Computation.

[12]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[13]  F. Wörgötter,et al.  Self-organized adaptation of a simple neural circuit enables complex robot behaviour , 2010, ArXiv.

[14]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

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

[16]  O. Cueto,et al.  Physical aspects of low power synapses based on phase change memory devices , 2012 .

[17]  Yoon-Ha Jeong,et al.  ReRAM-based synaptic device for neuromorphic computing , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[18]  H. Kim,et al.  RRAM-based synapse for neuromorphic system with pattern recognition function , 2012, 2012 International Electron Devices Meeting.

[19]  R. Kempter,et al.  Hebbian learning and spiking neurons , 1999 .

[20]  Shimeng Yu,et al.  Low-Energy Robust Neuromorphic Computation Using Synaptic Devices , 2012, IEEE Transactions on Electron Devices.

[21]  D. Querlioz,et al.  Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture , 2012, IEEE Transactions on Electron Devices.

[22]  Konstantin K. Likharev,et al.  CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks , 2011 .

[23]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[24]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[25]  Leon O. Chua.  Resistance switching memories are memristors , 2011 .

[26]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[27]  David Sussillo,et al.  A recurrent neural network for closed-loop intracortical brain–machine interface decoders , 2012, Journal of neural engineering.

[28]  David Sussillo,et al.  Neural circuits as computational dynamical systems , 2014, Current Opinion in Neurobiology.

[29]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[30]  W. J. Wang,et al.  Breaking the Speed Limits of Phase-Change Memory , 2012, Science.

[31]  Shinhyun Choi,et al.  Comprehensive physical model of dynamic resistive switching in an oxide memristor. , 2014, ACS nano.

[32]  Shimeng Yu,et al.  On the Switching Parameter Variation of Metal-Oxide RRAM—Part I: Physical Modeling and Simulation Methodology , 2012, IEEE Transactions on Electron Devices.

[33]  Luping Shi,et al.  Enabling an Integrated Rate-temporal Learning Scheme on Memristor , 2014, Scientific Reports.

[34]  Zhigang Zeng,et al.  Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators , 2014, IEEE Transactions on Fuzzy Systems.

[35]  Shukai Duan,et al.  An electronic implementation for Liao's chaotic delayed neuron model with non-monotonous activation function ☆ , 2007 .

[36]  Gregory S. Snider,et al.  Spike-timing-dependent learning in memristive nanodevices , 2008, 2008 IEEE International Symposium on Nanoscale Architectures.

[37]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[38]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[39]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[40]  Shimeng Yu,et al.  On the Switching Parameter Variation of Metal Oxide RRAM—Part II: Model Corroboration and Device Design Strategy , 2012, IEEE Transactions on Electron Devices.

[41]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[42]  Dharmendra S. Modha,et al.  The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[43]  Johannes Schemmel,et al.  A wafer-scale neuromorphic hardware system for large-scale neural modeling , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[44]  Narayan Srinivasa,et al.  A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. , 2012, Nano letters.

[45]  L. F. Abbott,et al.  Generating Coherent Patterns of Activity from Chaotic Neural Networks , 2009, Neuron.

[46]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[47]  Mohammed Ismail,et al.  Analog VLSI Implementation of Neural Systems , 2011, The Kluwer International Series in Engineering and Computer Science.

[48]  Qing Wan,et al.  Artificial synapse network on inorganic proton conductor for neuromorphic systems. , 2014, Nature communications.

[49]  Yong Liu,et al.  A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[50]  Fabien Alibart,et al.  A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing , 2011, ArXiv.

[51]  Kaushik Roy,et al.  Cognitive computing with spin-based neural networks , 2012, DAC Design Automation Conference 2012.

[52]  M. Alexander,et al.  Principles of Neural Science , 1981 .

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

[54]  X. Miao,et al.  Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems , 2014, Scientific Reports.

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

[56]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[57]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[58]  Shimeng Yu,et al.  Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.

[59]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[60]  Ligang Gao,et al.  High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm , 2011, Nanotechnology.

[61]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[62]  M. Mitchell Waldrop,et al.  Neuroelectronics: Smart connections , 2013, Nature.

[63]  Shimeng Yu,et al.  HfOx-based vertical resistive switching random access memory suitable for bit-cost-effective three-dimensional cross-point architecture. , 2013, ACS nano.

[64]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[65]  Shimeng Yu,et al.  An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation , 2011, IEEE Transactions on Electron Devices.

[66]  Dean V. Buonomano,et al.  ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS , 2012, Nature Neuroscience.

[67]  Geoffrey W. Burr,et al.  Nanoscale electronic synapses using phase change devices , 2013, JETC.

[68]  Luping Shi,et al.  Phase Change Random Access Memory Devices with Nickel Silicide and Platinum Silicide Electrode Contacts for Integration with CMOS Technology , 2011 .

[69]  Chuandong Li,et al.  Memristor-based chaotic neural networks for associative memory , 2014, Neural Computing and Applications.