Emulating short-term synaptic dynamics with memristive devices

Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems.

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

[2]  M M Merzenich,et al.  Temporal information transformed into a spatial code by a neural network with realistic properties , 1995, Science.

[3]  Christofer Toumazou,et al.  Two centuries of memristors. , 2012, Nature materials.

[4]  Yuriy V. Pershin,et al.  Memory effects in complex materials and nanoscale systems , 2010, 1011.3053.

[5]  Massimiliano Di Ventra,et al.  Phase-transition driven memristive system , 2009, 0901.0899.

[6]  C. N. Lau,et al.  The mechanism of electroforming of metal oxide memristive switches , 2009, Nanotechnology.

[7]  D. Jeong,et al.  Nanofilamentary resistive switching in binary oxide system; a review on the present status and outlook , 2011, Nanotechnology.

[8]  L. Abbott,et al.  A Quantitative Description of Short-Term Plasticity at Excitatory Synapses in Layer 2/3 of Rat Primary Visual Cortex , 1997, The Journal of Neuroscience.

[9]  Cheol Seong Hwang,et al.  Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold , 2013, Nanotechnology.

[10]  Byungnam Kahng,et al.  Interface-modified random circuit breaker network model applicable to both bipolar and unipolar resistance switching , 2011 .

[11]  황철성,et al.  Nanofilamentary resistive switching in binary oxide system , 2011 .

[12]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[13]  D. Buonomano,et al.  A Novel Learning Rule for Long-Term Plasticity of Short-Term Synaptic Plasticity Enhances Temporal Processing , 2011, Front. Integr. Neurosci..

[14]  Ali Khiat,et al.  Concurrent resistive and capacitive switching of nanoscale TiO2 memristors , 2012 .

[15]  Michele Giugliano,et al.  Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity , 2014, PloS one.

[16]  T. Prodromakis,et al.  Stochastic switching of TiO2-based memristive devices with identical initial memory states , 2014, Nanoscale Research Letters.

[17]  Thomas K. Berger,et al.  Heterogeneity in the pyramidal network of the medial prefrontal cortex , 2006, Nature Neuroscience.

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

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

[20]  J. Movshon.  Reliability of Neuronal Responses , 2000, Neuron.

[21]  C. D. de Kock,et al.  Hyperconnectivity and slow synapses during early development of medial prefrontal cortex in a mouse model for mental retardation and autism. , 2012, Cerebral cortex.

[22]  N. Wu,et al.  Evidence for an oxygen diffusion model for the electric pulse induced resistance change effect in transition-metal oxides. , 2006, Physical Review Letters.

[23]  K. D. Cantley,et al.  Spike-Timing-Dependent Plasticity Using Biologically Realistic Action Potentials and Low-Temperature Materials , 2013, IEEE Transactions on Nanotechnology.

[24]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[25]  Muhammad A. Alam,et al.  A study of soft and hard breakdown - Part I: Analysis of statistical percolation conductance , 2002 .

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

[27]  Wei Lu,et al.  Interference and memory capacity effects in memristive systems , 2013 .

[28]  X. Miao,et al.  Ultrafast Synaptic Events in a Chalcogenide Memristor , 2013, Scientific Reports.

[29]  Themistoklis Prodromakis,et al.  A Memristor SPICE Model Accounting for Synaptic Activity Dependence , 2015, PloS one.

[30]  Ziv Rotman,et al.  Role of synaptic dynamics and heterogeneity in neuronal learning of temporal code. , 2013, Journal of neurophysiology.

[31]  W. Maass,et al.  Efficient temporal processing with biologically realistic dynamic synapses , 2001, Network.

[32]  Liam McDaid,et al.  Silicon-Based Dynamic Synapse With Depressing Response , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[33]  R. Williams,et al.  Exponential ionic drift: fast switching and low volatility of thin-film memristors , 2009 .

[34]  R. Waser,et al.  Atomically controlled electrochemical nucleation at superionic solid electrolyte surfaces. , 2012, Nature materials.

[35]  H. Markram,et al.  Hyper-Connectivity and Hyper-Plasticity in the Medial Prefrontal Cortex in the Valproic Acid Animal Model of Autism , 2008, Frontiers in neural circuits.

[36]  Ricardo Cavicchioli,et al.  Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects , 2013, Nature Communications.

[37]  Yong Liu,et al.  Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems , 2013, IEEE Transactions on Electron Devices.

[38]  Christos Papavassiliou,et al.  A $\mu $ -Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays , 2015, IEEE Transactions on Electron Devices.

[39]  R. Douglas,et al.  A silicon neuron , 1991, Nature.

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

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

[42]  Matthias H. Hennig,et al.  Theoretical models of synaptic short term plasticity , 2013, Front. Comput. Neurosci..

[43]  Anuradha Rao,et al.  Birth of a Synapse Not Such Long Labor , 2000, Neuron.

[44]  H. Markram,et al.  Redistribution of synaptic efficacy between neocortical pyramidal neurons , 1996, Nature.

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

[46]  Giacomo Indiveri,et al.  An adaptive silicon synapse , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[47]  G. I. Meijer,et al.  Who Wins the Nonvolatile Memory Race? , 2008, Science.

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

[49]  M. Wuttig,et al.  Phase-change materials for rewriteable data storage. , 2007, Nature materials.

[50]  Wolfgang Maass,et al.  E?cient Temporal Processing with Biolog-ically Realistic Dynamic Synapses , 2001 .

[51]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

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

[53]  H. Sompolinsky,et al.  The tempotron: a neuron that learns spike timing–based decisions , 2006, Nature Neuroscience.

[54]  Martin Garwicz,et al.  Authenticity, Depression, and Deep Brain Stimulation , 2011, Front. Integr. Neurosci..

[55]  Myoung-Jae Lee,et al.  Modeling for bipolar resistive memory switching in transition-metal oxides , 2010 .

[56]  Christofer Toumazou,et al.  Modelling of current percolation channels in emerging resistive switching elements , 2012, 1206.2746.

[57]  Michele Giugliano,et al.  Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses , 2013, PloS one.

[58]  Michele Giugliano,et al.  Adaptation of short-term plasticity parameters via error-driven learning may explain the correlation between activity-dependent synaptic properties, connectivity motifs and target specificity , 2015, Front. Comput. Neurosci..

[59]  Michele Giugliano,et al.  Emergence of Connectivity Patterns from Long-Term and Short-Term Plasticities , 2012, ICANN.

[60]  Richard H. R. Hahnloser,et al.  Silicon synaptic depression , 2001, Biological Cybernetics.

[61]  Chiara Bartolozzi,et al.  Synaptic Dynamics in Analog VLSI , 2007, Neural Computation.

[62]  O. Richard,et al.  10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation , 2011, 2011 International Electron Devices Meeting.

[63]  D. Natelson,et al.  Origin of hysteresis in resistive switching in magnetite is Joule heating , 2009, 0905.3510.

[64]  H. Hwang,et al.  Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device , 2011, Nanotechnology.

[65]  M. P. Sah,et al.  Pinched Hysteresis Loops is the Fingerprint of Memristive Devices , 2012, 1202.2437.

[66]  Henry Markram,et al.  Neural Networks with Dynamic Synapses , 1998, Neural Computation.

[67]  B. Sturman,et al.  Origin of stretched exponential relaxation for hopping-transport models. , 2003, Physical review letters.

[68]  Ali Khiat,et al.  X-ray Absorption Spectroscopy Study of TiO2–x Thin Films for Memory Applications , 2015 .

[69]  Xiang Yang,et al.  Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices , 2012, Scientific Reports.

[70]  Panayiota Poirazi,et al.  Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses , 2013, Front. Comput. Neurosci..

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

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

[73]  A. Sawa.  Resistive switching in transition metal oxides , 2008 .

[74]  Yuchao Yang,et al.  Observation of conducting filament growth in nanoscale resistive memories , 2012, Nature Communications.

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

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

[77]  P. Dayan,et al.  Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials , 2010, Nature Neuroscience.

[78]  M. Gearing,et al.  Correction: Corrigendum: Tonic inhibition in dentate gyrus impairs long-term potentiation and memory in an Alzheimer’s disease model , 2014, Nature Communications.

[79]  Mark C. W. van Rossum,et al.  Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits , 2013, Front. Comput. Neurosci..

[80]  B. Kahng,et al.  Random Circuit Breaker Network Model for Unipolar Resistance Switching , 2008 .

[81]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[82]  Jae Hyuck Jang,et al.  Atomic structure of conducting nanofilaments in TiO2 resistive switching memory. , 2010, Nature nanotechnology.

[83]  Ofer Feinerman,et al.  Reliable neuronal logic devices from patterned hippocampal cultures , 2008 .

[84]  H. Markram,et al.  Spontaneous and evoked synaptic rewiring in the neonatal neocortex , 2006, Proceedings of the National Academy of Sciences.

[85]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.