1 / 50

Ella Gale , Ben de Lacy Costello and Andrew Adamatzky

Observation and Characterization of Memristor Current Spikes and their Application to Neuromorphic Computation. Ella Gale , Ben de Lacy Costello and Andrew Adamatzky. Contents. How do Neurons Compute? Competing Models for the Memristor Making Spiking Neural Networks with Memristors

boris
Download Presentation

Ella Gale , Ben de Lacy Costello and Andrew Adamatzky

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Observation and Characterization of Memristor Current Spikes and their Application to Neuromorphic Computation Ella Gale, Ben de Lacy Costello and Andrew Adamatzky

  2. Contents • How do Neurons Compute? • Competing Models for the Memristor • Making Spiking Neural Networks with Memristors • The Memristor Acting as a Neuron • Characteristics and Properties • Where do the Spikes come from?

  3. How Does the Brain Differ From a Modern-Day Computer? • Slow • Parallel Processing • High degree of interconnectivity • Spiking Neural Nets • Ionic • Analogue

  4. How does a Neuron Compute?

  5. Memristive Systems to Describe Nerve Axon Membranes

  6. Synapse Long-Term Potentiation

  7. The Memristor as a Synapse Before learning Before learning After learning After learning During learning

  8. Spike-Time Dependent Plasticity, STDP • Process by which synapses are potentiated • Related to Hebb’s Rule • Possibly a cause of memory and learning • Relative timing of spike inputs to a synapse important Bi and Poo, Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength and Postsynaptic Cell Type, J. Neurosci., 1998

  9. Memristor Structure and Function

  10. Phenomenological Model = ionic mobility of the O+ vacancies Roff = resistance of TiO2 Ron = resistance of TiO(2-x) Strukov et al, The Missing Memristor Found, Nature, 2008

  11. Chua’s Definitions of Types of Memristors Charge-Controlled Memristor Flux-Controlled Memristor L. Chua, Memristor – The Missing Circuit Element, IEEE Trans. Circuit Theory, 1971

  12. What the Flux? But, where is the magnetic flux? Strukov et al, 2008 Chua, 1971

  13. Starting From The Ions… • Memristance is a phenomenon associated with ionic current flow • Therefore  calculate the magnetic flux of the IONS • Vacancy Volume Current  , L = eLectric field • Vacancy Magnetic Field  • Vacancy Magnetic Flux 

  14. Memristance, as Derived from Ion Flow • Universal constants: • X, Experimental constants: product of surface area and electric field • , material variable, = Gale, The Missing Magnetic Flux in the HP Memristor Found, 2011

  15. Mem-Con Theory Gale, The Missing Magnetic Flux in the HP Memristor Found, Submitted, 2011

  16. Memristor I-V Behaviour

  17. Our Intent: • To make a memristor brain • & thus a machine intelligence

  18. Connecting Memristors with Spiking Neurons to Implement STDP Simulation Results 1. Zamarreno-Ramos et al, On Spike Time Dependent Plasticity, Memristive Devices and Building a Self-Learning Visual Cortex, Frontiers in Neuroscience, 2011 0. Linares-Barranco and Serrano-Gotarredona, Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses, Nature Preceedings, 2009

  19. But, Memristors Spike Naturally!

  20. Our Memristors • Crossed Aluminium electrodes • Thin-film (40nm) TiO2 sol-gel layer 1. Gergel-Hackett et al, A Flexible Solution Processed Memristor, IEEE Elec. Dev. Lett., 2009 2. Gale et al, Aluminium Electrodes Effect the Operation of Titanium Dioxide Sol-Gel Memristors, Submitted 2012

  21. Current Spikes Seen in I-t Plots

  22. Spikes are Reproducible Current Spike Response Voltage Square Wave

  23. Spikes are Repeatable Current Response Voltage Ramp

  24. Memristor Behaviour Looks Similar to Neurons Neuron Memristor Bal and McCormick, Synchronized Oscilliations in the Inferior Olive are controlled by the Hyperpolarisation-Activated Cation Current Ih, J. Neurophysiol, 77, 3145-3156, 1997

  25. Spikes Seen In the Literature

  26. SpintronicMemristor Current Spikes Pershinand Di Ventra, Spin Memristive Systems: Spin Memory Effects in Semi-conductor Spintronics, Phys. Rev. B, 2008

  27. Properties of Spikes • Direction of Spikes is related to not V • The switch to 0V has a associated current spike • Spikes are repeatable • Spikes are reproducable • Spikes are seen in bipolar switching memristors/ReRAM • Spikes are not seen in unipolar switching, UPS ReRAM type memristors

  28. Two Different Types of Memristor Behaviour Seen in Our Lab Curved (BPS-like) Memristors Triangular (UPS-like) Memristors • Pictures

  29. Two Different Types of Memristor Behaviour Seen in Our Lab Triangular (UPS-like) Memristors Curved (BPS-like) Memristors

  30. Where do the Spikes Come From? Does Current Theory Predict Their Existence?

  31. Mem-Con Model Applied to Memristor Spikes Memristors Neurons

  32. In Chua’s Model Neuron Voltage Spikes Memristor Current Spikes • Dynamics related to min. response time, τ, related to speed of ion diffusion across membrane • Memory property = ??? • Neuron operated in a current-controlled way • Dynamics related to τ, which is related to • Memory property = qv • Memristor operated in voltage controlled way

  33. What is the Memory Property of Neurons? • More complex system than a single memristor • Short-term memory associated with membrane potential • Long term memory associated with the number of synaptic buds

  34. Memristor Models Fit the Data Sol-Gel MemristorPositiveV Sol-Gel Memristor Negative V

  35. Memristor Model Fits the PEO-PANI Memristor

  36. Al-TiO2-Al Sol-Gel Memristor

  37. Time & Frequency Dependence of Hysteresis for Al-TiO2-Al

  38. Au-TiO2-Au WORMS Memory

  39. Au-TiO2-Au WORMS Memory Time Dependent I-V I-t Response to Stepped Voltage

  40. Al-TiO2-Al Current Response to Voltage Ramp Current Response Voltage Ramp

  41. Further Work • Neurology: • Modelling Neurons with the Mem-Con Theory to prove that they are Memristive • Investigate the Memory Property for neurons • Unconventional Computing: • Further Investigation of memristor and ReRAM properties • Attempt to build a neuromorphic control system for a navigation robot

  42. Summary • Neurons May Be Biological Memristors • Neurons Operate via Voltage Spikes • Memristors can Operative via Current Spikes • Thus, Memristors are Good Candidates for Neuromorphic Computation • A Memristor-based Neuromorphic Computer will be Voltage Controlled and transmit data via Current Spikes

  43. With Thanks to • Victor Erokhin and his group (University of Parma) • Steve Kitson (HP UK) • David Pearson (HP UK) • Bristol Robotics Laboratory • Ben de Lacy Costello • Andrew Adamatzky • David Howard • Larry Bull

More Related