Ella Gale , Ben de Lacy Costello and Andrew Adamatzky
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Transcript of Ella Gale , Ben de Lacy Costello and Andrew Adamatzky
Ella Gale, Ben de Lacy Costello and Andrew
Adamatzky
Observation and Characterization of Memristor
Current Spikes and their Application to Neuromorphic
Computation
• 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?
Contents
• Slow• Parallel Processing• High degree of interconnectivity• Spiking Neural Nets• Ionic• Analogue
How Does the Brain Differ From a Modern-Day Computer?
Influx of Ionic I
Voltage Spike
Axon:Transmission along
neuron
Synapse:Transmission
between neurons
How does a Neuron Compute?
Memristive Systems to Describe Nerve Axon
Membranes
Synapse Long-Term Potentiation
The Memristor as a Synapse
Before learning Before learning
During learningAfter learning
After learning
• 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
Spike-Time Dependent Plasticity, STDP
Bi and Poo, Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength and Postsynaptic Cell Type, J. Neurosci., 1998
Memristor Structure and Function
Phenomenological Model
𝑀 (𝑞 (𝑡 ) )=𝑅off−𝜇𝑣
𝐷2 𝑅off 𝑅on𝑞 (𝑡)
Strukov et al, The Missing Memristor Found, Nature, 2008
= ionic mobility of the O+ vacancies
Roff = resistance of TiO2
Ron = resistance of TiO(2-x)
Charge-Controlled Memristor
Flux-Controlled Memristor
Chua’s Definitions of Types of Memristors
L. Chua, Memristor – The Missing Circuit Element, IEEE Trans. Circuit Theory, 1971
What the Flux?
𝑑𝜑=𝑀 (𝑞 (𝑡 ) )𝑑𝑞𝑀 (𝑞 (𝑡 ) )=𝑅𝑜𝑓𝑓−𝜇𝑣
𝐷2 𝑅𝑜𝑓𝑓 𝑅𝑜𝑛𝑞(𝑡)
But, where is the magnetic flux?
𝑉=𝑀 (𝑡 ) 𝐼
Chua, 1971Strukov et al, 2008
• 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
Starting From The Ions…
• Universal constants:
• X, Experimental constants: product of surface area and electric field
• , material variable, =
Memristance, as Derived from Ion Flow
Gale, The Missing Magnetic Flux in the HP Memristor Found, 2011
Mem-Con Theory
𝑞 ↔ 𝑀(𝑞) ↔ 𝜑 ↑ 𝑉 ↔ 𝑅𝑡𝑜𝑡(𝑡) ↔ 𝐼
Ionic Electronic
Gale, The Missing Magnetic Flux in the HP Memristor Found, Submitted, 2011
Memristor I-V Behaviour
To make a memristor brain
& thus a machine intelligence
Our Intent:
Connecting Memristors with Spiking Neurons to Implement STDP
1. Zamarreno-Ramos et al, On Spike Time Dependent Plasticity, Memristive Devices and Building a Self-Learning Visual Cortex, Frontiers in Neuroscience, 20110. Linares-Barranco and Serrano-Gotarredona, Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses, Nature Preceedings, 2009
Simulation Results
Memristors Spike
Naturally!
But,
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., 20092. Gale et al, Aluminium Electrodes Effect the Operation of Titanium Dioxide Sol-Gel Memristors, Submitted 2012
Current Spikes Seen in I-t Plots
Voltage Square Wave Current Spike Response
Spikes are Reproducible
Voltage Ramp Current Response
Spikes are Repeatable
Neuron
Memristor
Memristor Behaviour Looks Similar to Neurons
Bal and McCormick, Synchronized Oscilliations in the Inferior Olive are controlled by the Hyperpolarisation-Activated Cation Current Ih, J. Neurophysiol, 77, 3145-3156, 1997
SPIKES SEEN IN THE LITERATURE
Pershin and Di Ventra, Spin Memristive Systems: Spin Memory Effects in Semi-conductor Spintronics, Phys. Rev. B, 2008
Spintronic Memristor Current 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
Properties of Spikes
Pictures
Curved (BPS-like) Memristors
Triangular (UPS-like) Memristors
Two Different Types of Memristor Behaviour Seen in Our Lab
Curved (BPS-like) Memristors
Triangular (UPS-like) Memristors
Two Different Types of Memristor Behaviour Seen in Our Lab
Where do the Spikes Come From?
Does Current Theory Predict Their Existence?
q φI V
q φV I
Neurons Memristors
Mem-Con Model Applied to Memristor 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
Neuron Voltage Spikes Memristor Current Spikes
In Chua’s Model
• More complex system than a single memristor
• Short-term memory associated with membrane potential
• Long term memory associated with the number of synaptic buds
What is the Memory Property of Neurons?
Sol-Gel Memristor Negative V
Sol-Gel Memristor Positive V
Memristor Models Fit the Data
Memristor Model Fits the PEO-PANI Memristor
Al-TiO2-Al Sol-Gel Memristor
Time & Frequency Dependence of Hysteresis for Al-TiO2-Al
Au-TiO2-Au WORMS Memory
I-t Response to Stepped Voltage
Time Dependent I-V
Au-TiO2-Au WORMS Memory
Voltage Ramp Current Response
Al-TiO2-Al Current Response to Voltage Ramp
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
Further Work
• 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
Summary
• Ben de Lacy Costello
• Andrew Adamatzky• David Howard• Larry Bull
With Thanks to
• Victor Erokhin and his group (University of Parma)
• Steve Kitson (HP UK)• David Pearson (HP
UK)
• Bristol Robotics Laboratory