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Medical Implants for Heart Failure:
Neuromodulation of the autonomic system with
central pattern generator hardware
Alain Nogaret
Department of Physics, University of Bath
Background
Aims
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Exact models
Software limitations
Capability
Translation
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Aims
To make analogue networks of silicon neurons which compete through mutually
inhibitory synapses to stimulate the heart with sequences of electric pulses
programmed to respond dynamically to physiological feedback.
Robust
Responds to
changes in
dynamics
Translation path:
Rice
grain
Human trials
and market
CPG demonstrator on PCB
Patent GB120917 (May 2012)
J.NeuroSci.Meth. 212, 124 (2013)
Today
Implant chips
Publications
Patents
Animal trials
Publications
MHRA approval
June 2017
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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“StateProof
of the
art:” data assimilation
“CPG”
hardware
of principle:
State
from
of electrophysiological
the neural
art in CPG
hardware
recordings of songbird HVC neurons
CalTech
Coordination of robotic legs.
(binary pulses, feed-forward
neural network, no adaptation)
Tenore et al., ISCA 2005
NorthEastern Univ. & ETH Zurich
Swimming motion in fish and
lampreys.
(Neuron approximation, small
parameter space, external control
electronics, no adaptation)
Ijspeert et al, Science 315, 1416 (2007)
Lee et al., Neurocomp. 71, 284 (2007)
CPG electronics implemented elsewhere is: rudimentary, far from biological reality
unsuited for medical applications and ”stiff” as no adaptation programmed in.
Aims
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Background
“State of the art”;
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Exact models
Software limitations
Capability
Translation
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Software neurons substituted to neurons inside biological CPGs
Advantages: Biologically meaningful, easy to program
Drawbacks: Hard to scale, limited computational power,
stiffness of systems of equations representing real neurons
Leech Heart CPG Emory Univ. / Ga State Univ.
Stomatogastric ganglion of the lobster UC San Diego
A.Olypher et al., J.Neurophysiol. 96, 2857 (2006)
Pinto et al., J. NeuroSci Meth. 108, 39 (2001)
Pinto et al., Phys. Rev. E 62, 2644 (2000)
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Modelling of Central Pattern Generator dynamics in Nogaret’s group
Rose-Hindmarsh
Neuron models.
(approximations
of real neurons).
The network
conductances,
and the inputs
determine the
spatiotemporal
sequences of
spike bursts.
The dynamics of
competing
neurons is
generally chaotic.
Modelling CPG in software can inform the design of the hardware.
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Modelling of Central Pattern Generator dynamics in Nogaret’s group
Question 1:
Is there a general method for calculating the neuron and network parameters so
that the CPG replicates the motor patterns of a physiological system (e.g.
biological , heart) and how these motor pattern change as a function of
stimulation ?
Question 2:
What type of neuron is required for this to work ?
Aims
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Background
Exact models
Software limitations
Capability
Translation
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Pros and Cons of software neurons:
Building exact neuron/network models with data assimilation
We are able to program the
dynamic response of the heart
to an arbitrary stimulus profile
using data assimilation:
1.
Assimilate cardiac
electrophysiological data
to obtain synaptic
conductances
2.
Set the conductance
values in the hardware
This method has successfully
obtained quantitative models
of real neurons (songbird HVC)
PI/UCSD work
Meliza et al., PLoS Comp Biol. (in revision)
Aims
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Background
Exact models
Software limitations
Capability
Translation
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Pros and cons of software neurons:
Building exact neurons models - theory
Multichannel Hodgkin-Huxley
 dV (t ) 
I app  V / Rs 
  J ion 

 Cm
dt
area




 da (t )
 i   i (V )1  ai (t )   i (V ) ai (t )
 dt
ai 0 (V ) 
1
1  tanh  i 
2
;
 i (V )  t i 0   i 1  tanh 2  it  
i
2
1  tanh  it 
i  1...12
i  1...12
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Limitations of software neurons: Sophisticated integration is needed
Fourth order RK
Integration.
Large systems of
coupled diff. eqs.
develop stiffness
associated with
different time
constants.
Adaptive stepsize
Fifth order RK
integration is
required to resolve
system stiffness.
Modelling CPG dynamics by software quickly becomes prohibitively expensive in
terms of computer time, and is not appropriate for real time responsiveness.
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Limitation of software neurons; sensitivity to parameters - bifurcation
Aims
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Background
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Comparative study:
Exact models
Software limitations
Capability
Translation
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Software vs hardware networks
Digital/software neurons:
Analogue (in)  Digital  Analogue (out)
Analogue/hardware networks:
Analogue (in)  Analogue (out)
Synchronous (internal clock, ADC. DAC)
Asynchronous – respond to stimulus in real time.
Slow – 30s to 300s to integrate 1 HVC neuron
Instantaneous response.
Computational accuracy and speed decreases
when the size of the network increases
Computation is instantaneous, it retains infinite
accuracy even in networks of infinite size.
Increasingly simple neuron approximations
needs to be considered as the network size
increases to keep integration tractable
Realistic multichannel neurons can be integrated
in networks of arbitrary size.
Hard to scale down.
Easy to scale down (ADC, DAC, CPU unnecessary)
Poor energy efficiency.
Battery lifetime >> patient life
Software is easy to program and modify.
(Useful for preliminary study)
Designing analogue electronics is more
demanding than programming software.
Our brains are analogue computers !
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Technological capability
Staff: Alain Nogaret, Le Zhao (PhD student)
Track record in theoretical modelling:
• Computer models of CPGs dynamics (Rose-Hindmarsh neurons, multichannel
HH conductance models).
• Exact neuron models (songbird HVC)
 Breakthrough in establishing parameter extraction techniques for
programming neurons and networks.
Circuit design and testing
• Printed circuit boards
• VLSI design – III-V quantum devices, CMOS circuits etc.
• Experimental bench for testing CPG response to electrophysiological
recordings to adapt CPG parameters to different type of animal experiments.
Nanofabrication facility and semiconductor processing expertise
Suite of class 100 clean rooms with electron beam lithography with laser
interferometer, optical litho., direct write laser litho., reactive ion etching, thin
film deposition systems, ALD, AFMs, Dektak profiler, various microscopes, wet
benches and wire bonders.
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Proofcapability:
of principle: data
assimilation
from electrophysiological
Running
of songbird
neurons
Processing
Micro
and nano-devices
madetitle
by the recordings
Nogaret over
the lastHVC
20 years
PI’s expertise in micro/nanofabrication: >20 years of experience in processing GaAs/AlGaAs,
InAs/AlSb/GaSb, Si etc. with optical / electron beam lithography and designing advanced devices for
Physics research. One of the most experienced device physicists in the UK.
NRC GaTech (91-93) / Nottingham (94-98) / Glasgow nanofab (94-98) / Bath Univ. nanofab (98-now).
Phys.Rev.Lett. 22, 226802 (2009)
50µm
New.J.Phys. 10, 083010 (2008)
Appl.Phys.Lett. 71, 2937 (1997)
PRB 74, 6443 (1993)
10µm
2µm
GaAs neuron
Free standing GaMnAs nerve fibre
Single electron tunnel device
RT diode interconnect
50nm
2.5µm
100nm
Silicon MEMs
Appl. Phys. Lett. 99, 242107 (2013)
EPR microwave source and coplanar waveguide
Appl. Phys. Lett. 99, 242107 (2012)
GaAs/AlAs RT loop InAs/GaSb tunnel diodes
PRB 50, 8074 (1994)
PRB 51, 13198 (1995)
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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Processing
CAD ofDevice
printed
circuits
boards
andat
lithographic
plates
Proofcapability:
of principle: data assimilation
from
processing
electrophysiological
expertise
recordings
Bath
ofmask
songbird
HVC neurons
Design of CPG circuit on Printed Circuit Board
(ALREADY BUILT and TESTED )
Design of lithographic process
for implant chips
scaling
Software: PCB design (Techsoft UK)
Software: Layout Editor
Wavemaker (Bernard Microsystems)
The PI and the University of Bath have unique expertise for making CPG circuits
in the miniaturized form suitable for medical implants.
Background
Aims
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Exact models
Software limitations
Capability
Translation
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Technology roadmap
Implants
PoC
Today
18 mo.
Heart failure – RSA
Rats
CRT / Adpative pacing
Human trials
42 mo.
114 mo.
Pigs
2N CPG on PCB 2N CPG Scaling of CPG on chip
CAD of lithographic
vv
Build x5
x3
Consulting on testing
vv& MHRA approval
layers / chip testing
Rats/Pigs
3N CPG on PCB
x3
3N CPG software
Rose-Hindmarsh model
Clean room
microfabrication
of
vv
implant chips
vv/
Manufacture of
implant vv
chipset and
casing
Proof of benefits
of RSA in animals
vvtrials
Human
Heart failure
CPG implants
Market
Multi input CPGs
 adaptive CRT, adaptive cardiac pacing, bioengineering applications…
‘Programming’ analogue CPGs to generate the motor patterns of
biological CPGs and their adaptation to physiological feedback.
Aims
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Background
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Exact models
Software limitations
Capability
Translation
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…
thefrom
answers
to the
last questions
Proof of principle: data assimilation
Device
processing
electrophysiological
expertise
atrecordings
Bath
of songbird HVC neurons
How to obtain respiratory signal?
• Unlike conventional medical devices, CPGs read and interpret information
directly from nerve inputs (Phenic, EMG, other neurons).
• Respiration may be read from sensors – small adaptation needed
 technological progress ?
Are there enough data to build the CPG for the proof of concept study?
Yes - a single input dual output CPG (2N) is currently used in animal trials.
This CPG needs minor adaptation for large animal trials.
3 phase stimulation will be developed to mimic the dynamics of the brain
stem CPG – see Julian’s electrophysiological recordings of brainstem neurons.
Simple software networks modelled at Bath
In-phase and out-of-phase synchronization of a mutually
excitatory and mutually inhibitory neuron pair.
Modelling the effects of transmission time delay on
remotely connected neurons. Increasing time delay can
induce out-of-phase synchronization in a mutually
excitatory pair and in phase synchronization in a mutually
inhibitory pair.