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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 Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation “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 Background “State of the art”; Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation Limitation of software neurons; sensitivity to parameters - bifurcation Aims Background Comparative study: Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation 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 Exact models Software limitations Capability Translation 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 Background Exact models Software limitations Capability Translation … 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.