The neuromorphic computing for biointegrated electronics

Abstract

This review investigates the transformative potential of neuromorphic computing in advancing biointegrated electronics, with a particular emphasis on applications in medical sensing, diagnostics, and therapeutic interventions. By examining the convergence of edge computing and neuromorphic principles, we explore how emulating the operational principles of the human brain can enhance the energy efficiency and functionality of biointegrated electronics. The review begins with an introduction to recent breakthroughs in materials and circuit designs that aim to mimic various aspects of the biological nervous system. Subsequent sections synthesize demonstrations of neuromorphic systems designed to augment the functionality of healthcare-related electronic systems, including those capable of direct signal communication with biological tissues. The neuromorphic biointegrated devices remain in a nascent stage, with a relatively limited number of publications available. The current review aims to meticulously summarize these pioneering studies to evaluate the current state and propose future directions to advance the interdisciplinary field.

Keywords

Neuromorphic computing / biointegrated electronics / memristor / memtransistor

References

[1] Dubey A.Cortical electrocorticogram (ECoG) is a local signal.J Neurosci2019;39:4299-311 PMCID:PMC6538865

[2] Alahi MEE,Xu Z,Wu T.Recent advancement of electrocorticography (ECoG) electrodes for chronic neural recording/stimulation.Mater Today Commun2021;29:102853

[3] Yang T,Schwartz TH.Intraoperative electrocorticography (ECog): indications, techniques, and utility in epilepsy surgery.Epileptic Disord2014;16:271-9

[4] Boran E,Krayenbühl N.High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome.Clin Neurophysiol2019;130:1882-8

[5] Anwar H,Nadeem N,Ali M.Epileptic seizures.Discoveries2020;8:e110 PMCID:PMC7305811

[6] Shoeibi A,Ghassemi N.Epileptic seizures detection using deep learning techniques: a review.Int J Environ Res Public Health2021;18:5780 PMCID:PMC8199071

[7] Assi E, Nguyen DK, Rihana S, Sawan M. Towards accurate prediction of epileptic seizures: a review.Biomed Signal Proces2017;34:144-57

[8] Amengual-Gual M,Loddenkemper T.Patterns of epileptic seizure occurrence.Brain Res2019;1703:3-12

[9] Das R,Heidari H.Biointegrated and wirelessly powered implantable brain devices: a review.IEEE Trans Biomed Circuits Syst2020;14:343-58

[10] Koo JH,Yoo S,Son D.Unconventional device and material approaches for monolithic biointegration of implantable sensors and wearable electronics.Adv Mater Technol2020;5:2000407

[11] Ray TR,Bandodkar AJ.Bio-integrated wearable systems: a comprehensive review.Chem Rev2019;119:5461-533

[12] Farkhani H,Farkhani S,Moradi F.A low-power high-speed spintronics-based neuromorphic computing system using real-time tracking method.IEEE J Emerg Sel Topics Circuits Syst2018;8:627-38

[13] Xia Q.Memristive crossbar arrays for brain-inspired computing.Nat Mater2019;18:309-23

[14] Ielmini D.In-memory computing with resistive switching devices.Nat Electron2018;1:333-43

[15] Basheer IA.Artificial neural networks: fundamentals, computing, design, and application.J Microbiol Methods2000;43:3-31

[16] Chun J,Kim SB.Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra.FEMS Microbiol Lett1993;114:115-9

[17] Chun J,Ward AC.Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains.FEMS Microbiol Lett1993;107:321-6

[18] Timmins EM.Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks.J Appl Microbiol1997;83:208-18

[19] Goodacre R.Use of pyrolysis mass spectrometry with supervised learning for the assessment of the adulteration of milk of different species.Appl Spectrosc1997;51:1144-53

[20] Vallejo-cordoba B,Nakai S.Predicting milk shelf-life based on artificial neural networks and headspace gas chromatographic data.J Food Sci1995;60:885-8

[21] Horimoto Y,Nakai S.Classification of microbial defects in milk using a dynamic headspace gas chromatograph and computer-aided data processing. 2. Artificial neural networks, partial least-squares regression analysis, and principal component regression analysis.J Agric Food Chem1997;45:743-7

[22] Yazdanbakhsh A,Sharma H,Esmaeilzadeh H.Neural acceleration for GPU throughput processors. In: The 48th Annual IEEE/ACM International Symposium of Microarchitecture; 2015 Dec 05-09; Waikiki, Hawaii. Association for Computing Machinery; 2015. pp. 482-93.

[23] Tan T.Efficient execution of deep neural networks on mobile devices with NPU. In: Proceedings of the 20th International Conference on Information Processing in Sensor Networks (Co-Located with CPS-IoT Week 2021); 2021 May 18-21; Nashville, USA. Association for Computing Machinery; 2021. pp. 283-98.

[24] She X,Mukhopadhyay S.Improving robustness of ReRAM-based spiking neural network accelerator with stochastic spike-timing-dependent-plasticity. In: 2019 International Joint Conference on Neural Networks (IJCNN); 2019 Jul 14-19; Budapest, Hungary. IEEE; 2019. p. 1-8.

[25] Schuman CD,Parsa M,Date P.Opportunities for neuromorphic computing algorithms and applications.Nat Comput Sci2022;2:10-9

[26] Rajendran B,Schmuker M,Eleftheriou E.Low-power neuromorphic hardware for signal processing applications: a review of architectural and system-level design approaches.IEEE Signal Process Mag2019;36:97-110

[27] Young AR,Plank JS.A review of spiking neuromorphic hardware communication systems.IEEE Access2019;7:135606-20

[28] Wan Q,Erickson JR,Xiong F.Emerging artificial synaptic devices for neuromorphic computing.Adv Mater Technol2019;4:1900037

[29] Kornijcuk V.Recent progress in real-time adaptable digital neuromorphic hardware.Adv Intell Syst2019;1:1900030

[30] Geng Z,Yan Y.A multiplexer system for multi-channel charge signal processing in in-shoe force measurement. In: 2011 IEEE International Instrumentation and Measurement Technology Conference; 2011 May 10-12; Hangzhou, China. IEEE; 2011. p. 1-5.

[31] Davies M,Orchard G.Advancing neuromorphic computing with loihi: a survey of results and outlook.Proc IEEE2021;109:911-34

[32] del Valle J, Ramírez JG, Rozenberg MJ, Schuller IK. Challenges in materials and devices for resistive-switching-based neuromorphic computing.J Appl Phys2018;124:211101

[33] Zou X,Chen X,Han Y.Breaking the von Neumann bottleneck: architecture-level processing-in-memory technology.Sci China Inf Sci2021;64:3227

[34] Ma J.A review for dynamics in neuron and neuronal network.Nonlinear Dyn2017;89:1569-78

[35] Burkitt AN.A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input.Biol Cybern2006;95:1-19

[36] Long L.A review of biologically plausible neuron models for spiking neural networks. In: AIAA Infotech@Aerospace 2010; 2010 Apr 20-22; Atlanta, Georgia. American Institute of Aeronautics and Astronautics; 2010.

[37] Yamazaki K,Bulsara D.Spiking neural networks and their applications: a review.Brain Sci2022;12:863 PMCID:PMC9313413

[38] Lansky P.A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models.Biol Cybern2008;99:253-62

[39] Lee K,Han K.Deep learning entrusted to fog nodes (DLEFN) based smart agriculture.Appl Sci2020;10:1544

[40] Yang R,Guo X.Memristive synapses and neurons for bioinspired computing.Adv Elect Mater2019;5:1900287

[41] Burr GW,Sebastian A.Neuromorphic computing using non-volatile memory.Adv Phys X2017;2:89-124

[42] Upadhyay NK,Wang Z,Xia Q.Emerging memory devices for neuromorphic computing.Adv Mater Technol2019;4:1800589

[43] Shama F,Imani MA.FPGA realization of hodgkin-huxley neuronal model.IEEE Trans Neural Syst Rehabil Eng2020;28:1059-68

[44] Liu Y,Qian Y.Implementation of Hodgkin-Huxley neuron model with the novel memristive oscillator.IEEE Trans Circuits Syst II2021;68:2982-6

[45] Corinto F,Sung-Mo SK.Memristor-based neural circuits. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS); 2013 May 19-23; Beijing, China. IEEE; 2013. pp. 417-20.

[46] Fang X,Wang L.Memristive Hodgkin-Huxley spiking neuron model for reproducing neuron behaviors.Front Neurosci2021;15:730566 PMCID:PMC8496503

[47] Xu Y,Li Z,Miao X.Adaptive Hodgkin–Huxley neuron for retina-inspired perception.Adv Intell Syst2022;4:2200210

[48] Yang Z,Huang Y.55nm CMOS analog circuit implementation of LIF and STDP functions for low-power SNNs. In: 2021 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED); 2021 Jul 26-28; Boston, USA. IEEE; 2021. p. 1-6.

[49] Koshino S,Matsui C.Compact CiM co-optimized by heterogeneous multi-level ReRAM & random weight SNN for event-based vision sensor of edge AI. In: 2022 IEEE Silicon Nanoelectronics Workshop (SNW); 2022 Jun 11-12; Honolulu, USA. IEEE; 2022. p. 1-2.

[50] Luo J,Liu T.Capacitor-less stochastic leaky-FeFET neuron of both excitatory and inhibitory connections for SNN with reduced hardware cost. In: 2019 IEEE International Electron Devices Meeting (IEDM); 2019 Dec 07-11; San Francisco, USA. IEEE; 2019. pp. 6.4.1-6.4.4.

[51] Uleru GI.Influence of capacitor variability on the electronic spiking neurons. In: 2021 25th International Conference on System Theory, Control and Computing (ICSTCC); 2021 Oct 20-23; Iasi, Romania. IEEE; 2021. pp. 255-9.

[52] Radamson HH,Wu Z.State of the art and future perspectives in advanced CMOS technology.Nanomaterials2020;10:1555 PMCID:PMC7466708

[53] Rahiminejad E,Parvizi-Fard A,Linares-Barranco B.A neuromorphic CMOS circuit with self-repairing capability.IEEE Trans Neural Netw Learn Syst2022;33:2246-58

[54] Basu A,Karnik T.Low-power, adaptive neuromorphic systems: recent progress and future directions.IEEE J Emerg Sel Topics Circuits Syst2018;8:6-27

[55] Saxena V,Zhu K.Energy-efficient CMOS memristive synapses for mixed-signal neuromorphic system-on-a-chip. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS); 2018 May 27-30; Florence, Italy. IEEE; 2018. p. 1-5.

[56] Iakymchuk T,Guerrero-martínez JF,Francés-víllora JV.Simplified spiking neural network architecture and STDP learning algorithm applied to image classification.J Image Video Proc2015;2015:59

[57] Serrano-Gotarredona T,Prodromakis T,Linares-Barranco B.STDP and STDP variations with memristors for spiking neuromorphic learning systems.Front Neurosci2013;7:2 PMCID:PMC3575074

[58] Debanne D.Spike timing-dependent plasticity and memory.Curr Opin Neurobiol2023;80:102707

[59] Wang R,Zhang X.Bipolar analog memristors as artificial synapses for neuromorphic computing.Materials2018;11:2102 PMCID:PMC6266336

[60] Indiveri G,Douglas R.A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.IEEE Trans Neural Netw2006;17:211-21

[61] Wang R,Mao J.Recent advances of volatile memristors: devices, mechanisms, and applications.Adv Intell Syst2020;2:2000055

[62] Corinto F,Chua LO.A theoretical approach to memristor devices.IEEE J Emerg Sel Topics Circuits Syst2015;5:123-32

[63] Woo J,Song J.Improved synaptic behavior under identical Pulses using AlO_x/HfO₂ bilayer RRAM array for neuromorphic systems.IEEE Electron Device Lett2016;37:994-7

[64] Li Z,Hu Z.Imaging metal-like monoclinic phase stabilized by surface coordination effect in vanadium dioxide nanobeam.Nat Commun2017;8:15561 PMCID:PMC5474733

[65] Wang Z,Burr GW.Resistive switching materials for information processing.Nat Rev Mater2020;5:173-95

[66] Xu Z,Wang W,Golberg D.Real-time in situ HRTEM-resolved resistance switching of Ag₂S nanoscale ionic conductor.ACS Nano2010;4:2515-22

[67] Yuan F,Liu C.Real-time observation of the electrode-size-dependent evolution dynamics of the conducting filaments in a SiO₂ layer.ACS Nano2017;11:4097-104

[68] Yang Y,Gaba S,Pan X.Observation of conducting filament growth in nanoscale resistive memories.Nat Commun2012;3:732

[69] Tian X,Zeng M.Bipolar electrochemical mechanism for mass transfer in nanoionic resistive memories.Adv Mater2014;26:3649-54

[70] Yuan R,Cai L.A neuromorphic physiological signal processing system based on VO₂ memristor for next-generation human-machine interface.Nat Commun2023;14:3695 PMCID:PMC10284901

[71] Chen W,Wang S.Essential characteristics of memristors for neuromorphic computing.Adv Elect Mater2023;9:2200833

[72] Sun W,Chi M.Understanding memristive switching via in situ characterization and device modeling.Nat Commun2019;10:3453 PMCID:PMC6672015

[73] Seok JY,Yoon JH.A review of three-dimensional resistive switching cross-bar array memories from the integration and materials property points of view.Adv Funct Mater2014;24:5316-39

[74] Feng X,Wong SL.Self-selective multi-terminal memtransistor crossbar array for in-memory computing.ACS Nano2021;15:1764-74

[75] Li Y.Hardware implementation of neuromorphic computing using large-scale memristor crossbar arrays.Adv Intell Syst2021;3:2000137

[76] Kuncic Z.Neuromorphic nanowire networks: principles, progress and future prospects for neuro-inspired information processing.Adv Phys X2021;6:1894234

[77] Lee YK,Park ES.Matrix mapping on crossbar memory arrays with resistive interconnects and its use in in-memory compression of biosignals.Micromachines2019;10:306 PMCID:PMC6562796

[78] Li J,Li Y.Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing.Sci Adv2023;9:eadf7474 PMCID:PMC10284536

[79] Woods W.Approximate vector matrix multiplication implementations for neuromorphic applications using memristive crossbars. In: 2017 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH); 2017 Jul 25-26; Newport, USA. IEEE; 2017. pp. 103-8.

[80] Bao H,Li J.Toward memristive in-memory computing: principles and applications.Front Optoelectron2022;15:23 PMCID:PMC9756267

[81] Bian J,Wang Z.A stacked memristive device enabling both analog and threshold switching behaviors for artificial leaky integrate and fire neuron.IEEE Electron Device Lett2022;43:1436-9

[82] Yang Z,Yuan R.Seizure detection using dynamic memristor-based reservoir computing and leaky integrate-and-fire neuron for post-processing.APL Mach Learn2023;1:046123

[83] Moon J,Shin JH.Temporal data classification and forecasting using a memristor-based reservoir computing system.Nat Electron2019;2:480-7

[84] Du C,Zidan MA,Lee SH.Reservoir computing using dynamic memristors for temporal information processing.Nat Commun2017;8:2204 PMCID:PMC5736649

[85] Zhong Y,Li X,Qian H.Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing.Nat Commun2021;12:408 PMCID:PMC7814066

[86] Liu Z,Gao B.Multichannel parallel processing of neural signals in memristor arrays.Sci Adv2020;6:eabc4797 PMCID:PMC7546699

[87] Abbas H,Truong SN.A memristor crossbar array of titanium oxide for non-volatile memory and neuromorphic applications.Semicond Sci Technol2017;32:065014

[88] Fernando BR,Yakopcic C.3D memristor crossbar architecture for a multicore neuromorphic system. In: 2020 International Joint Conference on Neural Networks (IJCNN); 2020 Jul 19-24; Glasgow, UK. IEEE; 2020. p. 1-8.

[89] Ismail M,Mahata C,Kim S.Demonstration of synaptic and resistive switching characteristics in W/TiO₂/HfO₂/TaN memristor crossbar array for bioinspired neuromorphic computing.J Mater Sci Technol2022;96:94-102

[90] Balakrishna Pillai P, De Souza MM. Nanoionics-based three-terminal synaptic device using zinc oxide.ACS Appl Mater Interfaces2017;9:1609-18

[91] Lenz J,Geisenhof FR,Weitz RT.Vertical, electrolyte-gated organic transistors show continuous operation in the MA cm^-2 regime and artificial synaptic behaviour.Nat Nanotechnol2019;14:579-85

[92] Lee H,Kim SJ.Three-terminal ovonic threshold switch (3T-OTS) with tunable threshold voltage for versatile artificial sensory neurons.Nano Lett2022;22:733-9

[93] Cohen MH,Ovshinsky SR.Simple band model for amorphous semiconducting alloys.Phys Rev Lett1969;22:1065-8

[94] Street RA.States in the gap in glassy semiconductors.Phys Rev Lett1975;35:1293-6

[95] Nakano M,Okuyama D.Collective bulk carrier delocalization driven by electrostatic surface charge accumulation.Nature2012;487:459-62

[96] Sarkar T,Pavlou A.An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing.Nat Electron2022;5:774-83

[97] Parr ZS,Paulsen BD.Semiconducting small molecules as active materials for p-type accumulation mode organic electrochemical transistors.Adv Elect Mater2020;6:2000215

[98] Hidalgo Castillo TC,Cendra C.Simultaneous performance and stability improvement of a p-type organic electrochemical transistor through additives.Chem Mater2022;34:6723-33

[99] Sun H,Berggren M.n-Type organic electrochemical transistors: materials and challenges.J Mater Chem C2018;6:11778-84

[100] Giovannitti A,Sbircea DT.N-type organic electrochemical transistors with stability in water.Nat Commun2016;7:13066 PMCID:PMC5059848

[101] Liu L,Ni Y.Stretchable neuromorphic transistor that combines multisensing and information processing for epidermal gesture recognition.ACS Nano2022;16:2282-91

[102] Imai J.Regulation of systemic metabolism by the autonomic nervous system consisting of afferent and efferent innervation.Int Immunol2022;34:67-79

[103] Johansson RS.Coding and use of tactile signals from the fingertips in object manipulation tasks.Nat Rev Neurosci2009;10:345-59

[104] Wang W,Zhong D.Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin.Science2023;380:735-42

[105] Liu F,Christou A,Kaboli M.Neuro-inspired electronic skin for robots.Sci Robot2022;7:eabl7344

[106] Nature Reviews Methods Primers. Electrolyte-gated transistors for enhanced performance bioelectronics.Nat Rev Method Prime2021;1:65

[107] Huang Y,Chen S.Emerging technologies of flexible pressure sensors: materials, modeling, devices, and manufacturing.Adv Funct Mater2019;29:1808509

[108] Kim Y,Xu W.A bioinspired flexible organic artificial afferent nerve.Science2018;360:998-1003

[109] Yao X,Jin W,Cui Y.Carbon nanotube field-effect transistor-based chemical and biological sensors.Sensors2021;21:995 PMCID:PMC7867273

[110] Kim S,Kim H.A tactile sensor system with sensory neurons and a perceptual synaptic network based on semivolatile carbon nanotube transistors.NPG Asia Mater2020;12:76

[111] Lee Y,Seo DG.A low-power stretchable neuromorphic nerve with proprioceptive feedback.Nat Biomed Eng2023;7:511-9

[112] He Y,Zhu Y.Recent progress on emerging transistor-based neuromorphic devices.Adv Intell Syst2021;3:2000210

[113] Wang R,Zhu X.Carbon nanotube-based strain sensors: structures, fabrication, and applications.Adv Mater Technol2023;8:2200855

[114] Ahuja P,Ujjain SK.A water-resilient carbon nanotube based strain sensor for monitoring structural integrity.J Mater Chem A2019;7:19996-20005

[115] Karimov KS,Chani MTS.Carbon nanotubes based strain sensors.Measurement2012;45:918-21

[116] Kang SK,Hwang SW.Bioresorbable silicon electronic sensors for the brain.Nature2016;530:71-6

[117] Kim DH,Amsden JJ.Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.Nat Mater2010;9:511-7 PMCID:PMC3034223

[118] Song E,Jin X.Ultrathin trilayer assemblies as long-lived barriers against water and ion penetration in flexible bioelectronic systems.ACS Nano2018;12:10317-26

[119] Song E,Li R.Transferred, ultrathin oxide bilayers as biofluid barriers for flexible electronic implants.Adv Funct Mater2018;28:1702284

[120] Lee YK,Song E.Dissolution of monocrystalline silicon nanomembranes and their use as encapsulation layers and electrical interfaces in water-soluble electronics.ACS Nano2017;11:12562-72 PMCID:PMC5830089

[121] Kang SK,Lee YK.Advanced materials and devices for bioresorbable electronics.Acc Chem Res2018;51:988-98

[122] Shin J,Bai W.Bioresorbable pressure sensors protected with thermally grown silicon dioxide for the monitoring of chronic diseases and healing processes.Nat Biomed Eng2019;3:37-46

[123] Boukhvalov DW,Vorokhta M.Revisiting the chemical stability of germanium selenide (GeSe) and the origin of its photocatalytic efficiency.Adv Funct Mater2021;31:2106228