[235] Hamerly R *et al* 2019 Experimental investigation of performance differences between coherent Ising machines and a quantum annealer *Sci. Adv.* **5** eaau0823

[236] Aramon M, Rosenberg G, Valiante E, Miyazawa T, Tamura H and Katzgraber H G 2019 Physics-inspired optimization for quadratic unconstrained problems using a digital annealer *Front. Phys.* **7** 48

[237] 2018 Fujitsu quantum-inspired digital annealer cloud service to rapidly resolve combinatorial optimization problems-fujitsu global https://fujitsu.com/global/about/resources/news/press-releases/2018/0515-01.html (accessed 05 February 2021)

[238] Takemoto T, Hayashi M, Yoshimura C and Yamaoka M 2020 A 2 × 30 k-spin multi-chip scalable CMOS annealing processor based on a processing-in-memory approach for solving large-scale combinatorial optimization problems *IEEE J. Solid-State Circuits* **55** 145–56

[239] Mahmoodi M R, Prezioso M and Strukov D B 2019 Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization *Nat. Commun.* **10** 5113

[240] Cai F *et al* 2020 Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks *Nat. Electron.* **3** 409–18

[241] Chou J, Bramhavar S and Ghosh S (W H-S Reports, and Undefined) 2019 Analog coupled oscillator based weighted Ising machine nature.com available: https://nature.com/articles/s41598-019-49699-5 (accessed: 06 February 2021)

[242] Dutta S, Khanna A, Gomez J, Ni K, Toroczkai Z and Datta S 2019 Experimental demonstration of phase transition nano-oscillator based ising machine 2019 IEEE Int. Electron Devices Meeting (IEDM) pp 37–8

[243] Xiao T 2019 *Optoelectronics for Refrigeration and Analog Circuits for Combinatorial Optimization* (UC Berkeley)

[244] Camsari K Y, Sutton B M and Datta S 2019 p-bits for probabilistic spin logic *Appl. Phys. Rev.* **6** 011305

[245] Abbink E J W, Albino L, Dollevoet T, Huisman D, Roussado J and Saldanha R L 2011 *Solving Large Scale Crew Scheduling Problems in Practice* vol 3 (Berlin: Springer) pp 149–64

[246] Hamerly R *et al* 2018 Scaling advantages of all-to-all connectivity in physical annealers: the coherent Ising machine vs D-wave 2000Q

[247] Kalehbasti R, Ushijima-Mwesigwa H, Mandal A and Ghosh I 2020 Ising-based louvain method: clustering large graphs with specialized hardware (arXiv:2012.11391) (accessed: 05 February 2021)

[248] Strubell E, Ganesh A and Mccallum A 2019 Energy and policy considerations for deep learning in NLP *Proc. 57th Annual Meeting of the Association for Computational Linguistics (ACL)* 3645–50

[249] Rumelhart D E, Hinton G E and Williams R J 1986 Learning representations by back-propagating errors *Nature* **323** 533–6

[250] Hsieh E R *et al* 2019 High-density multiple bits-per-cell 1T4R RRAM array with gradual SET/RESET and its effectiveness for deep learning *Proc. IEEE Int. Electron Devices Meeting (IEDM)*

[251] Esmanhotto E *et al* 2020 High-density 3D monolithically integrated multiple 1T1R multi-level-cell for neural networks *Proc. IEEE Int. Electron Devices Meeting (IEDM)*

[252] Barraud S *et al* 2020 3D RRAMs with gate-all-around stacked nanosheet transistors for in-memory-computing *Proc. IEEE Int. Electron Devices Meeting (IEDM)*

[253] Alfaro Robayo D *et al* 2019 Integration of OTS based back-end selector with HfO₂ OxRAM for crossbar arrays *Proc. IEEE Int. Electron Devices Meeting (IEDM)*

[254] Le B Q, Grossi A, Vianello E, Wu T, Lama G, Beigne E, Wong H-S P and Mitra S 2019 Resistive RAM with multiple bits per cell: array-level demonstration of 3 bits per cell *IEEE Trans. Electron Devices* **66** 641–6

[255] Valentian A *et al* 2019 Fully integrated spiking neural network with analog neurons and RRAM synapses *Proc. IEEE Int. Electron Devices Meeting (IEDM)*

[256] Ambrogio S *et al* 2018 Equivalent-accuracy accelerated neural-network training using analogue memory *Nature* **558** 60–7

[257] Payvand M and Indiveri G 2019 Spike-based plasticity circuits for alwayson on-line learning in neuromorphic systems *IEEE Int. Symp. Circuits and Systems (ISCAS)*

[258] Ly D R B *et al* 2018 Role of synaptic variability in resistive memory-based spiking neural networks with unsupervised learning *J. Phys. D: Appl. Phys.* **51** 444002

[259] Gerstner W, Lehmann M, Liakoni V, Corneil D and Brea J 2018 Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules *Front. Neural Circuits* **12** 53

[260] Bellec G, Scherr F, Subramoney A, Hajek E, Salaj D, Legenstein R and Maass W 2020 A solution to the learning dilemma for recurrent networks of spiking neurons *Nat. Commun.* **11** 3625

[261] Crafton B, Parihar A, Gebhardt E and Raychowdhury A 2019 Direct feedback alignment with sparse connections for local learning *Front. Neurosci.* **13** 525

[262] Ernoult M *et al* 2019 Updates of equilibrium prop match gradients of backprop through time in an RNN with static input *NeurIPS 2019 Proc.* (arXiv:1905.13633)

[263] Demirag Y *et al* 2021 PCM-trace: scalable synaptic eligibility traces with resistivity drift of phase-change materials *Proc. IEEE Int. Symp. Circuits and Systems (ISCAS)* (to be published)

[264] Dalgaty T, Castellani N, Turck C, Harabi K-E, Querlioz D and Vianello E 2021 *In situ* learning using intrinsic memristor variability via Markov chain Monte Carlo sampling *Nat. Electron.* **4** 151–61

[265] Vivet P *et al* 2020 A 220GOPS 96-core processor with 6 chiplets 3D-stacked on an active interposer offering 0.6 ns mm⁻¹ latency, 3-Tb/s/mm² inter-chiplet interconnects and 156 mW mm⁻² @ 82%-peakefficiency DC–DC converters *Proc. IEEE Int. Solid-State Circuits Conf. (ISSCC)* 46–8

[266] Ambs P 2010 Optical computing: a 60 year adventure *Adv. Opt. Technol.* **2010** 372652

[267] Nahmias M A, De Lima T F, Tait A N, Peng H-T, Shastri B J and Prucnal P R 2020 Photonic multiply-accumulate operations for neural networks *IEEE J. Sel. Top. Quantum Electron.* **26** 1–18

[268] Lin X, Rivenson Y, Yardimci N T, Veli M, Luo Y, Jarrahi M and Ozcan A 2018 All-optical machine learning using diffractive deep neural networks *Science* **361** 1004–8

[269] Shastri B J *et al* 2020 Photonics for artificial intelligence and neuromorphic computing *Nat. Photon.* **15** 102–14

[270] Shen Y *et al* 2017 Deep learning with coherent nanophotonic circuits *Nat. Photon.* **11** 441–6

[271] Tanaka G *et al* 2019 Recent advances in physical reservoir computing: a review *Neural Netw.* **115** 100–23

[272] Feldmann J, Youngblood N, Wright C D, Bhaskaran H and Pernice W H P 2019 All-optical spiking neurosynaptic networks with self-learning capabilities *Nature* **569** 208–14

[273] Cheng Z, Ríos C, Pernice W H, Wright C D and Bhaskaran H 2017 On-chip photonic synapse *Sci. Adv.* **3** e1700160

[274] Wu C, Yu H, Lee S, Peng R, Takeuchi I and Li M 2021 Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network *Nat. Commun.* **12** 96
[275] Xu X et al 2021 11 TOPS photonic convolutional accelerator for optical neural networks Nature 589 44–51

[276] Sebastian A et al 2017 Temporal correlation detection using computational phase-change memory Nat. Commun. 8 1115

[277] Rios C et al 2019 In-memory computing on a photonic platform Sci. Adv. 5 eaau5759

[278] Murmann B 2015 The race for the extra decibel: a brief review of current ADC performance trajectories IEEE Solid-State Circuits Mag. 7 58–66

[279] Marin-Palomo P et al 2017 Microresonator-based solitons for massively parallel coherent optical communications Nature 546 274–9

[280] Huang C, de Lima T F, Jha A, Abbaslou S, Shastri B J and Prucnal R 2019 Giant enhancement in signal contrast using integrated all-optical nonlinear thresholder Optics InfoBase Conf. Papers

[281] Wuttig M, Bhaskaran H and Taubner T 2017 Phase-change materials for non-volatile photonic applications Nat. Photon. 11 465–76

[282] Pernice W H and Bhaskaran H 2012 Photonic non-volatile memories using phase change materials Appl. Phys. Lett. 101 171101

[283] Reshef O, De Leon I, Alam M Z and Boyd R W 2019 Nonlinear optical effects in epsilon-near-zero media Nat. Rev. Mater. 4 535–51

[284] Gupta S, Agrawal A, Gopalakrishnan K and Narayanan P 2015 Deep learning with limited numerical precision 32nd Int. Conf. Machine Learning (ICML 2015)

[285] Schemmel J, Brüderle D, Grübl A, Hock M, Meier K and Millner S 2021 A wafer-scale neuromorphic hardware system for large-scale neural modelling Proc. Int. Symp. Circuits System 1947–50

[286] Davison A P, Brüderle D, Eppler J M, Kremkow J, Muller E, Pecevski D A, Perrinet L and Yger P 2009 PyNN: a common interface for neuronal network simulators Front. Neuroinform. 2 11

[287] Knight J C and Nowotny T 2018 GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model Front. Neurosci. 12 941

[288] van Albada S J, Rowley A G, Senk J, Hopkins M, Schmidt M, Stokes A B, Lester D R, Diesmann M and Furber S B 2018 Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model Front. Neurosci. 12 291

[289] Rhodes O, Peres L, Rowley A G D, Gait A, Plana L A, Brenninkmeijer C and Furber S B 2020 Realtime cortical simulation on neuromorphic hardware Phil. Trans. R. Soc. A 378 20190160

[290] Blouw P, Choo X, Hunsberger E and Eliasmith C 2019 Benchmarking keyword spotting efficiency on neuromorphic hardware Proc. NICE'19

[291] Arthur J and Boahen K 2006 Learning in silicon: timing is everything Advances in Neural Information Processing Systems 18 ed Y Weiss, B Schölkopf and J Platt (Cambridge, MA: MIT Press)

[292] Baldi P, Sadowski P and Lu Z 2017 Learning in the machine: the symmetries of the deep learning channel Neural Netw. 95 110–33

[293] Baydin A G, Pearlmutter B A, Radul A A and Siskind J M 2017 Automatic differentiation in machine learning: a survey J. Mach. Learn. Res. 18 5595–637

[294] Bellec G, Scherr F, Hajek E, Salaj D, Legenstein R and Maass W 2019 Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets (arXiv:1901.09049)

[295] Davies M, Srinivasa N, Lin T H, Chinya G, Joshi P, Lines A, Wild A and Wang H 2018 Loihi: a neuromorphic manycore processor with on-chip learning IEEE Micro 38 82–99

[296] Yigit D, Moro F, Dalgaty T, Navarro G, Frenkel C, Indiveri G, Vianello E and Payvand M 2021 PCM-trace: scalable synaptic eligibility traces with resistivity drift of phase-change materials (arXiv:2102.07260)

[297] Ercsey-Ravasz M, Markov N T, Lamy C, Van Essen D C, Knoblauch K, Toroczkai Z and Kennedy H 2013 A predictive network model of cerebral cortical connectivity based on a distance rule Neuron 80 184–97

[298] Esser S K et al 2016 Convolutional networks for fast, energy-efficient neuromorphic computing Proc. Natl Acad. Sci. USA 113 11441–6

[299] Fouda M E, Kurdahi F, Eltawil A and Neftci E 2019 Spiking Neural Networks for Inference and Learning: A Memristor-Based Design Perspective (Elsevier) ch 19 (accepted)

[300] Simon F, Schemmel J, Grubl A, Hartel A, Hock M and Meier K 2017 Demonstrating hybrid learning in a flexible neuromorphic hardware system IEEE Trans. Biomed. Circuits Syst. 11 128–42

[301] Galluppi F, Lagorce X, Stromatias E, Pfeiffer M, Plana L A, Furber S B and Benosman R B 2014 A framework for plasticity implementation on the spinnaker neural architecture Front. Neurosci. 8 429

[302] Jaderberg M, Czarnecki W M, Osindero S, Vinyals O, Graves A and Kavukcuoglu K 2016 Decoupled neural interfaces using synthetic gradients (arXiv:1608.05343)

[303] Jia Z, Tillman B, Maggioni M and Scarpazza D P 2019 Dissecting the graphcore IPU architecture via microbenchmarking (arXiv:1912.03413)

[304] Kaiser J, Mostafa H and Neftci E 2019 Synaptic plasticity for deep continuous local learning 14 424

[305] Kumaran D, Hassabis D and McClelland J L 2016 What learning systems do intelligent agents need? Complementary learning systems theory updated Trends Cognit. Sci. 20 512–34

[306] Lillicrap T P, Santoro A, Marris L, Akerman C J and Hinton G 2020 Backpropagation and the brain Nat. Rev. Neurosci. 21 335–46

[307] Neftci E O 2018 Data and power efficient intelligence with neuromorphic learning machines iScience 5 52–68

[308] Payvand M, Fouda M E, Kurdahi F, Eltawil A and Neftci E O 2020 Error-triggered threefactor learning dynamics for crossbar arrays 2020 2nd IEEE Int. Conf. Artificial Intelligence Circuits and Systems (AICAS) 218–22

[309] Pedroni B U, Joshi S, Deiss S R, Sheik S, Detorakis G, Paul S, Augustine C, Neftci E O and Cauwenberghs G 2019 Memory-efficient synaptic connectivity for spike-timing- dependent plasticity Front. Neurosci. 13 357

[310] Pfeil T, Potjans T C, Schrader S, Potjans W, Schemmel J, Diesmann M and Meier K 2012 Is a 4 bit synaptic weight resolution enough? Constraints on enabling spike-timing dependent plasticity in neuromorphic hardware Front. Neurosci. 6 90

[311] Prezioso M, Mahmoodi M R, Merrikh Bayat F, Nili H, Kim H, Vincent A and Strukov D B 2018 Spike-timing dependent plasticity learning of coincidence detection with passively integrated memristive circuits Nat. Commun. 9 5311

[312] Rastegari M, Ordonez V, Redmon J and Farhadi A 2016 Xnor-net: imagenet classification using binary convolutional neural networks European Conf. Computer Vision (Berlin: Springer) 525–42

[313] Richards B A et al 2019 A deep learning framework for neuroscience Nat. Neurosci. 22 1761–70

[314] Rueckauer B, Bybee C, Goettsche R, Singh Y, Mishra J and Wild A 2021 NxTF: an API and compiler for deep spiking neural networks on intel loihi (arXiv:2101.04261)
[315] Shrestha S B and Orchard G 2018 SLAYER: spike layer error reassignment in time *Advances in Neural Information Processing Systems* 1412–21

[316] Spilger P et al 2020 hxtorch: PyTorch for brainscales-2 IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning (Berlin: Springer) pp 189–200

[317] Stewart K, Orchard G, Shrestha S B and Neftci E 2020 Online few-shot gesture learning on a neuromorphic processor *IEEE J. Emerg. Sel. Top. Circuits Syst.* 10 512–21

[318] Thiele J C, Bichler O and Dupret A 2019 SpikeGrad: an ANN-equivalent computation model for implementing backpropagation with spikes (arXiv:1906.00851)

[319] Wang Z et al 2018 Fully memristive neural networks for pattern classification with unsupervised learning *Nat. Electron.* 1 137

[320] Zenke F and Gerstner W 2014 Limits to high-speed simulations of spiking neural networks using general-purpose computers *Front. Neuroinf.* 8 76

[321] Zenke F and Neftci E O 2021 Brain-inspired learning on neuromorphic substrates *Proc. IEEE* 109 935–50

[322] Zenke F and Ganguli S 2017 Superspike: supervised learning in multi-layer spiking neural networks (arXiv:1705.11146)

[323] Lake B M, Ullman T D, Tenenbaum J B and Gershman S J 2017 Building machines that learn and think like people *Behav. Brain Sci.* 40 e253

[324] Murray J M 2019 Local online learning in recurrent networks with random feedback *eLife* 8 e43299

[325] Scherr F, Stöckl C and Maass W 2020 One-shot learning with spiking neural networks (bioRxiv)

[326] Hochreiter S, Younger A S and Conwell R 2020 Learning to learn using gradient descent *Int. Conf. Artificial Neural Networks* (Berlin: Springer) pp 87–94

[327] Confavreux B, Zenke F, Agnes E J, Lillicrap T and Vogels T 2020 A meta-learning approach to (re) discover plasticity rules that carve a desired function into a neural network (bioRxiv)

[328] Jordan J, Schmidt M, Senn W and Petrovici M A 2020 Evolving to learn: discovering interpretable plasticity rules for spiking networks (arXiv:2005.14149)

[329] Bohnstingl T, Scherr F, Pehle C, Meier K and Maass W 2019 Neuromorphic hardware learns to learn *Front. Neurosci.* 13 483

[330] Wang J X, Kurth-Nelson Z, Tirumala D, Soyer H, Leibo J Z, Munos R and Botvinick M 2016 Learning to reinforcement learn (arXiv:1611.05763)

[331] Duan Y, Schulman J, Chen X, Bartlett L, Sutskever I and Abbeel P 2016 Rl²: fast reinforcement learning via slow reinforcement learning (arXiv:1611.02779)

[332] Wang J X, Kurth-Nelson Z, Kumaran D, Tirumala D, Soyer H, Leibo J Z, Hassabis D and Botvinick M 2018 Prefrontal cortex as a meta-reinforcement learning system *Nat. Neurosci.* 21 860–8

[333] Bellec G, Salaj D, Subramoney A, Legenstein R and Maass W 2018 Long short-term memory and learning-to-learn in networks of spiking neurons *Advances in Neural Information Processing Systems* 787–97

[334] Subramoney A, Bellec G, Scherr F, Legenstein R and Maass W 2020 Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks (bioRxiv)

[335] Subramoney A, Scherr F and Maass W 2019 Reservoirs learn to learn (arXiv:1909.07486)

[336] Finn C, Abbeel P and Levine S 2017 Model-agnostic meta-learning for fast adaptation of deep networks *Proc. 34th Int. Conf. Machine Learning (PMLR)* vol 70 pp 1126–35

[337] Furber S B, Lester D R, Plana L A, Garside J D, Painkras E, Temple S and Brown A D 2012 Overview of the spinnaker system architecture *IEEE Trans. Comput.* 62 2454–67

[338] Salimans T, Ho J, Chen X, Sidor S and Sutskever I 2017 Evolution strategies as a scalable alternative to reinforcement learning (arXiv:1703.03864)

[339] Grübl A, Billaudelle S, Cramer B, Karasenko V and Schemmel J 2020 Verification and design methods for the brainscales neuromorphic hardware system *J. Signal Process. Syst.* 92 1277–92

[340] Sejnowski T J, Koch C and Churchland P S 1988 Computational neuroscience *Science* 241 1299–306

[341] Abbott L F 2008 Theoretical neuroscience rising *Neuron* 60 489–95

[342] Hodgkin A L and Huxley A F 1952 A quantitative description of membrane current and its application to conduction and excitation in nerve *J. Physiol.* 117 500–44

[343] Hopfield J J 1988 Artificial neural networks *IEEE Circuits Dev. Mag.* 4 3–10

[344] Schwartz E L 1993 *Computational Neuroscience* (Cambridge, MA: MIT Press)

[345] Thiele A and Bellgrove M A 2018 Neuromodulation of attention *Neuron* 97 769–85

[346] Baddeley A 1992 Working memory *Science* 255 556–9

[347] Hollerman J R and Schultz W 1998 Dopamine neurons report an error in the temporal prediction of reward during learning *Nat. Neurosci.* 1 304–9

[348] Markram H et al 2015 Reconstruction and simulation of neocortical microcircuitry *Cell* 163 456–92

[349] Koch C and Reid R C 2012 Observatories of the mind *Nature* 483 397–8

[350] Koroshetz W et al 2018 The state of the NIH BRAIN initiative *J. Neurosci.* 38 6427–38

[351] Amunts K, Ebell C, Muller J, Telefont M, Knoll A and Lippert T 2016 The human brain project: creating a European Research infrastructure to decode the human brain *Neuron* 92 574–81

[352] Adams A et al 2020 International brain initiative: an innovative framework for coordinated global brain Research efforts *Neuron* 105 212–6

[353] Okano H, Miyawaki A and Kasai K 2015 Brain/MINDS: brain-mapping project in Japan *Phil. Trans. R. Soc. B* 370 20140310

[354] Davison A P 2012 Collaborative modelling: the future of computational neuroscience? *Netw. Comput. Neural Syst.* 23 157–66

[355] Ramaswamy S et al 2015 The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex *Front. Neural Circuits* 9 44

[356] Markram H 2013 Seven challenges for neuroscience *Funct. Neurol.* 28 145–51

[357] Suárez L E, Richards B A, Lajoie G and Misic B 2021 Learning function from structure in neuromorphic networks *Nat. Mach. Intell.* 3 771–86

[358] Tsodyks M 2008 Computational neuroscience grand challenges—a humble attempt at future forecast *Front. Neurosci.* 2 21

[359] Hasler J and Marr B 2013 Finding a roadmap to achieve large neuromorphic hardware systems *Front. Neurosci.* 7 118

[360] Niven J E and Laughlin S B 2008 Energy limitation as a selective pressure on the evolution of sensory systems *J. Exp. Biol.* 211 1792–804

[361] Branco T and Staras K 2009 The probability of neurotransmitter release: variability and feedback control at single synapses *Nat. Rev. Neurosci.* 10 373–83
[362] Hamilton T J, Afshar S, van Schaik A and Tapson J C 2014 Stochastic electronics: a neuro-inspired design paradigm for integrated circuits Proc. IEEE 102 843–59

[363] Alawad M and Lin M 2019 Survey of stochastic-based computation paradigms IEEE Trans. Emerg. Top. Comput. 7 98–114

[364] Pantone R D, Kendall J D and Nino J C 2018 Memristive nanowires exhibit small-world connectivity Neural Netw. 106 144–51

[365] Rigotti M, Barak O, Warden M R, Wang X-J, Daw N D, Miller E K and Fusi S 2013 The importance of mixed selectivity in complex cognitive tasks Nature 497 585–90

[366] Thakur C S, Afshar S, Wang R M, Hamilton T J, Tapson J and van Schaik A 2016 Bayesian estimation and inference using stochastic electronics Front. Neurosci. 10 104

[367] Maass W 2014 Noise as a resource for computation and learning in networks of spiking neurons Proc. IEEE 102 860–80

[368] Hubel D H and Wiesel T N 1962 Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex J. Physiol. 160 106–54

[369] Fukushima K and Miyake S 1982 Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition Competition and Cooperation in Neural Nets (Berlin: Springer) pp 267–85

[370] Kim Y and Panda P 2021 Visual explanations from spiking neural networks using interspike intervals (arXiv:2103.14441)

[371] Jain S, Sengupta A, Roy K and Raghunathan A 2020 RxNN: a framework for evaluating deep neural networks on resistive crossbars IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 40 326–38

[372] Chen P-Y, Peng X and Yu S 2018 NeuroSim: a circuit-level macro model for benchmarking neuro-inspired architectures in online learning IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 37 3067–80

[373] Diehl P U, Neil D, Binas J, Cook M, Liu S C and Pfeiffer M 2015 Fast-classifying, highaccuracy spiking deep networks through weight and threshold balancing 2015 Int. Joint Conf. Neural Networks (IJCNN) (Piscataway, NJ: IEEE) pp 1–8

[374] Lee J H, Delbruck T and Pfeiffer M 2016 Training deep spiking neural networks using backpropagation Front. Neurosci. 10 508

[375] Gallego G et al 2020 Event-based vision: a survey IEEE Trans. Pattern Anal. Mach. Intell. 44 154–80

[376] Goodfellow I J, Shlens J and Szegedy C 2014 Explaining and harnessing adversarial examples (arXiv:1412.6572)

[377] Mostafa H 2017 Supervised learning based on temporal coding in spiking neural networks IEEE Trans. Neural Netw. Learn. Syst. 29 3227–35

[378] Han B and Roy K 2020 Deep spiking neural network: energy efficiency through time based coding European Conf. Computer Vision p 388

[379] Montemurro M A, Rasch M J, Murayama Y, Logothetis N K and Panzeri S 2008 Phase-of-firing coding of natural visual stimuli in primary visual cortex Curr. Biol. 18 375–80

[380] Kim J, Kim H, Huh S, Lee J and Choi K 2018 Deep neural networks with weighted spikes Neurocomputing 311 373–86

[381] Rathi N, Srinivasan G, Panda P and Roy K 2020 Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation (arXiv:2005.01807)

[382] Wu Y, Deng L, Li G, Zhu J and Shi L 2018 Spatio-temporal backpropagation for training high-performance spiking neural networks Front. Neurosci. 12 331

[383] Kim Y and Panda P 2020 Revisiting batch normalization for training low-latency deep spiking neural networks from scratch (arXiv:2010.01729)

[384] Sharmin S, Panda P, Sarwar S S, Lee C, Ponghiran W and Roy K 2019 A comprehensive analysis on adversarial robustness of spiking neural networks 2019 Int. Joint Conf. Neural Networks (IJCNN) (Piscataway, NJ: IEEE) pp 1–8

[385] Sharmin S, Rathi N, Panda P and Roy K 2020 Inherent adversarial robustness of deep spiking neural networks: effects of discrete input encoding and non-linear activations European Conf. Computer Vision (Berlin: Springer) pp 399–414

[386] Roy K, Jaiswal A and Panda P 2019 Towards spike-based machine intelligence with neuromorphic computing Nature 575 607–17

[387] Lukoševičius M and Jaeger H 2009 Reservoir computing approaches to recurrent neural network training Comput. Sci. Rev. 3 127–49

[388] Jaeger H 2001 The ‘echo state’ approach to analysing and training recurrent neural network with an erratum note GMD Technical Report 148.34 (Bonn: German National Research Center for Information Technology) p 13

[389] Maass W, Natschläger T and Markram H 2002 Real-time computing without stable states: a new framework for neural computation based on perturbations Neural Comput. 14 2531–60

[390] Jaeger H 2007 Echo state network Scholarpedia 2 2330

[391] Tanaka G, Yamane T, Héroux J B, Nakane R, Kanazawa N, Takeda S, Numata H, Nakano D and Hirose A 2019 Recent advances in physical reservoir computing: a review Neural Netw. 115 100–23

[392] Appeltant L et al 2011 Information processing using a single dynamical node as complex system Nat. Commun. 2 468

[393] Fernando C and Sojakka S 2003 Pattern recognition in a bucket European Conf. Artificial Life (Berlin: Springer)

[394] Shi W, Cao J, Zhang Q, Li Y and Xu L 2016 Edge computing: vision and challenges IEEE Internet Things J. 3 637–46

[395] Jaeger H 2002 Tutorial on Training Recurrent Neural Networks, Covering BPPT, RTRL, EKF and the ‘Echo State Network’ Approach vol 5 (Bonn: GMD-Forschungszentrum Informationstechnik)

[396] Bertschinger N and Natschläger T 2004 Real-time computation at the edge of chaos in recurrent neural networks Neural Comput. 16 1413–36

[397] Lukoševičius M 2012 A practical guide to applying echo state networks Neural Networks: Tricks of the Trade (Berlin: Springer) pp 659–86

[398] Grigoryeva L and Ortega J-P 2018 Echo state networks are universal Neural Netw. 108 495–508

[399] Nakajima K and Fischer I (ed) 2021 Reservoir Computing: Theory, Physical Implementations, and Applications (Berlin: Springer)

[400] Ma C et al 2021 Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout Sci. Rep. 11 3102

[401] Araujo F A et al 2020 Role of non-linear data processing on speech recognition task in the framework of reservoir computing Sci. Rep. 10 328

[402] Nakane R et al Spin waves propagating through a stripe magnetic domain structure and their applications to reservoir computing Phys. Rev. Res. (accepted)

[403] Pathak J, Lu Z, Hunt B R, Girvan M and Ott E 2017 Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data Chaos 27 121102

[404] Gallicchio C, Micheli A and Pedrelli L 2017 Deep reservoir computing: a critical experimental analysis Neurocomputing 268 87–99

[405] Hadaeghi F 2021 Neuromorphic Electronic Systems for Reservoir Computing (Reservoir Computing Natural Computing Series) ed K Nakajima and I Fischer (Berlin: Springer)
[406] Van der Sande G, Brunner D and Soriano M C 2017 Advances in photonic reservoir computing *Nanophotonics* 6 561–76

[407] Cramer B et al 2020 Control of criticality and computation in spiking neuromorphic networks with plasticity *Nat. Commun.* 11 2853

[408] Tanaka G et al 2020 Guest editorial: special issue on new frontiers in extremely efficient reservoir computing *IEEE Trans. Neural Netw. Learn. Syst.* (unpublished)

[409] Gerstner W and Kistler W M 2002 *Spiking Neuron Models: Single Neurons, Populations, Plasticity* (Cambridge: Cambridge University Press)

[410] Paugam-Moisy H and Bohte S M 2012 Computing with spiking neuron networks *Handbook of Natural Computing* vol 1 pp 1–47

[411] Mahowald M 1992 *VLSI Analogies of Neuronal Visual Processing: A Synthesis of Form and Function*

[412] Furber S B, Galluppi F, Temple S and Plana L A 2014 The spinnaker project *Proc. IEEE 102* 652–65

[413] Delorme A, Gautrais J, Van Rullen R and Thorpe S 1999 *SpikeNET: a simulator for modeling large networks of integrate and fire neurons Neurocomputing* 26–27 989–96

[414] Delorme A and Thorpe S J 2003 *SpikeNET: an event-driven simulation package for modeling large networks of spiking neurons Netw. Comput. Neural Syst.* 14 613–27

[415] Adrian E D and Matthews R 1927 The action of light on the eye *J. Physiol.* 63 378–414

[416] Thorpe S J 1990 Spike arrival times: a highly efficient coding scheme for neural networks *Parallel Processing in Neural Systems and Computers* ed R Eckmiller, G Hartmann and G Hauske (Amsterdam: North-Holland) pp 91–4

[417] Gollisch T and Meister M 2008 Rapid neural coding in the retina with relative spike latencies *Science* 319 1108–11

[418] Thorpe S and Gautrais J 1998 Rank order coding *Computational Neuroscience: Trends in Research* ed J Bower (New York: Plenum) pp 113–8

[419] Furber S B, John Bainbridge W, Mike Cumpstey J and Temple S 2004 Sparse distributed memory using N-of-M codes *Neural Netw.* 17 1437–51

[420] Thorpe S J, Guyonneau R, Guilbaud N, Allegraud J-M and Vanrullen R 2004 *SpikeNet: real-time visual processing with one spike per neuron Neurocomputing* 58–60 857–64

[421] Agus T R, Thorpe S J and Pressnitzer D 2010 Rapid formation of robust auditory memories: insights from noise *Neuron* 66 610–8

[422] Thunell E and Thorpe S J 2019 Memory for repeated images in rapid-serial-visual-presentation streams of thousands of images *Psychol. Sci.* 30 989–1000

[423] Masquelier T and Thorpe S J 2007 Unsupervised learning of visual features through spike timing dependent plasticity *PLoS Comput. Biol.* 3 e31

[424] Masquelier T, Guyonneau R and Thorpe S J 2008 Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains *PLoS One* 3 e1377

[425] Bichler O, Querlioz D, Thorpe S J, Bourgoin J-P and Gamrat C 2012 Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity *Neural Netw.* 32 339–48

[426] Thorpe S, Yousefzadeh A, Martin J and Masquelier T 2017 Unsupervised learning of repeating patterns using a novel STDP based algorithm *J. Vis.* 17 1079

[427] Indiveri G 1999 Neuromorphic analog VLSI sensor for visual tracking: circuits and application examples *IEEE Trans. Circuits Syst. II* 46 1337–47

[428] http://jaerproject.org

[429] Lichtsteiner P, Posch C and Delbruck T 2008 A 128 × 128 120 dB 15 µs latency asynchronous temporal contrast vision sensor *IEEE J. Solid-State Circuits* 43 566–76

[430] Qiao N et al 2015 A re-configurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128 K synapses *Front. Neurosci.* 9 141

[431] Monforte M, Arriandiaga A, Glover A and Bartolozzi C 2020 Where and when: event-based spatiotemporal trajectory prediction from the iCub’s point-of-view *2020 IEEE Int. Conf. Robotics and Automation (ICRA)* (Piscataway, NJ: IEEE) pp 9521–7

[432] Kreiser R, Renner A, Leite V R, Serhan B, Bartolozzi C, Glover A and Sandamirskaya Y 2020 An on-chip spiking neural network for estimation of the head pose of the iCub robot *Front. Neurosci.* 14 551

[433] Gutierrez-Galan D, Dominguez-Morales J P, Perez-Peña F, Jimenez-Fernandez A and Linares-Barranco A 2020 NeuroPod: a real-time neuromorphic spiking CPG applied to robotics *Neurocomputing* 381 10–9

[434] Zhao J, Risi N, Monforte M, Bartolozzi C, Indiveri G and Donati E 2020 Closed-loop spiking control on a neuromorphic processor implemented on the iCub *IEEE J. Emerg. Sel. Top. Circuits Syst.* 10 546–56

[435] Naveros F, Luque N R, Ros E and Arleo A 2020 VOR adaptation on a humanoid iCub robot using a spiking cerebellar model *IEEE Trans. Cybern.* 50 47444757

[436] García D H, Adams S, Rast A, Wennekers T, Furber S and Cangelosi A 2018 Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network *Robot. Auton. Syst.* 104 56–71

[437] Rapp H and Nawrot M P 2020 *Proc. Natl Acad. Sci. USA* 117 28412–21

[438] Kaiser J, Mostafa H and Neftci E 2020 Synaptic plasticity dynamics for deep continuous local learning (DECOLLE) *Front. Neurosci.* 14 424

[439] Illing B, Gerstner W and Brea J 2019 Biologically plausible deep learning—but how far can we go with shallow networks? *Neural Netw.* 118 90–101

[440] Klos C, Kossio Y F K, Goedeke S, Gilra A and Memmesheimer R M 2020 Dynamical learning of dynamics *Phys. Rev. Lett.* 125 088103

[441] Panzeri S, Harvey C D, Piasini E, Latham E and Fellin T 2017 Cracking the neural code for sensory perception by combining statistics, intervention, and behavior *Neuron* 93 491507

[442] Yang S, Wang J, Zhang N, Deng B, Pang Y and Azghadi M R 2021 CerebelluMorphic: large-scale neuromorphic model and architecture for supervised motor learning *IEEE Trans. Neural Netw. Learn. Syst.* 1–15

[443] 2016 Video interview with anthony foxx available online at https://theverge.com/a/verge2021/secretary-anthony-foxx

[444] Adams E 2020 The revolution will not be self-driven: the real future of autonomous cars available online at https://robbreport.com/motors/cars/self-driving-cars-explainer-2901586/

[445] Jason Eichenholz, Luminar Technologies Inc 2019 personal communication

[446] Gawron J H, Keoleian G A, De Kleine R D, Wallington T J and Kim H C 2018 Life cycle assessment of connected and automated vehicles: sensing and computing subsystem and vehicle level effects *Environ. Sci. Technol.* 52 3249–56

[447] Lichtsteiner P and Delbruck T 2005 64 × 64 event-driven logarithmic temporal derivative silicon retina *2005 IEEE Workshop on Charge-Coupled Devices and Advanced Image Sensors* (Nagano, Japan) pp 157–60
[448] Itti L, Koch C and Niebur E 1998 A model of saliency-based visual attention for rapid scene analysis *IEEE Trans. Pattern Anal. Machine Intell.* **20** 1254–9

[449] Moreira O et al 2020 NeuronFlow: a hybrid neuromorphic—dataflow processor architecture for AI workloads *2nd IEEE Int. Conf. Artificial Intelligence Circuits (AICAS 2020)*

[450] Brown T et al 2020 Language models are few-shot learners (arXiv:2005.14165v4)

[451] Voelker A, Rasmussen D and Eliasmith C 2020 A spike in performance (arXiv:2002.03553)

[452] Persaud K and Dodd G 1982 Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose *Nature* **299** 352–5

[453] Gardner J W, Hines E L and Wilkinson M 1990 Application of artificial neural networks to an electronic olfactory system *Meas. Sci. Technol.* **1** 446–51

[454] Hines E L and Gardner J W 1994 An artificial neural emulator for an odour sensor array *Sensors Actuators B* **19** 661–4

[455] Persaud K C, Marco S and Gutierrez-Galvez A 2013 *Neuromorphic Olfaction (Frontiers in Neuroengineering)* (Boca Raton, FL: CRC Press)

[456] Pearce T C, Schiffman S S, Nagle H T and Gardner J W 2003 *Handbook of Machine Olfaction: Electronic Nose Technology* (New York: Wiley)

[457] Gronowitz M E, Liu A, Qiu Q, Yu C R and Cleland T A 2009 *A physicochemical model of odor sampling (bioRxiv)*

[458] Pearce T C, Karout S, Rácz Z, Capurro A, Gardner J W and Cole M 2013 Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex *Front. Neurosci.* **7** 119

[459] Vergara A, Fonollosa J, Mahiques J, Trincavelli M, Rulkov N and Huerta R 2013 On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines *Sensors Actuators B* **185** 462–77

[460] Cleland T A and Borthakur A 2020 A systematic framework for olfactory bulb signal transformations *Front. Comput. Neurosci.* **14** 579143

[461] Schmuker M and Schneider G 2007 Processing and classification of chemical data inspired by insect olfaction *Proc. Natl. Acad. Sci. USA* **104** 20285–9

[462] Huerta R, Vembu S, Amigó J M, Nowotny T and Elkan C 2012 Inhibition in multiclass classification *Neural Comput.* **24** 2473–507

[463] Koickal T J, Hamilton A, Tan S L, Covington J A, Gardner J W and Pearce T C 2007 Analog VLSI circuit implementation of an adaptive neuromorphic olfaction chip *IEEE Trans. Circuits Syst.* **I** 54 60–73

[464] Vanarse A, Osseiran A and Rassau A 2017 An investigation into spike-based neuromorphic approaches for artificial olfactory systems *Sensors* **17** 2591

[465] Diamond A, Schmuker M and Nowotny T 2019 An unsupervised neuromorphic clustering algorithm *Biol. Cybern.* **113** 423–37

[466] Imam N and Cleland T A 2020 Rapid online learning and robust recall in a neuromorphic olfactory circuit *Nat. Mach. Intell.* **2** 181–91

[467] Borthakur A and Cleland T A 2019 Signal conditioning for learning in the wild *Presented at the Neuro-inspired Computational Elements Workshop* (Albany, NY, USA) available: https://doi.org/10.1145/3320288.3320293

[468] Borthakur A and Cleland T A 2017 A neuromorphic transfer learning algorithm for orthogonalizing highly overlapping sensor array responses *Presented at the ISOCs/IEEE Int. Symp. Olfaction and Electronic Nose (ISOEN)* (Montreal, Canada)

[469] Guerrero-Rivera R and Pearce T C 2007 Attractor-based pattern classification in a spiking FPGA implementation of the olfactory bulb *2007 3rd Int. IEEE/EMBS Conf. Neural Engineering* (Piscataway, NJ: IEEE) pp 593–9

[470] Imam N, Cleland T A, Manohar R, Merolla P A, Arthur J V, Akopyan F and Modha D S 2012 Implementation of olfactory bulb glomerular-layer computations in a digital neurosynaptic core *Front. Neurosci.* **6** 83

[471] Marco S et al 2014 A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation *Microsyst. Technol.* **20** 729–42

[472] BelBruno J J 2019 Molecularly imprinted polymers *Chem. Rev.* **119** 94–119

[473] Mahowald M and Mead C 1991 The silicon retina *Sci. Am.* **264** 76–82

[474] Posch C, Serrano-Gotarredona T, Linares-Barranco B and Delbruck T 2014 Retinomorphic event-based vision sensors: bioinspired cameras with spiking output *Proc. IEEE 102* 1470–84

[475] Posch C, Matolin D and Wohlgenannt R 2011 A QVGA 143 dB dynamic range frame-free PWM image sensor with lossless pixel-level video compression and time-domain CDS *IEEE J. Solid-State Circuits* **46** 259–75

[476] Brandli C, Berner R, Yang M, Liu S-C and Delbruck T 2014 A 240 × 180 130 dB 3 µs latency global shutter spatiotemporal vision sensor *IEEE J. Solid-State Circuits* **49** 2333–41

[477] Son B et al 2017 A 640 × 480 dynamic vision sensor with a 9 µm pixel and 300 Meps addressevent representation 2017 *IEEE Int. Solid-State Circuits Conf. (ISSCC)* (San Francisco, CA) pp 66–7

[478] Finateu T et al 2020 A 1280 × 720 back-illuminated stacked temporal contrast event-based vision sensor with 4.86 µm pixels, 1.066 GEPS readout, programmable event-rate controller and compressive data-formatting pipeline 2020 *IEEE Int. Solid-State Circuits Conf. (ISSCC)* (San Francisco, CA, USA) pp 112–4

[479] Moradi S, Qiao N, Stefanini F and Indiveri G 2018 A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs) *IEEE Trans. Biomed. Circuits Syst.* **12** 106122

[480] Moreira O et al 2020 NeuronFlow: a neuromorphic processor architecture for live AI applications

[481] Zhou F et al 2019 Optoelectronic resistive random access memory for neuromorphic vision sensors *Nat. Nanotechnol.* **14** 776

[482] Tian H, Wang X, Wu F, Yang Y and Ren T-L 2018 High performance 2D perovskite/graphene optical synapses as artificial eyes 2018 *IEEE Int. Electron Devices Meeting* (IEDM)

[483] 2021 *Neuromorphic Sensing & Computing Report* (Yole Développement) www.yole.fr

[484] Shamma S A 1985 Speech processing in the auditory system: I. The representation of speech sounds in the responses of the auditory nerve *J. Acoust. Soc. Am.* **78** 1612–21

[485] Palmer A R and Russell I J 1986 Phase-locking in the cochlear nerve of the Guinea-pig and its relation to the receptor potential of inner hair-cells *Hear. Res.* **24** 1–15

[486] Lyon R F and Mead C 1988 An analog electronic cochlea *IEEE Trans. Acoust. Speech Signal Process.* **36** 1119–34

[487] Lyon R F, Katsiamis A G and Drakakis E M 2010 History and future of auditory filter models *Proc. 2010 IEEE Int. Symp. Circuits and Systems* pp 3809–12

[488] Liu S-C, Delbruck T, Indiveri G, Whatley A and Douglas R 2015 *Event-Based Neuromorphic Systems* (New York: Wiley)

[489] Yang M, Chien C-H, Delbruck T and Liu S-C 2016 A 0.5 V 55 µW 64 × 2 channel binaural silicon cochlea for event-driven stereo-audio sensing *IEEE J. Solid-State Circuits* **51** 2554–69
[490] Sarpeshkar R, Salthouse C, Sit J-J, Baker M W, Zhak S M, Lu T K-T, Turicchia L and Balster S 2005 An ultra-low-power programmable analog bionic ear processor *IEEE Trans. Biomed. Eng.* **52** 711–27

[491] Horiuchi T K 2009 A spike-latency model for sonar-based navigation in obstacle fields *IEEE Trans. Circuits Syst.* **1** 56 2393–401

[492] Chan V, Jin C and van Schaik A 2010 Adaptive sound localisation with a silicon cochlea pair *Front. Neurosci.* **4** 196

[493] Liu S-C, Rueckauer B, Ceolini E, Huber A and Delbruck T 2019 Event-driven sensing for efficient perception: vision and audition algorithms *IEEE Signal Process. Mag.* **36** 29–37

[494] Uysal I, Sathyendra H and Harris J 2008 Towards spike-based speech processing: a biologically plausible approach to simple acoustic classification *Int. J. Appl. Math. Comput. Sci.* **18** 129–37

[495] Gao C, Braun S, Kiselev I, Anumula J, Delbruck T and Liu S 2019 Real-time speech recognition for IoT purpose using a delta recurrent neural network accelerator 2019 IEEE Int. Symp. Circuits and Systems (ISCAS) pp 1–5

[496] Tsai W Y *et al* 2016 Always-on speech recognition using truenorth, a reconfigurable, neurosynaptic processor *IEEE Trans. Comput.* **66** 996–1007

[497] Yang M, Yeh C-H, Zhou Y, Cerqueira J P, Lazar A A and Seok M 2019 Design of an always-on deep neural network-based 1 µW voice activity detector aided with a customized software model for analog feature extraction *IEEE J. Solid-State Circuits* **54** 1764–77

[498] Neftci E, Mostafa H and Zenke F 2019 Surrogate gradient learning in spiking neural networks *IEEE Signal Process. Mag.* **36** 61–3

[499] Rueckauer B, Lungu I, Hu Y, Pfeiffer M and Liu S-C 2017 Conversion of continuous-valued deep networks to efficient event-driven networks for image classification *Front. Neurosci.* **11** 682

[500] Wu J, Yilmaz E, Zhang M, Li H and Tan K C 2020 Deep spiking neural networks for large vocabulary automatic speech recognition *Front. Neurosci.* **14** 199

[501] Zai A, Bhargava S, Mesgarani N and Liu S-C 2015 Reconstruction of audio waveforms from spike trains of artificial cochlea models *Front. Neurosci.* **9** 347

[502] Ceolini E, Anumula J, Braun S and Liu S 2019 Event-driven pipeline for low-latency low-compute keyword spotting and speaker verification system 2019 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 7953–7

[503] Shan W, Yang M-H, Xu J, Lu Y, Zhang S, Wang T, Yang J, Shi L and Seok M 2020 14.1 A 510 nW 0.41 V low-memory low-computation keyword-spotting chip using serial FFT-based MFCC and binarized depthwise separable convolutional neural network in 28 nm CMOS 2020 IEEE Int. Solid-State Circuits Conf. (ISSCC) 230–2

[504] Renaud-LeMasson S, LeMasson G, Marder E and Abbot L F 1993 Hybrid circuits of interacting computer model and biological neurons *Presented at the Advances in Neural Information Processing Systems 5* (NIPS Conf.)

[505] Chapin J K, Moxon K A, Markowitz R S and Nicolelis M A L 1999 Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex *Nat. Neurosci.* **2** 664–70

[506] Hochberg L R *et al* 2012 Reach and grasp by people with tetraplegia using a neurally controlled robotic arm *Nature* **485** 372–5

[507] Panuccio G, Semprini M and Chiappalone M 2016 Intelligent biohybrid systems for functional brain repair *New Horiz. Transl. Med.* **3** 162–74

[508] Guggenmos D J, Azin M, Barbay S, Mahnken J D, Dunham C, Mohseni P and Nudo R J 2013 Restoration of function after brain damage using a neural prosthesis *Proc. Natl Acad. Sci.* **110** 2177

[509] Vassanelli S and Mahmud M 2016 Trends and challenges in neuroengineering: toward ‘intelligent’ neuroprostheses through brain-‘brain inspired systems’ communication *Front. Neurosci.* **10** 438

[510] Serb A *et al* 2020 Memristive synapses connect brain and silicon spiking neurons *Sci. Rep.* **10** 2590

[511] Pautot S, Wyart C and Isacoff E Y 2008 Colloid-guided assembly of oriented 3D neuronal networks *Nat. Methods* **5** 735–40

[512] Gómez-Martínez R, Hernández-Pinto A M, Duch M, Vázquez P, Zinoviev K, de la Rosa E J, Esteve J, Suárez T and Plaza J A 2013 Silicon chips detect intracellular pressure changes in living cells *Nat. Nanotechnol.* **8** 517–21

[513] Chen L Y, Parizi K B, Kosuge H, Milaninia K M, McConnell M V, Wong H-S P and Poon A S Y 2013 Mass fabrication and delivery of 3D multilayer µtags into living cells *Sci. Rep.* **3** 2295

[514] Desai S B *et al* 2016 MoS₂ transistors with one-nanometer gate lengths *Science* **354** 6308

[515] van de Burgt Y, Melianas A, Keene S T, Malliaras G and Salleo A 2018 Organic electronics for neuromorphic computing *Nat. Electron.* **1** 386–97

[516] Tran L-G, Cha H-K and Park W-T 2017 RF power harvesting: a review on designing methodologies and applications *Micro Nano Syst. Lett.* **5** 14

[517] Basaeri H, Christensen D B and Roundy S 2016 A review of acoustic power transfer for bio-medical implants *Smart Mater. Struct.* **25** 123001

[518] Rebel G, Estevez F, Gloesekoetter P and Castillo-Secilla J M 2015 Energy harvesting on human bodies *Smart Health: Open Problems and Future Challenges* ed A Holzinger, C Röcker and M Ziefle (Berlin: Springer) pp 125–59

[519] Roseman J M, Lin J, Ramakrishnan S, Rosenberg J K and Shepard K L 2015 Hybrid integrated biological-solid-state system powered with adenosine triphosphate *Nat. Commun.* **6** 10070

[520] Wang W *et al* 2018 Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses *Sci. Adv.* **4** eaat4752

[521] Huang X 2018 Materials and applications of bioresorbable electronics *J. Semicond.* **39** 011003

[522] Lecomte A, Giantomasi L, Rancati S, Boi F, Angotzi G N and Berdondini L 2020 Surface-functionalized self-standing microdevices exhibit predictive localization and seamless integration in 3D neural spheroids *Adv. Biosys.* **4** 2000114

[523] Jafari A, Ganesan A, Thalisetty C S K, Sivasubramanian V, Oates T and Mohsenin T 2019 SensorNet: a scalable and low-power deep convolutional neural network for multimodal data classification *IEEE Trans. Circuits Syst.* **1** 66 274–87

[524] Hosseini M, Ren H, Rashid H-A, Mazumder A N, Prakash B and Mohsenin T 2020 Neural networks for pulmonary disease diagnosis using auditory and demographic information *epiDAMIK 2020: 3rd epiDAMIK ACM SIGKDD Int. Workshop on Epidemiology Meets Data Mining and Knowledge Discovery*

[525] Dauphin Y N and Bengio Y 2013 Big neural networks waste capacity (arXiv:1301.3583)

[526] Gong Y, Liu L, Yang M and Bourdev L 2014 Compressing deep convolutional networks using vector quantization (arXiv:1412.6115)

[527] Alemdar H, Leroy V, Prost A B and Pétrot F 2017 Ternary neural networks for resource-efficient AI applications *Int. Joint Conf. Neural Networks (IJCNN)* (Anchorage, AK, USA)

[528] Courbariaux M, Bengio Y and David J 2015 Binaryconnect: training deep neural networks with binary weights during propagations *Advances in Neural Information Processing Systems* vol 28 3123–31
[529] Lo C Y, Lau F C M and Sham C W 2018 Fixed-point implementation of convolutional neural networks for image classification *Int. Conf. Advanced Technologies for Communications (ATC)* (Ho Chi Minh City, Vietnam)

[530] Umuroglu Y, Fraser N J, Gambardella G, Blott M, Leong P, Jahre M and Vissers K 2017 FINN: a framework for fast, scalable binarized neural network inference *Proc. 2017 ACM/SIGDA Int. Symp. Field-Programmable Gate Arrays* (New York: ACM) pp 65–74

[531] Ren H, Mazumder A N, Rashid H-A, Chandrareddy V, Shiri A and Mohsenin T 2020 End-to-end scalable and low power multi-modal CNN for respiratory-related symptoms detection *IEEE 33rd Int. System-on-Chip Conf. (SOCC)*

[532] Khatwani M, Rashid H-A, Paneliya H, Horton M, Waytowich N, Hairston W D and Mohsenin T 2020 A flexible multichannel EEG artifact identification processor using depthwise-separable convolutional neural networks *ACM J. Emerg. Technol. Comput. Syst.* **17** 1–23

[533] Enoka R M 1995 Morphological features and activation patterns of motor units *J. Clin. Neurophysiol.* **12** 538–59

[534] Rodriguez-Tapia B, Soto I, Martinez D M and Arballo N C 2020 Myoelectric interfaces and related applications: current state of EMG signal processing—a systematic review *IEEE Access* **8** 7792–805

[535] Arteaga M V, Castiblanco J C, Mondragon I F, Colorado J D and Alvarado-Rojas C 2020 EMG-driven hand model based on the classification of individual finger movements *Biomed. Signal Process. Control* **58** 101834

[536] Park K H and Lee S W 2016 Movement intention decoding based on deep learning for multiuser myoelectric interfaces *2016 4th Int. Winter Conf. Brain-Computer Interface (BCI)* (Piscataway, NJ: IEEE) pp 1–2

[537] Donati E, Payvand M, Risi N, Krause R, Burelo K, Indiveri G, Dalgaty T and Vianello E 2018 Processing EMG signals using reservoir computing on an event-based neuromorphic system *2018 IEEE Biomedical Circuits and Systems Conf. (BioCAS)* (Piscataway, NJ: IEEE) pp 1–4

[538] Donati E, Payvand M, Risi N, Krause R and Indiveri G 2019 Discrimination of EMG signals using a neuromorphic implementation of a spiking neural network *IEEE Trans. Biomed. Circuits Syst.* **13** 795–803

[539] Ceolini E, Frenkel C, Shrestha S B, Taverni G, Khacef L, Payvand M and Donati E 2020 Handgesture recognition based on EMG and event-based camera sensor fusion: a benchmark in neuromorphic computing *Front. Neurosci.* **14** 637

[540] Behrenbeck *J et al* 2019 Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware *J. Neural Eng.* **16** 026014

[541] Ma Y, Chen B, Ren P, Zheng N, Indiveri G and Donati E 2020 EMG-based gestures classification using a mixed-signal neuromorphic processing system *IEEE J. Emerg. Sel. Top. Circuits Syst.* **10** 578–87

[542] Azghadi M R, Lammie C, Eshraghian J K, Payvand M, Donati E, Linares-Barranco B and Indiveri G 2020 Hardware implementation of deep network accelerators towards healthcare and biomedical applications *IEEE Trans. Biomed. Circuits Syst.* **14** 1138–59

[543] Del Vecchio A, Germer C M, Elias L A, Fu Q, Fine J, Santello M and Farina D 2019 The human central nervous system transmits common synaptic inputs to distinct motor neuron pools during non-synergistic digit actions *J. Physiol.* **597** 5935–48

[544] Valle G *et al* 2018 Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis *Neuron* **100** 37–45

[545] Moin A *et al* 2020 A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition *Nat. Electron.* **4** 54–63

[546] Urh F, Strnad D, Clarke A, Farina D and Holobar A 2020 On the selection of neural network architecture for supervised motor unit identification from high-density surface EMG *2020 42nd Annual Int. Conf. IEEE Engineering in Medicine & Biology Society (EMBC)* (Piscataway, NJ: IEEE) pp 736–9

[547] Kleine B U, van Dijk J P, Lapatki B G, Zwarts M J and Stegeman D F 2007 Using two-dimensional spatial information in decomposition of surface EMG signals *J. Electromyogr. Kinesiol.* **17** 535–48

[548] Holobar A and Zazula D 2007 Multichannel blind source separation using convolution kernel compensation *IEEE Trans. Signal Process.* **55** 4487–96

[549] Rossi D *et al* 2015 PULP: a parallel ultra low power platform for next generation IoT applications *2015 IEEE Hot Chips 27 Symp. (HCS)* (Piscataway, NJ: IEEE) pp 1–39

[550] Chatterjee S, Roy S S, Bose R and Pratiher S 2020 Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders *IET Sci. Meas. Technol.* **14** 817–24

[551] Trigili E, Grazi L, Crea S, Accogli A, Carpaneto J, Micera S, Vitiello N and Panarese A 2019 Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks *J. Neuroeng. Rehabil.* **16** 45

[552] Ajoudani A, Zanchettin A M, Ivaldi S, Albu-Schäffer A, Kosuge K and Khatib O 2018 Progress and prospects of the human-robot collaboration *Auton. Robots* **42** 957–75

[553] Ingrand F and Ghallab M 2017 Deliberation for autonomous robots: a survey *Artif. Intell.* **247** 10–44

[554] Kunze L, Hawes N, Duckett T, Hanheide M and Krajnik T 2018 Artificial intelligence for long-term robot autonomy: a survey *IEEE Robot. Autom. Lett.* **3** 4023–30

[555] Capolei M C, Angelidis E, Falotico E, Lund H H and Tolu S 2019 A biomimetic control method increases the adaptability of a humanoid robot acting in a dynamic environment *Front. Neurorobot.* **13** 70

[556] Indiveri G and Sandamirskaya Y 2019 The importance of space and time for signal processing in neuromorphic agents: the challenge of developing low-power, autonomous agents that interact with the environment *IEEE Signal Process. Mag.* **36** 16–28

[557] Thompson F and Galeazzi R 2020 Robust mission planning for autonomous marine vehicle fleets *Robot. Auton. Syst.* **124** 103404

[558] Zool H I and Nohaidda S 2019 A survey and analysis of cooperative multi-agent robot systems: challenges and directions *Applications of Mobile Robots* vol 1 (IntechOpen)

[559] Yang H, Han Q-L, Ge X, Ding L, Xu Y, Jiang B and Zhou D 2020 Fault-tolerant cooperative control of multiagent systems: a survey of trends and methodologies *IEEE Trans. Ind. Inf.* **16** 4–17

[560] Fardet T and Levina A 2020 Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions *PLoS Comput. Biol.* **16** e1008503

[561] Naveros F, Garrido J A, Carrillo R R, Ros E and Luque N R 2017 Event- and time-driven techniques using parallel CPU-GPU co-processing for spiking neural networks *Front. Neuroinform.* **11** 7

[562] Furber S 2016 Large-scale neuromorphic computing systems *J. Neural Eng.* **13** 051001

[563] Thompson F and Guihen D 2019 Review of mission planning for autonomous marine vehicle fleets *J Field Robot.* **36** 333–54

[564] Atyabi A, MahmoudZadeh S and Nefti-Meziani S 2018 Current advancements on autonomous mission planning and management systems: an AUV and UAV perspective *Annu. Rev. Control* **46** 196–215
[565] Li J, Li Z, Chen F, Bicchi A, Sun Y and Fukuda T 2019 Combined sensing, cognition, learning, and control for developing future neuro-robotics systems: a survey *IEEE Trans. Cognit. Dev. Syst.* **11** 148–61

[566] Woo J, Kim J H, Im J-P and Moon S E 2020 Recent advancements in emerging neuromorphic device technologies *Adv. Intell. Syst.* **2** 2070101

[567] Hauser S, Mutlu M, Léziart P-A, Khodr H, Bernardino A and Ijspeert A J 2020 Roombots extended: challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture *Robot. Auton. Syst.* **127** 103467

[568] Galin R, Meshcheryakov R, Kamesheva S and Samoshina A 2020 Cobots and the benefits of their implementation in intelligent manufacturing *IOP Conf. Ser.: Mater. Sci. Eng.* **862** 032075

[569] Müller V C 2020 Ethics of artificial intelligence and robotics *The Stanford Encyclopedia of Philosophy* (Winter 2020 Edition) ed E N Zalta (https://plato.stanford.edu/archives/win2020/entries/ethics-ai/)

[570] Coeckelbergh M 2020 *AI Ethics* (Cambridge, MA: MIT Press)

[571] Topol E 2019 *Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again* (New York: Basic Books)

[572] Cohen I 2018 Is there a duty to share healthcare data? *Big Data, Health Law, and Bioethics* ed I Cohen, H Lynch, E Vayena and U Gasser (Cambridge: Cambridge University Press) pp 209–22

[573] Binns R 2018 Fairness in machine learning: lessons from political philosophy *Proc. 1st Conf. Fairness, Accountability and Transparency, Proc. Machine Learning Research* vol 81 149–59

[574] Ploug T and Holm S 2020 The right to refuse diagnostics and treatment planning by artificial intelligence *Med. Health Care Philos.* **23** 107–14

[575] Nyholm S and Frank L 2017 From sex robots to love robots: is mutual love with a robot possible? *Robot Sex: Social and Ethical Implications* vol 219–243 (Cambridge, MA: MIT Press)

[576] Danaher J 2019 The philosophical case for robot friendship *J. Posthuman Stud.* **3** 5–24

[577] Baldwin R 2019 *The Globotics Upheaval: Globalisation, Robotics and the Future of Work* (New York: Oxford University Press)

[578] Goos M 2018 The impact of technological progress on labour markets: policy challenges *Oxford Rev. Econ. Pol.* **34** 362–75

[579] Turner J 2019 *Robot Rules: Regulating Artificial Intelligence* (Berlin: Springer)

[580] Coeckelbergh M 2016 Care robots and the future of ICT-mediated elderly care: a response to doom scenarios *AI Soc.* **31** 455–62

[581] Bostrom N 2014 *Superintelligence: Paths, Dangers, Strategies* (Oxford: Oxford University Press)

[582] Floridi L 2016 Should we be afraid of AI? Machines seem to be getting smarter and smarter and much better at human jobs, yet true AI is utterly implausible. Why? (Aeon) https://aeon.co/essays/true-ai-is-both-logically-possible-and-utterly-implausible