Simple model of spiking neurons

Dynamical Systems in Neuroscience

Izhikevich, E. M. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2526.001.0001 · Cited by 3,776

NEURAL EXCITABILITY, SPIKING AND BURSTING

Izhikevich, E. M. (2000) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127400000840 · Cited by 1,994

Intrinsic firing patterns of diverse neocortical neurons

Connors, B. W., & Gutnick, M. J. (1990) · Trends in Neurosciences · doi:10.1016/0166-2236(90)90185-d · Cited by 1,440

Two networks of electrically coupled inhibitory neurons in neocortex

Gibson, J. R., Beierlein, M., & Connors, B. W. (1999) · Nature · doi:10.1038/47035 · Cited by 1,404

Weakly Connected Neural Networks

Hoppensteadt, F. C., & Izhikevich, E. M. (1997) · Applied mathematical sciences · doi:10.1007/978-1-4612-1828-9 · Cited by 1,261

Chattering Cells: Superficial Pyramidal Neurons Contributing to the Generation of Synchronous Oscillations in the Visual Cortex

Gray, C. M., & McCormick, D. A. (1996) · Science · doi:10.1126/science.274.5284.109 · Cited by 906

Parabolic Bursting in an Excitable System Coupled with a Slow Oscillation

Ermentrout, G. B., & Kopell, N. (1986) · SIAM Journal on Applied Mathematics · doi:10.1137/0146017 · Cited by 627

Bursts as a unit of neural information: selective communication via resonance

Izhikevich, E. M., Desai, N. S., Walcott, E. C., & Hoppensteadt, F. C. (2003) · Trends in Neurosciences · doi:10.1016/s0166-2236(03)00034-1 · Cited by 606

Resonate-and-fire neurons

Izhikevich, E. M. (2001) · Neural Networks · doi:10.1016/s0893-6080(01)00078-8 · Cited by 509

Global and partial synchronism in phase-locked loop networks

Monteiro, L. H. A., Canto, N. C. F., Chauí‐Berlinck, J. G., Orsatti, F. M., & Piqueira, J. R. C. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820441 · Cited by 24

Spike-Timing-Dependent Plasticity With Axonal Delay Tunes Networks of Izhikevich Neurons to the Edge of Synchronization Transition With Scale-Free Avalanches

Khoshkhou, M., & Montakhab, A. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00073 · Cited by 23

Neuroattack:undermining spiking neural networks security through externally triggered bit-flips

Venceslai, V. (., Marchisio, A. (., Alouani, I., Martina, M. (., & Shafique, M. (. (2020) · LillOA (Université de Lille (University Of Lille)) · Cited by 26

A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns

She, X., Dash, S., Kim, D., & Mukhopadhyay, S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.615756 · Cited by 26

Using focal cooling to link neural dynamics and behavior

Banerjee, A., Egger, R., & Long, M. A. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.029 · Cited by 29

Effect of magnetic induction on the synchronizability of coupled neuron network

Rajagopal, K., Jafari, S., Karthikeyan, A., & Srinivasan, A. (2021) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0061406 · Cited by 26

Comprehensive characterization of oscillatory signatures in a model circuit with PV- and SOM-expressing interneurons

Wal, M. T., & Tiesinga, P. H. E. (2021) · Biological Cybernetics · doi:10.1007/s00422-021-00894-6 · Cited by 27

Model design for networks of heterogeneous Hodgkin–Huxley neurons

Giannari, A. G., & Astolfi, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.115 · Cited by 26

An overview of brain-like computing: Architecture, applications, and future trends

Ou, W., Xiao, S., Zhu, C., Han, W., & Zhang, Q. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1041108 · Cited by 30

Biomimetic strategies and technologies for artificial tactile sensory systems

Wang, J., Liu, X., Li, R., & Fan, Y. (2023) · Trends in biotechnology · doi:10.1016/j.tibtech.2022.12.012 · Cited by 27

Emerging Fluorite-Structured Antiferroelectrics and Their Semiconductor Applications

Park, G. H., Lee, D. H., Choi, H., Kwon, T., Cho, Y. H., Kim, S. H., & Park, M. H. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.2c01615 · Cited by 26

Analysis of Network Models with Neuron-Astrocyte Interactions

Manninen, T., Aćimović, J., & Linne, M. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09622-w · Cited by 29

Complexity matching in neural networks

Mafahim, J. U., Lambert, D., Zare, M., & Paolo Grigolini, A. (2015) · New Journal of Physics · doi:10.1088/1367-2630/17/1/015003 · Cited by 26

A neurobehavioural framework for autonomous animation of virtual human faces

Sagar, M. A., Bullivant, D. P., Robertson, P., Efimov, O., Jawed, K., Kalarot, R., & Wu, T. (2014) · doi:10.1145/2668956.2668960 · Cited by 25

Statistical modelling of higher-order correlations in pools of neural activity

Montani, F., Phoka, E., Portesi, M., & Schultz, S. R. (2013) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2013.03.012 · Cited by 24

Gait event detection through neuromorphic spike sequence learning

Lee, W. W., Yu, H., & Thakor, N. V. (2014) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2014.6913895 · Cited by 26

Incorporating synaptic time-dependent plasticity and dynamic synapse into a computational model of wind-up

Farajidavar, A., Saeb, S., & Behbehani, K. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.12.021 · Cited by 26

The antipsychotic drugs olanzapine and haloperidol modify network connectivity and spontaneous activity of neural networks in vitro

Dzyubenko, E., Juckel, G., & Faissner, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-11944-0 · Cited by 25

Diverse neuronal responses of a fractional-order Izhikevich model: journey from chattering to fast spiking

Mondal, A., & Upadhyay, R. K. (2017) · Nonlinear Dynamics · doi:10.1007/s11071-017-3944-9 · Cited by 25

Period-adding bifurcation and chaos in a hybrid Hindmarsh–Rose model

Yang, Y., Liao, X., & Dong, T. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.04.009 · Cited by 26

Dynamic Texture Decoding Using a Neuromorphic Multilayer Tactile Sensor

Nguyen, H. H., Osborn, L. E., Iskarous, M. M., Shallal, C., Hunt, C. L., Betthauser, J., & Thakor, N. V. (2018) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2018.8584826 · Cited by 26

Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems

López-Vázquez, G., Ornelas-Rodríguez, M., Espinal, A., Soria-Alcaraz, J. A., Rojas‐Domínguez, A., Soberanes, H. J. P., Carpio, M., & Rostro‐González, H. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/4182639 · Cited by 26

Achieving Neural Compatibility With Human Sensorimotor Control in Prosthetic and Therapeutic Devices

Lan, N., Niu, C. M., Hao, M., Chou, C.-H., & Dai, C. (2019) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2019.2930356 · Cited by 27

Antiferromagnetic skyrmion repulsion based artificial neuron device

Bindal, N., Ang, C. C. I., Lew, W. S., & Kaushik, B. K. (2021) · Nanotechnology · doi:10.1088/1361-6528/abe261 · Cited by 26

How Gibbs Distributions May Naturally Arise from Synaptic Adaptation Mechanisms. A Model-Based Argumentation

Cessac, B., Rostro‐González, H., Vasquez, J. C., & Viéville, T. (2009) · Journal of Statistical Physics · doi:10.1007/s10955-009-9786-1 · Cited by 26

Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach

Kasabov, N. K. (2008) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2008.926584 · Cited by 26

A Flexible Laser-Induced Graphene Memristor with Volatile Switching for Neuromorphic Applications

Ganeriwala, M. D., Motos, R., Marín, E. G., Cuesta-Lopez, J., Palomo, M. G., Rodríguez, N., Ruiz, F. G., & Godoy, A. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c07589 · Cited by 26

Spiking neural networks for autonomous driving: A review

Martínez, F. S., Casas-Roma, J., Subirats, L., & Parada, R. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109415 · Cited by 29

Synchronization behavior of memristive FitzHugh-Nagumo neurons in time-varying networks under external stimuli

Huang, W., Wu, Y., Ding, Q., Jia, Y., Xie, Y., & Hu, Y. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116001 · Cited by 25

Nonlinear dynamics based digital logic and circuits

Kia, B., Lindner, J. F., & Ditto, W. L. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00049 · Cited by 25

Dynamical responses to external stimuli for both cases of excitatory and inhibitory synchronization in a complex neuronal network

Kim, S.-Y., & Lim, W. (2017) · Cognitive Neurodynamics · doi:10.1007/s11571-017-9441-5 · Cited by 25

Biomimetic neural network for modifying biological dynamics during hybrid experiments

Ambroise, M., Buccelli, S., Grassia, F., Pirog, A., Bornat, Y., Chiappalone, M., & Levi, T. (2017) · Artificial Life and Robotics · doi:10.1007/s10015-017-0366-1 · Cited by 25

Spatiotemporal activities of a pulse-coupled biological neural network

Mineeja, K. K., & Ignatius, R. P. (2018) · Nonlinear Dynamics · doi:10.1007/s11071-018-4169-2 · Cited by 25

Effect of Lévy noise on the networks of Izhikevich neurons

Vinaya, M., & Ignatius, R. P. (2018) · Nonlinear Dynamics · doi:10.1007/s11071-018-4414-8 · Cited by 25

Vibrational resonance in a randomly connected neural network

Qin, Y., Han, C., Che, Y., & Zhao, J. L. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9492-2 · Cited by 25

A MEMS Nonlinear Dynamic Approach for Neural Computing

Alsaleem, F., Hasan, M. H., & Tesfay, M. K. (2018) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2018.2864175 · Cited by 25

Principles of Transcranial Direct Current Stimulation (tDCS): Introduction to the Biophysics of tDCS

Reato, D., Salvador, R., Bikson, M., Opitz, A., Dmochowski, J., & Miranda, P. C. (2019) · Springer eBooks · doi:10.1007/978-3-319-95948-1_2 · Cited by 25

Sensing and Control for Prosthetic Hands in Clinical and Research Applications

Osborn, L. E., Iskarous, M. M., & Thakor, N. V. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814659-0.00022-9 · Cited by 29

Route to hyperchaos and chimera states in a network of modified Hindmarsh-Rose neuron model with electromagnetic flux and external excitation

Wang, S., He, S., Rajagopal, K., Karthikeyan, A., & Sun, K. (2020) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2020-900247-7 · Cited by 25

Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory

Liang, Q. Y., Zeng, Y., & Xu, B. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00051 · Cited by 27

Spatio-Temporal Encoding Improves Neuromorphic Tactile Texture Classification

Gupta, A. K., Nakagawa-Silva, A., Lepora, N. F., & Thakor, N. V. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3087511 · Cited by 25

Analog Synapses Based on Nonvolatile FETs With Amorphous ZrO2 Dielectric for Spiking Neural Network Applications

Liu, H., Li, J., Wang, G., Chen, J., Yu, X., Liu, Y., Jin, C., Wang, S., Hao, Y., & Han, G. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3139570 · Cited by 25

Exploration in neo-Hebbian reinforcement learning: Computational approaches to the exploration–exploitation balance with bio-inspired neural networks

Triche, A., Maida, A. S., & Kumar, A. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.021 · Cited by 27

Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Approximate Derivative Algorithm

Liu, Y., Cao, K., Li, R., Zhang, H., & Zhou, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3207098 · Cited by 25

A Fully Analog CMOS Implementation of a Two-variable Spiking Neuron in the Subthreshold Region and its Network Operation

Moriya, S., Yamamoto, H., Sato, S., Yuminaka, Y., Horio, Y., & Madrenas, J. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891920 · Cited by 25

DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs

Aung, M. T. L., Gerlinghoff, D., Qu, C., Yang, L., Huang, T., Goh, R. S. M., Luo, T., & Wong, W. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3272284 · Cited by 25

Device physics recipe to make spiking neurons

Bisquert, J. (2023) · Chemical Physics Reviews · doi:10.1063/5.0145391 · Cited by 25

On-chip spiking neural networks based on add-drop ring microresonators and electrically reconfigurable phase-change material photonic switches

Zhang, Q., Jiang, N., Zhang, Y., Li, A., Xiong, H., Hu, G., Cao, Y., & Qiu, K. L. (2024) · Photonics Research · doi:10.1364/prj.507178 · Cited by 25

A two-variable silicon neuron circuit based on the Izhikevich model

Mizoguchi, N., Nagamatsu, Y., Aihara, K., & Kohno, T. (2011) · Artificial Life and Robotics · doi:10.1007/s10015-011-0956-2 · Cited by 25

Learning expectation in insects: A recurrent spiking neural model for spatio-temporal representation

Arena, P., Patané, L., & Termini, P. S. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.034 · Cited by 25

Deterministic neural dynamics transmitted through neural networks

Asai, Y., Guha, A., & Villa, A. E. P. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.06.014 · Cited by 25

Oscillations and synchrony in a cortical neural network

Qu, J., Wang, R., Yan, C., & Du, Y. (2013) · Cognitive Neurodynamics · doi:10.1007/s11571-013-9268-7 · Cited by 25

A Novel PWC Spiking Neuron Model: Neuron-Like Bifurcation Scenarios and Responses

Yamashita, Y., & Torikai, H. (2012) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2012.2190651 · Cited by 25

Which model to use for the Liquid State Machine?

Grzyb, B. J., Chinellato, E., Wójcik, G. M., & Kamiński, W. A. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178822 · Cited by 25

ToolConnect: A Functional Connectivity Toolbox for In vitro Networks

Pastore, V. P., Poli, D., Godjoski, A., Sergio Martinoia, A., & Massobrio, P. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00013 · Cited by 25

An integrative model of the intrinsic hippocampal theta rhythm

Hummos, A., & Nair, S. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182648 · Cited by 26

Perceptual Generalization and Context in a Network Memory Inspired Long-Term Memory for Artificial Cognition

Duro, R. J., Becerra, J. A., Monroy, J., & Bellas, F. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500533 · Cited by 25

Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex

Beyeler, M., Richert, M., Dutt, N. D., & Krichmar, J. L. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9220-y · Cited by 25

The race to learn: Spike timing and STDP can coordinate learning and recall in CA3

Nolan, C. R., Wyeth, G. F., Milford, M. J., & Wiles, J. (2010) · Hippocampus · doi:10.1002/hipo.20777 · Cited by 25

Synchronization effects using a piecewise linear map-based spiking–bursting neuron model

Aguirre, C., Campos, D., Pascual, P., & Serrano, E. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.056 · Cited by 25

Slow and fast pulses in 1-D cultures of excitatory neurons

Álvarez-Lacalle, E., & Moses, E. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-008-0123-5 · Cited by 25

Self-Sustained Irregular Activity in 2-D Small-World Networks of Excitatory and Inhibitory Neurons

Guo, D., & Li, C. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2044419 · Cited by 24

Low Cost Digital Implementation of Hybrid FitzHugh Nagumo–Morris Lecar Neuron Model Considering Electromagnetic Flux Coupling

Majidifar, S., Hayati, M., Malekshahi, M. R., & Abbott, D. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3214851 · Cited by 24

Review of medical data analysis based on spiking neural networks

Li, X., Xiaofan, Z., Yi, X. H., Liu, D., Wang, H., Zhang, B., Bohan, Z., Zhao, D., & Wang, L. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.08.138 · Cited by 24

SFOD: Spiking Fusion Object Detector

Fan, Y., Zhang, W., Liu, C., Li, M., & Lu, W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01627 · Cited by 29

Ultra-low-Voltage Integrable Electronic Realization of Integer- and Fractional-Order Liao’s Chaotic Delayed Neuron Model

Kant, N. A., Dar, M. R., Khanday, F. A., & Psychalinos, C. (2017) · Circuits Systems and Signal Processing · doi:10.1007/s00034-017-0615-5 · Cited by 24

A single Markov-type kinetic model accounting for the macroscopic currents of all human voltage-gated sodium channel isoforms

Balbi, P., Massobrio, P., & Kotaleski, J. H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005737 · Cited by 24

Field coupling-induced wave propagation and pattern stability in a two-layer neuronal network under noise

Qin, H., Ma, J., Ren, G., & Zhou, P. (2018) · International Journal of Modern Physics B · doi:10.1142/s0217979218502983 · Cited by 24

ELFENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models

Shifman, A. R., & Lewis, J. E. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00035 · Cited by 24

A Framework for the Analysis of Throughput-Constraints of SNNs on Neuromorphic Hardware

Balaji, A., & Das, A. K. (2019) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2019.00043 · Cited by 24

A consecutive hybrid spiking-convolutional (CHSC) neural controller for sequential decision making in robots

Azimirad, V., Ramezanlou, M. T., Sotubadi, S. V., & Janabi‐Sharifi, F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.097 · Cited by 24

Bio-plausible digital implementation of a reward modulated STDP synapse

Quintana, F. M., Perez‐Peña, F., & Galindo, P. L. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07220-6 · Cited by 24

Superconducting Bio-Inspired Au-Nanowire-Based Neurons

Skryabina, O. V., Schegolev, A. E., Klenov, N. V., Bakurskiy, S. V., Shishkin, A. G., Sotnichuk, S. V., Napolskii, K. S., Nazhestkin, I. A., Soloviev, I. I., Kupriyanov, M. Y., et al. (2022) · Nanomaterials · doi:10.3390/nano12101671 · Cited by 24

Emergence of hidden dynamics in different neuronal network architecture with injected electromagnetic induction

Upadhyay, R. K., Sharma, S. K., Mondal, A., & Mondal, A. (2022) · Applied Mathematical Modelling · doi:10.1016/j.apm.2022.06.031 · Cited by 24

Hardware for Deep Learning Acceleration

Song, C., Ye, C., Sim, Y., & Jeong, D. S. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300762 · Cited by 28

Circuit reactivation dynamically regulates synaptic plasticity in neocortex

Kruskal, P. B., Li, L., & MacLean, J. N. (2013) · Nature Communications · doi:10.1038/ncomms3574 · Cited by 27

A sequential Monte Carlo approach to estimate biophysical neural models from spikes

Meng, L. K., Kramer, M., & Eden, U. T. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/6/065006 · Cited by 25

A parallel spiking neural network simulator

Cheung, K., Schultz, S. R., & Leong, P. H. W. (2009) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2009.5377667 · Cited by 24

Realistic thermodynamic and statistical-mechanical measures for neural synchronization

Kim, S.-Y., & Lim, W. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.12.013 · Cited by 24

Insect inspired unsupervised learning for tactic and phobic behavior enhancement in a hybrid robot

Arena, P., Fiore, S. D., Patané, L., Pollino, M., & Ventura, C. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596542 · Cited by 24

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics

Sinapayen, L., Masumori, A., & Ikegami, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170388 · Cited by 24

A Spiking Neural Network Model for Sound Recognition

Xiao, R., Yan, R., Tang, H., & Tan, K. C. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-5230-9_57 · Cited by 24

SPARE: Spiking Neural Network Acceleration Using ROM-Embedded RAMs as In-Memory-Computation Primitives

Agrawal, A., Ankit, A., & Roy, K. (2018) · IEEE Transactions on Computers · doi:10.1109/tc.2018.2867048 · Cited by 26

The role of cortical oscillations in a spiking neural network model of the basal ganglia

Fountas, Z., & Shanahan, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189109 · Cited by 24

Stochastic resonance and synchronization behaviors of excitatory-inhibitory small-world network subjected to electromagnetic induction

Zhang, X., & Liu, S. (2018) · Chinese Physics B · doi:10.1088/1674-1056/27/4/040501 · Cited by 24

Visual Attention Decits in Schizophrenia Can Arise From Inhibitory Dysfunction in Thalamus or Cortex

John, Y. J., Zikopoulos, B., Bullock, D. H., & Barbas, H. (2018) · Computational Psychiatry · doi:10.1162/cpsy_a_00023 · Cited by 22

A comparative study on spiking neural network encoding schema: implemented with cloud computing

Almomani, A., Alauthman, M., Alweshah, M., Dorgham, O. M., & Albalas, F. A. (2019) · Cluster Computing · doi:10.1007/s10586-018-02891-0 · Cited by 24

Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions

Rongala, U. B., Mazzoni, A., Spanne, A., Jörntell, H., & Oddo, C. M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.11.020 · Cited by 26

Semi-Implicit and Semi-Explicit Adams-Bashforth-Moulton Methods

Вадимовна, Т. А., Искандарович, К. Т., & Бутусов, Д. (2020) · Mathematics · doi:10.3390/math8050780 · Cited by 24

Emulated muscle spindle and spiking afferents validates VLSI neuromorphic hardware as a testbed for sensorimotor function and disease

Niu, C. M., Nandyala, S. K., & Sanger, T. D. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00141 · Cited by 24

Neurones and Synapses for Systemic Models of Psychiatric Disorders

Postnova, S., Rosa, E., & Braun, H. (2010) · Pharmacopsychiatry · doi:10.1055/s-0030-1252025 · Cited by 24

Chaotic solutions in the quadratic integrate-and-fire neuron with adaptation

Zheng, G., & Arnaud Tonnelier, S. (2008) · Cognitive Neurodynamics · doi:10.1007/s11571-008-9069-6 · Cited by 24

Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial IntelligencePreprint

Frenkel, C., Bol, D., & Indiveri, G. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-217499 · Cited by 24

Real and apparent biological inspiration in cognitive architectures

Holland, O. E., Diamond, A., Marques, H. G., Mitra, B. K., & Devereux, D. (2012) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2012.07.008 · Cited by 23

Synchronization generations and transitions in two map-based neurons coupled with locally active memristor

Hu, J., Bao, H., Xu, Q., Chen, M., & Bao, B. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114993 · Cited by 23

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model

Tyagi, P. K., & Agrawal, D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108877 · Cited by 23

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT

Su, Q., Mei, S., Xing, X., Yao, M., Zhang, J., Xu, B., & Li, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106630 · Cited by 25

Spiking Neural Networks: A Comprehensive Survey of Training Methodologies, Hardware Implementations and Applications

Khan, A. H., Cao, X., Luo, C., Zhang, S., Guo, W., Katsikis, V. N., & Li, S. (2025) · Artificial Intelligence Science and Engineering · doi:10.23919/aise.2025.000013 · Cited by 27

Pattern separation in the hippocampus through the eyes of computational modeling

Chavlis, S., & Poirazi, P. (2017) · Synapse · doi:10.1002/syn.21972 · Cited by 25

Tunable Voltage-Mode Subthreshold CMOS Neuron

Ronchini, M., Zamani, M., Farkhani, H., & Moradi, F. (2020) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi49217.2020.00053 · Cited by 28

Neuromorphic analog spiking-modulator for audio signal processing

Ferreira, P. M., Nebhen, J., Klisnick, G., & Benlarbi‐Delaï, A. (2020) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-020-01729-3 · Cited by 23

A fast-computational spiking neuron model for a variety of cortical neuron

Kobayashi, R., Tsubo, Y., & Shinomoto, S. (2009) · Frontiers in Neuroinformatics · doi:10.3389/conf.neuro.11.2009.08.088

Which Model to Use for Cortical Spiking Neurons?

Izhikevich, E. M. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.832719 · Cited by 2,555

A fast-computational spiking neuron model adaptable to any cortical neuron

Kobayashi, R., Tshubo, Y., & Shinomoto, S. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-s1-p22

Which Model to Use for Cortical . . .

Izhikevich, E. M. (2004)

Novel Integrate-and-fire-like Model of Repetitive Firing in Cortical Neurons

Stevens, C. F., & Zador, A. M. (1998)