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

Design principles of sensory processing in cerebellum-like structures

Roberts, P. D., & Portfors, C. V. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0217-1 · Cited by 35

A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

Wang, R. M., Hamilton, T. J., Tapson, J. C., & André, V. S. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00180 · Cited by 34

Inhibitory Networks of Fast-Spiking Interneurons Generate Slow Population Activities due to Excitatory Fluctuations and Network Multistability

Ho, E. C. Y., Strüber, M., Bartos, M., Zhang, L., & Skinner, F. K. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5446-11.2012 · Cited by 32

Detecting connectivity changes in neuronal networks

Berry, T., Hamilton, F., Peixoto, N., & SAUER, T. D. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.06.021 · Cited by 34

Integrative connectionist learning systems inspired by nature: current models, future trends and challenges

Kasabov, N. K. (2008) · Natural Computing · doi:10.1007/s11047-008-9066-z · Cited by 35

Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data

Gutzen, R., von Papen, M., Trensch, G., Quaglio, P., Grün, S., & Denker, M. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00090 · Cited by 34

VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model

Naveros, F., Luque, N. R., Ros, E., & Arleo, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2899246 · Cited by 34

Credit score classification using spiking extreme learning machine

Kuppili, V., Tripathi, D., & Edla, D. R. (2019) · Computational Intelligence · doi:10.1111/coin.12242 · Cited by 34

The role of individual neuron ion conductances in the synchronization processes of neuron networks

Boaretto, B. R. R., Manchein, C., Prado, T. D. L., & Lopes, S. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.01.019 · Cited by 34

LSMCore: A 69k-Synapse/mm 2 Single-Core Digital Neuromorphic Processor for Liquid State Machine

Wang, L., Yang, Z., Guo, S., Qu, L., Zhang, X., Kang, Z., & Xu, W. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3147380 · Cited by 34

Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning

Zhang, D., Zhang, T., Jia, S., & Xu, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19879 · Cited by 34

Three-dimensional memristive Morris–Lecar model with magnetic induction effects and its FPGA implementation

Bao, H., Yu, X., Xu, Q., Wu, H., & Bao, B. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09871-6 · Cited by 34

Virtual synaptic interconnect using an asynchronous network-on-chip

Rast, A. D., Yang, S., Khan, M. M., & Furber, S. B. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634181 · Cited by 34

The Matter of Critique: Readings in Kant's Philosophy

Rehberg, A., & Jones, R. (2000) · Cited by 34

Network models provide insights into how oriens-lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations

Ferguson, K. A., Huh, C. Y. L., Amilhon, B., Manseau, F., Williams, S., & Skinner, F. K. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00110 · Cited by 33

Quantization of Map-Based Neuronal Model for Embedded Simulations of Neurobiological Networks in Real-Time

Rulkov, N. F., Hunt, A. M., Rulkov, P. N., & Maksimov, A. (2016) · American Journal of Engineering and Applied Sciences · doi:10.3844/ajeassp.2016.973.984 · Cited by 33

Light-stimulated adaptive artificial synapse based on nanocrystalline metal-oxide film

Balashov, I. S., Chezhegov, A. А., Chizhov, A. S., Grunin, A. A., Anokhin, K. V., & Fedyanin, A. A. (2023) · Opto-Electronic Science · doi:10.29026/oes.2023.230016 · Cited by 33

Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation

Wei, W., Zhang, M., Qu, H., Belatreche, A., Zhang, J., & Chen, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00968 · Cited by 36

Neuromorphic computing for robotic vision: algorithms to hardware advances

Chowdhury, S. S., Sharma, D., Kosta, A. K., & Roy, K. (2025) · Communications Engineering · doi:10.1038/s44172-025-00492-5 · Cited by 40

Synchronization of Coupled Oscillator Dynamics

Joshi, S. K., Sen, S., & Kar, I. N. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.03.073 · Cited by 33

Development and Applications of Biomimetic Neuronal Networks Toward BrainMorphic Artificial Intelligence

Levi, T., Nanami, T., Tange, A., Aihara, K., & Kohno, T. (2018) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2018.2824827 · Cited by 33

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor

Salimi-Nezhad, N., Amiri, M., Falotico, E., & Laschi, C. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00322 · Cited by 33

Design and application examples of CMOS fractional-order differentiators and integrators

Bertsias, P., Psychalinos, C., Elwakil, A. S., Safari, L., & Mınaeı, S. (2018) · Microelectronics Journal · doi:10.1016/j.mejo.2018.11.013 · Cited by 33

A Novel Nonlinear Function Evaluation Approach for Efficient FPGA Mapping of Neuron and Synaptic Plasticity Models

Jokar, E., Abolfathi, H., & Ahmadi, A. (2019) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2019.2900943 · Cited by 33

Amortized Bayesian Model Comparison With Evidential Deep Learning

Radev, S. T., D’Alessandro, M., Mertens, U. K., Voß, A., Köthe, U., & Bürkner, P. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3124052 · Cited by 34

Dynamic expression of a HR neuron model under an electric field

Qiao, S., & An, X. (2020) · International Journal of Modern Physics B · doi:10.1142/s0217979221500247 · Cited by 33

Spin-Torque-Driven Terahertz Auto-Oscillations in Noncollinear Coplanar Antiferromagnets

Shukla, A., & Rakheja, S. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.17.034037 · Cited by 33

Collective dynamics of neuronal network under synapse and field coupling

Zhou, Q., & Wei, D. Q. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06575-0 · Cited by 33

Quantum superposition inspired spiking neural network

Sun, Y., Zeng, Y., & Zhang, T. (2021) · iScience · doi:10.1016/j.isci.2021.102880 · Cited by 37

Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia

Doborjeh, M., Doborjeh, Z., Merkin, A., Bahrami, H., Sumich, A. L., Krishnamurthi, R., Medvedev, O. N., Crook‐Rumsey, M., Morgan, C., Kirk, I. J., et al. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.09.013 · Cited by 36

Memristive FHN spiking neuron model and brain-inspired threshold logic computing

Fang, X., Duan, S.-K., & Wang, L.-D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.056 · Cited by 27

libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience

Vella, M., Cannon, R. C., Crook, S. M., Davison, A. P., Ganapathy, G., Robinson, H. P. C., Silver, R. A., & Gleeson, P. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00038 · Cited by 33

The effects of time delay on the stochastic resonance in feed-forward-loop neuronal network motifs

Liu, C., Wang, J., Yu, H., Deng, B., Tsang, K. M., Chan, W. L., & Wong, Y. K. (2013) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2013.08.021 · Cited by 32

Deterministic and Stochastic Neuronal Contributions to Distinct Synchronous CA3 Network Bursts

Takano, H., McCartney, M. L., Ortinski, P. I., Yue, C., Putt, M. E., & Coulter, D. A. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4277-11.2012 · Cited by 33

Network bursting using experimentally constrained single compartment CA3 hippocampal neuron models with adaptation

Dur-e-Ahmad, M., Nicola, W., Campbell, S. A., & Skinner, F. K. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0372-6 · Cited by 33

Fractional-order Fitzhugh-Nagumo and Izhikevich neuron models

Armanyos, M., & Radwan, A. G. (2016) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecticon.2016.7561406 · Cited by 33

Tutorial: Neuromorphic spiking neural networks for temporal learning

Jeong, D. S. (2018) · Journal of Applied Physics · doi:10.1063/1.5042243 · Cited by 35

Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: A simulation study

Kim, K. H., Kim, S., & Kim, S. J. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.06.015 · Cited by 34

Dendritic excitations govern back-propagation via a spike-rate accelerometer

Park, P., Wong-Campos, J. D., Itkis, D. G., Lee, B. H., Qi, Y., Davis, H. C., Antin, B., Pasarkar, A., Grimm, J. B., Plutkis, S. E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55819-9 · Cited by 51

A Review of Recent Developments in Neuromorphic Computing Based on Emerging Memory Devices

Sun, J., Sun, Y., Li, X., Sun, Y., Hong, Q., & Wang, C. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-11727-7 · Cited by 33

Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

Rasch, M. J., Schuch, K., Logothetis, N. K., & Maass, W. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00845.2009 · Cited by 34

Integrated circuit implementation of a cortical neuron

Wijekoon, J. H. B., & Dudek, P. (2008) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2008.4541785 · Cited by 32

Intrinsic mechanisms stabilize encoding and retrieval circuits differentially in a hippocampal network model

Hummos, A., Franklin, C. C., & Nair, S. S. (2014) · Hippocampus · doi:10.1002/hipo.22324 · Cited by 33

Analysis of a No Equilibrium Linear Resistive-Capacitive-Inductance Shunted Junction Model, Dynamics, Synchronization, and Application to Digital Cryptography in Its Fractional-Order Form

Kingni, S. T., Kuiate, G. F., Kengne, R., Tchitnga, R., & Woafo, P. (2017) · Complexity · doi:10.1155/2017/4107358 · Cited by 32

Intracellular Dynamics in Cuneate Nucleus Neurons Support Self-Stabilizing Learning of Generalizable Tactile Representations

Rongala, U. B., Spanne, A., Mazzoni, A., Bengtsson, F., Oddo, C. M., & Jörntell, H. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00210 · Cited by 38

Haptic Glove and Platform with Gestural Control For Neuromorphic Tactile Sensory Feedback In Medical Telepresence †

D’Abbraccio, J., Massari, L., Prasanna, S., Baldini, L., Sorgini, F., Farulla, G. A., Bulletti, A., Mazzoni, M., Capineri, L., Menciassi, A., et al. (2019) · Sensors · doi:10.3390/s19030641 · Cited by 31

Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity

Venkadesh, S., Komendantov, A. O., Wheeler, D. W., Hamilton, D. J., & Ascoli, G. A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007462 · Cited by 32

Maximized lateral inhibition in paired magnetic domain wall racetracks for neuromorphic computing

Cui, C., Akinola, O. G., Hassan, N., Bennett, C. H., Marinella, M. J., Friedman, J. S., & Incorvia, J. A. C. (2020) · Nanotechnology · doi:10.1088/1361-6528/ab86e8 · Cited by 32

Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition

Wang, Q., Zhang, T., Han, M., Wang, Y., Zhang, D., & Xu, B. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25081 · Cited by 32

Importance of the Cutoff Value in the Quadratic Adaptive Integrate-and-Fire Model

Touboul, J. D. (2009) · Neural Computation · doi:10.1162/neco.2009.09-08-853 · Cited by 32

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

Liu, C., Wang, J., Yu, H., Deng, B., Wei, X., Li, H., Loparo, K. A., & Fietkiewicz, C. (2015) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2015.03.018 · Cited by 32

Causal information quantification of prominent dynamical features of biological neurons

Montani, F., Baravalle, R., Montangie, L., & Rosso, O. A. (2015) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2015.0109 · Cited by 32

Synchrony in networks of coupled non-smooth dynamical systems: Extending the master stability function

Coombes, S., & Thul, R. (2016) · European Journal of Applied Mathematics · doi:10.1017/s0956792516000115 · Cited by 32

Optogenetically induced low-frequency correlations impair perception

Nandy, A. S., Nassi, J. J., Jadi, M. P., & Reynolds, J. H. (2019) · eLife · doi:10.7554/elife.35123 · Cited by 33

A Low-Energy High-Density Capacitor-Less I&F Neuron Circuit Using Feedback FET Co-Integrated With CMOS

Kwon, M.-W., Park, K., Baek, M.-H., Lee, J., & Park, B. (2019) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2019.2941917 · Cited by 32

Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons

Arnaud Tonnelier, S., Belmabrouk, H., & Martinez, D. (2007) · Neural Computation · doi:10.1162/neco.2007.19.12.3226 · Cited by 32

Accuracy and performance of the state-based Φ and liveliness measures of information integration

Gamez, D., & Aleksander, I. (2011) · Consciousness and Cognition · doi:10.1016/j.concog.2011.05.016 · Cited by 33

Emerging AI Technologies Inspiring the Next Generation of E-Textiles

Cleary, F., Srisa‐an, W., Henshall, D. C., & Balasubramaniam, S. (2023) · IEEE Access · doi:10.1109/access.2023.3282184 · Cited by 32

A Review of Computing with Spiking Neural Networks

Wu, J., Wang, Y., Li, Z., Lu, L., & Li, Q. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.047240 · Cited by 39

TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling

Zhang, S., Qu, Y., Ma, C., Wu, J., Li, H., & Tan, K. C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29625 · Cited by 31

Exploiting High Performance Spiking Neural Networks With Efficient Spiking Patterns

Shen, G., Zhao, D., & Zeng, Y. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3540408 · Cited by 34

A forecast-based STDP rule suitable for neuromorphic implementation

Davies, S., Galluppi, F., Rast, A. D., & Furber, S. B. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.018 · Cited by 31

Lyapunov exponents computation for hybrid neurons

Bizzarri, F., Brambilla, A., & Gajani, G. S. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0448-6 · Cited by 31

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

Wang, R. M., Hamilton, T. J., Tapson, J. C., & André, V. S. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00051 · Cited by 31

A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks

Zhang, X., Foderaro, G., Henriquez, C. S., & Ferrari, S. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065717500150 · Cited by 31

A real-time FPGA implementation of a biologically inspired central pattern generator network

Chen, Q., Wang, J., Yang, S., Qin, Y., Deng, B., & Wei, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.028 · Cited by 31

Contrastive analysis of neuron model

Ling-Feng, X., Li, C., & Chen, L. (2016) · Acta Physica Sinica · doi:10.7498/aps.65.240701 · Cited by 31

Unsupervised Character Recognition with a Simplified FPGA Neuromorphic System

Lammie, C., Hamilton, T. J., & Azghadi, M. R. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351532 · Cited by 31

Dynamic parallelism for synaptic updating in GPU-accelerated spiking neural network simulations

Kasap, B., & Opstal, A. J. V. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.007 · Cited by 31

Complete dynamical analysis of a neuron under magnetic flow effect

Panahi, S., Jafari, S., Khalaf, A. J. M., Rajagopal, K., Pham, V., & Alsaadi, F. E. (2018) · Chinese Journal of Physics · doi:10.1016/j.cjph.2018.08.008 · Cited by 31

Presynaptic Spike-Driven Spike Timing-Dependent Plasticity With Address Event Representation for Large-Scale Neuromorphic Systems

Park, J., & Jung, S.-D. (2020) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2020.2966884 · Cited by 31

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Strohmer, B., Manoonpong, P., & Larsen, L. B. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.00041 · Cited by 31

Observer design for hybrid dynamical systems with approximately known jump times

Bernard, P., & Sanfelice, R. G. (2022) · Automatica · doi:10.1016/j.automatica.2022.110225 · Cited by 31

Tactile Surface Roughness Categorization With Multineuron Spike Train Distance

Yi, Z., Xu, T., Guo, S., Shang, W., & Wu, X. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3021742 · Cited by 31

A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks

Zahra, O., Navarro-Alarcón, D., & Tolu, S. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500283 · Cited by 30

Spatial Memory in a Spiking Neural Network with Robot Embodiment

Lobov, S. A., Zharinov, A. I., Makarov, V. A., & Kazantsev, V. (2021) · Sensors · doi:10.3390/s21082678 · Cited by 30

Second-order information bottleneck based spiking neural networks for sEMG recognition

Zhang, A., Niu, Y., Gao, Y., Wu, J., & Gao, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2021.11.065 · Cited by 31

Neuronal Cultures: Exploring Biophysics, Complex Systems, and Medicine in a Dish

Soriano, J. (2023) · Biophysica · doi:10.3390/biophysica3010012 · Cited by 33

Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex

Chou, T.-S., Bucci, L. D., & Krichmar, J. L. (2015) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2015.00006 · Cited by 31

Silicon neuron: digital hardware implementation of the quartic model

Grassia, F., Levi, T., Kohno, T., & Saïghi, S. (2014) · Artificial Life and Robotics · doi:10.1007/s10015-014-0160-2 · Cited by 31

Izhikevich Neuron Model and its Application in Pattern Recognition

Vazquez, R. A. (2010) · International Conference on Intelligent Information Processing · Cited by 31

Synchronization in heterogeneous FitzHugh-Nagumo networks with hierarchical architecture

Plotnikov, S. A., Lehnert, J., Fradkov, А. L., & Schöll, E. (2016) · Physical review. E · doi:10.1103/physreve.94.012203 · Cited by 31

Bio-inspired spiking neural network for nonlinear systems control

Fernández, J. P., Carrillo, J. A. C., Castillo, J. J., & García, J. M. V. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.04.002 · Cited by 31

Beta-Rhythm Oscillations and Synchronization Transition in Network Models of Izhikevich Neurons: Effect of Topology and Synaptic Type

Khoshkhou, M., & Montakhab, A. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00059 · Cited by 30

Neuronal morphology and network properties modulate signal propagation in multi-layer feedforward network

Li, T., Wu, Y., Yang, L., Fu, Z., & Jia, Y. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113554 · Cited by 30

Astrocyte–Neuron Interactions

Linne, M., & Jalonen, T. O. (2014) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-397897-4.00005-x · Cited by 30

CLOSED-LOOP CONTROL OF THE THALAMOCORTICAL RELAY NEURON'S PARKINSONIAN STATE BASED ON SLOW VARIABLE

Liu, C., Wang, J., CHEN, Y.-Y., Deng, B., Wei, X., & Li, H. (2013) · International Journal of Neural Systems · doi:10.1142/s0129065713500172 · Cited by 30

Weak electric fields detectability in a noisy neural network

Zhao, J. L., Deng, B., Qin, Y., Men, C., Wang, J., Wei, X., & Sun, J. (2016) · Cognitive Neurodynamics · doi:10.1007/s11571-016-9409-x · Cited by 30

Microsaccade-rhythmic modulation of neural synchronization and coding within and across cortical areas V1 and V2

Lowet, E., Gips, B., Roberts, M. J., Weerd, P. D., Jensen, O. B., & van der Eerden, J. P. J. M. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2004132 · Cited by 34

Low-Power and Low-Cost Dedicated Bit-Serial Hardware Neural Network for Epileptic Seizure Prediction System

Kueh, S. M., & Kázmierski, T. J. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2867864 · Cited by 30

Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons

Blasi, S. D., Ciba, M., Bahmer, A., & Thielemann, C. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.11.013 · Cited by 31

On the accuracy and computational cost of spiking neuron implementation

Valadez-Godínez, S., Sossa, H., & Santiago-Montero, R. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.026 · Cited by 30

Further dynamical analysis of modified Fitzhugh–Nagumo model under the electric field

Yan, B., Panahi, S., He, S., & Jafari, S. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-020-05816-y · Cited by 31

Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

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