Simple model of spiking neurons

Dynamical Systems in Neuroscience

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NEURAL EXCITABILITY, SPIKING AND BURSTING

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Intrinsic firing patterns of diverse neocortical neurons

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Two networks of electrically coupled inhibitory neurons in neocortex

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Weakly Connected Neural Networks

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

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Parabolic Bursting in an Excitable System Coupled with a Slow Oscillation

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

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Resonate-and-fire neurons

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Global and partial synchronism in phase-locked loop networks

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Learning physical characteristics like animals for legged robots

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Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

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Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

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Synchronization of the small-world neuronal network with unreliable synapses

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Theoretical and Heuristic Synthesis of Digital Spiking Neurons for Spike-Pattern-Division Multiplexing

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Dynamics of the exponential integrate-and-fire model with slow currents and adaptation

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Force sensor in simulated skin and neural model mimic tactile SAI afferent spiking response to ramp and hold stimuli

Kim, E. K., Wellnitz, S. A., Bourdon, S. M., Lumpkin, E. A., & Gerling, G. J. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-45 · Cited by 30

Contingency Perception and Agency Measure in Visuo-Motor Spiking Neural Networks

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Biologically inspired reinforcement learning for mobile robot collision avoidance

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Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations

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Deciphering the Contribution of Oriens-Lacunosum/Moleculare (OLM) Cells to Intrinsic θ Rhythms Using Biophysical Local Field Potential (LFP) Models

Chatzikalymniou, A. P., & Skinner, F. K. (2018) · eNeuro · doi:10.1523/eneuro.0146-18.2018 · Cited by 32

A Neural Model of Empathic States in Attachment-Based Psychotherapy

Cittern, D., & Edalat, A. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00006 · Cited by 30

Dopamine depletion leads to pathological synchronization of distinct basal ganglia loops in the beta band

Ortone, A., Vergani, A. A., Ahmadipour, M., Mannella, R., & Mazzoni, A. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010645 · Cited by 29

Electroencephalography Studies on Marketing Stimuli: A Literature Review and Future Research Agenda

Alsharif, A. H., & Isa, S. M. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.70015 · Cited by 38

A Review of Fractional-Order Chaotic Systems of Memristive Neural Networks

Wang, C., Li, Y., Yang, G., & Deng, Q. (2025) · Mathematics · doi:10.3390/math13101600 · Cited by 30

Memristor-Based Spiking Neuromorphic Systems Toward Brain-Inspired Perception and Computing

Wang, X., Zhu, Y., Zhou, Z., Chen, X., & Jia, X. (2025) · Nanomaterials · doi:10.3390/nano15141130 · Cited by 36

Discrete neuron model based on locally active memristor: Dynamics analysis and circuit implementation

Chen, X., Sun, K., Wang, H., Liu, J., & Yao, Z. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117189 · Cited by 29

Neural Assembly Computing

Ranhel, J. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2190421 · Cited by 28

Synaptic multistability and network synchronization induced by the neuron–glial interaction in the brain

Lazarevich, I. A., Викторович, С. С., & Kazantsev, V. (2017) · Journal of Experimental and Theoretical Physics Letters · doi:10.1134/s0021364017030092 · Cited by 29

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

Li, X., Wang, W. J., Xue, F., & Song, Y. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.08.053 · Cited by 29

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

Pena, R. F. O., Zaks, M. A., & Roque, A. C. (2018) · Journal of Computational Neuroscience · doi:10.1007/s10827-018-0688-6 · Cited by 29

Cortical network effects of subthalamic deep brain stimulation in a thalamo-cortical microcircuit model

Farokhniaee, A., & Lowery, M. M. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abee50 · Cited by 29

Multi-Level Neuromorphic Devices Built on Emerging Ferroic Materials: A Review

Wang, C., Agrawal, A., Yu, E., & Roy, K. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.661667 · Cited by 28

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

Venkadesh, S., & Horn, J. D. V. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.752332 · Cited by 25

A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation

Wang, J., Peng, Z., Zhan, Y., Li, Y., Yu, G., Chong, K., & Wang, C. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3191004 · Cited by 29

Complex dynamics and autapse-modulated information patterns in memristive Wilson neurons

Njitacke, Z. T., Takembo, C. N., Koumetio, B. N., & Awrejcewicz, J. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-022-07738-3 · Cited by 29

Route to Chaos and Chimera States in a Network of Memristive Hindmarsh-Rose Neurons Model with External Excitation

Muni, S. S., Njitacke, Z. T., Feudjio, C., Fozin, T. F., & Awrejcewicz, J. (2022) · Chaos Theory and Applications · doi:10.51537/chaos.1144123 · Cited by 29

A MoS2 Hafnium Oxide Based Ferroelectric Encoder for Temporal‐Efficient Spiking Neural Network

Chien, Y., Xiang, H., Shi, Y., Duong, N. T., Li, S., & Ang, K. (2022) · Advanced Materials · doi:10.1002/adma.202204949 · Cited by 30

Complex dynamics of a heterogeneous network of Hindmarsh-Rose neurons

Njitacke, Z. T., Muni, S. S., Seth, S., Awrejcewicz, J., & Kengne, J. (2023) · Physica Scripta · doi:10.1088/1402-4896/acbdd1 · Cited by 29

Unconventional computing based on magnetic tunnel junction

Cai, B., He, Y., Xin, Y., Yuan, Z., Zhang, X., Zhu, Z., & Liang, G. (2023) · Applied Physics A · doi:10.1007/s00339-022-06365-4 · Cited by 34

A universal workflow for creation, validation, and generalization of detailed neuronal models

Reva, M., Rössert, C., Arnaudon, A., Damart, T., Mandge, D., Tuncel, A., Ramaswamy, S., Markram, H., & Geit, W. A. H. V. (2023) · Patterns · doi:10.1016/j.patter.2023.100855 · Cited by 32

Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states

Munn, B., Müller, E. J., Medel, V., Naismith, S. L., Lizier, J. T., Sanders, R. D., & Shine, J. M. (2023) · Nature Communications · doi:10.1038/s41467-023-42465-2 · Cited by 29

Implementing Neural Models in Silicon

Smith, L. S. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/0-387-27705-6_13 · Cited by 32

Investigating the Neural Correlates of Pathological Cortical Networks in Alzheimer's Disease Using Heterogeneous Neuronal Models

Abuhassan, K., Coyle, D., & Maguire, L. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2181843 · Cited by 29

SpiNNaker: Enhanced multicast routing

Navaridas, J., Luján, M., Plana, L. A., Temple, S., & Furber, S. B. (2015) · Parallel Computing · doi:10.1016/j.parco.2015.01.002 · Cited by 29

Linking brain dynamics, neural mechanisms, and deep brain stimulation in Parkinson's disease: An integrated perspective

Modolo, J., & Beuter, A. C. (2009) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2009.01.005 · Cited by 30

Model-Driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity

Antonietti, A., Casellato, C., D'Angelo, E. U., & Pedrocchi, A. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2598190 · Cited by 29

NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips

Venceslai, V., Marchisio, A., Alouani, I., Martina, M., & Shafique, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207351 · Cited by 34

Spiking and Bursting Firing Patterns of a Compact VLSI Cortical Neuron Circuit

Wijekoon, J. H. B., & Dudek, P. (2007) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2007.4371151 · Cited by 29

Integrate-and-Fire models with adaptation are good enough: predicting spike times under random current injection

Jolivet, R., Rauch, A., Lüscher, H., & Gerstner, W. (2005) · UCL Discovery (University College London) · Cited by 29

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Pham, M. D., D’Angiulli, A., Dehnavi, M. M., & Chhabra, R. (2023) · Brain Sciences · doi:10.3390/brainsci13091316 · Cited by 30

Recent Progress in Tactile Sensing and Machine Learning for Texture Perception in Humanoid Robotics

Yu, L., & Liu, D. (2024) · Interdisciplinary materials · doi:10.1002/idm2.12233 · Cited by 32

Managing the FPGA memory wall: Custom computing or vector processing?

Naylor, M. F., Fox, P. J., Markettos, A. T., & Moore, S. W. (2013) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl.2013.6645538 · Cited by 28

Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke

Butz, M., Steenbuck, I. D., & van Ooyen, A. (2014) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2014.00115 · Cited by 31

MODELING FLUCTUATIONS IN DEFAULT-MODE BRAIN NETWORK USING A SPIKING NEURAL NETWORK

Yamanishi, T., Liu, J., & Nishimura, H. (2012) · International Journal of Neural Systems · doi:10.1142/s0129065712500165 · Cited by 28

A novel digital implementation of neuron–astrocyte interactions

Nazari, S., Karim Faez, A., Amiri, M., & Karami, E. (2014) · Journal of Computational Electronics · doi:10.1007/s10825-014-0643-3 · Cited by 28

PRESYNAPTIC LEARNING AND MEMORY WITH A PERSISTENT FIRING NEURON AND A HABITUATING SYNAPSE: A MODEL OF SHORT TERM PERSISTENT HABITUATION

Ramanathan, K., Ning, N., Dhanasekar, D., Li, G., Shi, L., & Vadakkepat, P. (2012) · International Journal of Neural Systems · doi:10.1142/s0129065712500153 · Cited by 28

A Computational Model Based on Multi-Regional Calcium Imaging Represents the Spatio-Temporal Dynamics in a Caenorhabditis elegans Sensory Neuron

Kuramochi, M., & Doi, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0168415 · Cited by 29

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Fang, Y., Wang, Z., Gomez, J. T., Datta, S., Khan, A. I., & Raychowdhury, A. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00855 · Cited by 30

Robotic arm controlling based on a spiking neural circuit and synaptic plasticity

Wei, H., Bu, Y., & Zhu, Z. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101640 · Cited by 28

Emergence of Mixed Mode Oscillations in Random Networks of Diverse Excitable Neurons: The Role of Neighbors and Electrical Coupling

Ghosh, S. K., Mondal, A., Ji, P., Mishra, A., Dana, S. K., Antonopoulos, C. G., & Hens, C. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00049 · Cited by 28

High Performance Simulation of Spiking Neural Network on GPGPUs

Qu, P., Zhang, Y., Fei, X., & Zheng, W. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2994123 · Cited by 28

Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling

Bernal, S. L., Celdrán, A. H., Maimó, L. F., Barros, M. T., Balasubramaniam, S., & Pérez, G. M. (2020) · IEEE Access · doi:10.1109/access.2020.3017394 · Cited by 28

Concept of LIF Neuron Circuit for Rate Coding in Spike Neural Networks

Velichko, A., & Boriskov, P. (2019) · IEEE Transactions on Circuits and Systems - II - Express Briefs · doi:10.1109/tcsii.2020.2997117 · Cited by 21

Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario

Manna, D. L., Vicente-Sola, A., Kirkland, P., Bihl, T., & Caterina, G. D. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac999b · Cited by 28

A generalized analog implementation of piecewise linear neuron models using CCII building blocks

Soleimani, H., Ahmadi, A., Bavandpour, M., & Sharifipoor, O. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.12.004 · Cited by 28

Relating ion channel expression, bifurcation structure, and diverse firing patterns in a model of an identified motor neuron

Herrera-Valdez, M. A., McKiernan, E. C., Berger, S. D., Ryglewski, S., Duch, C., & Crook, S. M. (2012) · Journal of Computational Neuroscience · doi:10.1007/s10827-012-0416-6 · Cited by 30

Interactions of motion and form in visual cortex – A neural model

Beck, C., & Heiko Neumann, B. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.11.005 · Cited by 28

A Dynamic Circuit Hypothesis for the Pathogenesis of Blepharospasm

Peterson, D. A., & B, T. J. S. A. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00011 · Cited by 27

Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types

Venkadesh, S., Komendantov, A. O., Listopad, S., Scott, E. O., Jong, K. A. D., Krichmar, J. L., & Ascoli, G. A. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00008 · Cited by 32

A neuron–astrocyte transistor-like model for neuromorphic dressed neurons

Valenza, G., Pioggia, G., Armato, A., Ferro, M., Scilingo, E. P., & Rossi, D. D. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.03.013 · Cited by 28

A CMOS circuit implementation of a spiking neuron with bursting and adaptation on a biological timescale

Wijekoon, J. H. B., & Dudek, P. (2009) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2009.5372050 · Cited by 28

Models for Global Synchronization in CPG-based Locomotion

Seo, K., & Slotine, J.-J. E. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363800 · Cited by 28

Analysis of the dynamical behavior of discrete memristor-coupled scale-free neural networks

Deng, W., & Ma, M. (2024) · Chinese Journal of Physics · doi:10.1016/j.cjph.2024.08.033 · Cited by 27

Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems

Cachón, A., & Vazquez, R. A. (2014) · Neurocomputing · doi:10.1016/j.neucom.2012.11.059 · Cited by 27

Training Spiking Neural Models Using Artificial Bee Colony

Vazquez, R. A., & Garro, B. A. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/947098 · Cited by 27

Modeling the insect mushroom bodies: Application to a delayed match-to-sample task

Arena, P., Patané, L., Stornanti, V., Termini, P. S., Zäpf, B., & Strauß, R. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.11.013 · Cited by 27

Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations

Matias, F. S., Carelli, P. V., Mirasso, C. R., & Copelli, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140504 · Cited by 28

Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise

Nobukawa, S., & Nishimura, H. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500409 · Cited by 27

Electrical and Optical Activation of Mesoscale Neural Circuits with Implications for Coding

Millard, D. C., Whitmire, C. J., Gollnick, C., Rozell, C. J., & Stanley, G. B. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5045-14.2015 · Cited by 27

A symbolic information approach to determine anticipated and delayed synchronization in neuronal circuit models

Montani, F., Rosso, O. A., Matias, F. S., Bressler, S. L., & Mirasso, C. R. (2015) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2015.0110 · Cited by 27

Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks

Luo, J., Nikolić, K., Evans, B. D., Dong, N., Sun, X., András, P., Yakovlev, A., & Degenaar, P. A. (2016) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2571339 · Cited by 27

NIM: An HMC-Based Machine for Neuron Computation

Junior, G. F. D. O., Santos, P. C., Alves, M. A. Z., & Carro, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56258-2_3 · Cited by 27

Navigating mobile robots to target in near shortest time using reinforcement learning with spiking neural networks

Mahadevuni, A., & Peng, L. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966127 · Cited by 27

Field coupling-induced pattern formation in two-layer neuronal network

Qin, H., Wang, C., Cai, N., An, X., & Alzahrani, F. S. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.02.063 · Cited by 27

An Efficient Spiking Neuron Hardware System Based on the Hardware-Oriented Modified Izhikevich Neuron (HOMIN) Model

Leigh, A. J., Mirhassani, M., & Muscedere, R. (2020) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2020.2984932 · Cited by 29

A computational model‐based analysis of basal ganglia pathway changes in Parkinson’s disease inferred from resting‐state fMRI

Maith, O., Villagrasa, F., Dinkelbach, H. Ü., Baladron, J., Horn, A., Irmen, F., Kühn, A. A., & Hamker, F. H. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14868 · Cited by 26

BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons

Li, Y., Zhao, D., & Zeng, Y. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.991851 · Cited by 33

Event Stream Super-Resolution via Spatiotemporal Constraint Learning

Li, S., Feng, Y., Li, Y., Jiang, Y. Y., Zou, C., & Gao, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00444 · Cited by 27

The Receptron: a device for the implementation of information processing systems based on complex nanostructured systems

Martini, G., Mirigliano, M., Paroli, B., & Milani, P. (2022) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ac665c · Cited by 29

Loss of neuron network coherence induced by virus-infected astrocytes: a model study

Викторович, С. С., Hramov, A. E., & Kazantsev, V. (2023) · Scientific Reports · doi:10.1038/s41598-023-33622-0 · Cited by 27

Spiking neural predictive coding for continually learning from data streams

Ororbia, A. G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126292 · Cited by 43

A multiplier-free Rulkov neuron under memristive electromagnetic induction: Dynamics analysis, energy calculation, and circuit implementation.

Zhang, S.-H., Wang, C., Zhang, H.-L., & Lin, H.-R. (2023) · Chaos · doi:10.1063/5.0160751 · Cited by 27

Hidden dynamics of memristor-coupled neurons with multi-stability and multi-transient hyperchaotic behavior

Ma, T., Mou, J., Al-Barakati, A. A., Jahanshahi, H., & Miao, M. (2023) · Physica Scripta · doi:10.1088/1402-4896/acf24f · Cited by 27

Dynamic modulation of external excitation enhance synchronization in complex neuronal network

Wu, Y., Ding, Q., Huang, W., Hu, X., Ye, Z., & Jia, Y. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114896 · Cited by 27

Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types

Tomov, P., Pena, R. F. O., Zaks, M. A., & Roque, A. C. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00103 · Cited by 26

Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors

Han, B., & Taha, T. M. (2010) · Applied Optics · doi:10.1364/ao.49.000b83 · Cited by 27

A programmable facilitating synapse device

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A low power VLSI implementation of the Izhikevich neuron model

Demirkol, A. Ş., & Özoğuz, S. (2011) · IEEE International New Circuits and Systems Conference (NEWCAS) · doi:10.1109/newcas.2011.5981282 · Cited by 27

Noise Enhanced Signaling in STDP Driven Spiking-Neuron Network

Lobov, S. A., Zhuravlev, M. О., Makarov, V. A., & Kazantsev, V. (2017) · Mathematical Modelling of Natural Phenomena · doi:10.1051/mmnp/201712409 · Cited by 27

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

Pena, R. F. O., Vellmer, S., Bernardi, D., Roque, A. C., & Lindner, B. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00009 · Cited by 26

Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm

Barrios-Avilés, J., Rosado-Muñoz, A., Medus, L. D., Bataller‐Mompeán, M., & Guerrero, J. (2018) · Sensors · doi:10.3390/s18124122 · Cited by 27

Theory of spike-train power spectra for multidimensional integrate-and-fire neurons

Vellmer, S., & Lindner, B. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.023024 · Cited by 27

A bioinspired tactile scanner for computer haptics

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An analog implementation of biologically plausible neurons using CCII building blocks

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Numerical characterization of nonlinear dynamical systems using parallel computing: The role of GPUs approach

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Neuromorphic Vibrotactile Stimulation of Fingertips for Encoding Object Stiffness in Telepresence Sensory Substitution and Augmentation Applications

Sorgini, F., Massari, L., D’Abbraccio, J., Palermo, E., Menciassi, A., Petrović, P. B., Mazzoni, A., Carrozza, M. C., Newell, F. N., & Oddo, C. M. (2018) · Sensors · doi:10.3390/s18010261 · Cited by 27

Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series

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Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

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Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data

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

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Which Model to Use for Cortical Spiking Neurons?

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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

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