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Original Article

Associative memory in a network of ‘spiking’ neurons

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Pages 139-164 | Received 19 Mar 1991, Published online: 09 Jul 2009

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (11)

Christian R. Huyck. (2009) Variable binding by synaptic strength change. Connection Science 21:4, pages 327-357.
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M. Spiridon & W. Gerstner. (2001) Effect of lateral connections on the accuracy of the population code for a network of spiking neurons. Network: Computation in Neural Systems 12:4, pages 409-421.
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A.A. Koulakov. (2001) Properties of synaptic transmission and the global stability of delayed activity states. Network: Computation in Neural Systems 12:1, pages 47-74.
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Nicolas Brunel. (2000) Persistent activity and the single-cell frequency–current curve in a cortical network model. Network: Computation in Neural Systems 11:4, pages 261-280.
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Mona Spiridon & Wulfram Gerstner. (1999) Noise spectrum and signal transmission through a population of spiking neurons. Network: Computation in Neural Systems 10:3, pages 257-272.
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Gustavo Deco & Bernd Schürmann. (1998) The coding of information by spiking neurons: an analytical study. Network: Computation in Neural Systems 9:3, pages 303-317.
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Wolfgang Maass & Thomas Natschläger. (1997) Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding. Network: Computation in Neural Systems 8:4, pages 355-371.
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Martin W Simmen, Alessandro Treves & Edmund T Rolls. (1996) Pattern retrieval in threshold-linear associative nets. Network: Computation in Neural Systems 7:1, pages 109-122.
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Bo Cartling. (1995) A generalized neuronal activation function derived from ion-channel characteristics. Network: Computation in Neural Systems 6:3, pages 389-401.
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Bo Cartling. (1995) Autonomous neuromodulatory control of associative processes. Network: Computation in Neural Systems 6:2, pages 247-260.
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Evan Cudone, Amelia M. Lower & Robert A. McDougal. (2023) Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories. PLOS Computational Biology 19:10, pages e1011548.
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Grégory Dumont, Alberto Pérez-Cervera & Boris Gutkin. (2022) A framework for macroscopic phase-resetting curves for generalised spiking neural networks. PLOS Computational Biology 18:8, pages e1010363.
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Xianghong Lin, Mengwei Zhang & Xiangwen Wang. (2021) Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model. Computational Intelligence and Neuroscience 2021, pages 1-16.
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Renata Pazzini, Osame Kinouchi & Ariadne A. Costa. (2021) Neuronal avalanches in Watts-Strogatz networks of stochastic spiking neurons. Physical Review E 104:1.
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Bastian Pietras, Noé Gallice & Tilo Schwalger. (2020) Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons. Physical Review E 102:2.
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Emilio F. Galera & Osame Kinouchi. (2020) Physics of psychophysics: Large dynamic range in critical square lattices of spiking neurons. Physical Review Research 2:3.
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Oleg V. Maslennikov & Vladimir I. Nekorkin. (2020) Stimulus-induced sequential activity in supervisely trained recurrent networks of firing rate neurons. Nonlinear Dynamics 101:2, pages 1093-1103.
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Carson C. Chow & Yahya Karimipanah. (2020) Before and beyond the Wilson–Cowan equations. Journal of Neurophysiology 123:5, pages 1645-1656.
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Nan Zheng & Pinaki Mazumder. 2019. Learning in Energy‐Efficient Neuromorphic Computing. Learning in Energy‐Efficient Neuromorphic Computing 119 171 .
Tilo Schwalger & Anton V Chizhov. (2019) Mind the last spike — firing rate models for mesoscopic populations of spiking neurons. Current Opinion in Neurobiology 58, pages 155-166.
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Osame Kinouchi, Ludmila Brochini, Ariadne A. Costa, João Guilherme Ferreira Campos & Mauro Copelli. (2019) Stochastic oscillations and dragon king avalanches in self-organized quasi-critical systems. Scientific Reports 9:1.
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Ulises Pereira & Nicolas Brunel. (2018) Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data. Neuron 99:1, pages 227-238.e4.
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Sandeep Pande, Fearghal Morgan, Finn Krewer, Jim Harkin, Liam McDaid & Brian McGinley. (2016) Rapid application prototyping for hardware modular spiking neural network architectures. Neural Computing and Applications 28:9, pages 2767-2779.
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Grégory Dumont, Alexandre Payeur & André Longtin. (2017) A stochastic-field description of finite-size spiking neural networks. PLOS Computational Biology 13:8, pages e1005691.
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Ariadne Costa, Ludmila Brochini & Osame Kinouchi. (2017) Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons. Entropy 19:8, pages 399.
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Guillaume Hennequin, Everton J. AgnesTim P. Vogels. (2017) Inhibitory Plasticity: Balance, Control, and Codependence. Annual Review of Neuroscience 40:1, pages 557-579.
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Yi Ming Lai & Marc de Kamps. (2017) Population density equations for stochastic processes with memory kernels. Physical Review E 95:6.
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Sumit Bam Shrestha & Qing Song. (2017) Robust learning in SpikeProp. Neural Networks 86, pages 54-68.
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Johanni Brea & Wulfram Gerstner. (2016) Does computational neuroscience need new synaptic learning paradigms?. Current Opinion in Behavioral Sciences 11, pages 61-66.
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Skander Mensi, Olivier Hagens, Wulfram Gerstner & Christian Pozzorini. (2016) Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons. PLOS Computational Biology 12:2, pages e1004761.
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Friedemann Zenke, Everton J. Agnes & Wulfram Gerstner. (2015) Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Communications 6:1.
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Takayuki Osogami & Makoto Otsuka. (2015) Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity. Scientific Reports 5:1.
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Christian Pozzorini, Skander Mensi, Olivier Hagens, Richard Naud, Christof Koch & Wulfram Gerstner. (2015) Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models. PLOS Computational Biology 11:6, pages e1004275.
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Eleni Vasilaki, Nicolas Fr?maux, Robert Urbanczik, Walter Senn & Wulfram Gerstner. (2009) Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail. PLoS Computational Biology 5:12, pages e1000586.
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Werner M. Kistler. (2000) Stability properties of solitary waves and periodic wave trains in a two-dimensional network of spiking neurons. Physical Review E 62:6, pages 8834-8837.
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Nicolas Brunel. (2000) Persistent activity and the single-cell frequency–current curve in a cortical network model. Network: Computation in Neural Systems 11:4, pages 261-280.
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