Publications of Nicolas Brunel    :recent first  alphabetical  by type  by tags  bibtex listing:

Papers Published
  1. Feng, Y; Brunel, N, Attractor neural networks with double well synapses., PLoS Comput Biol, vol. 20 no. 2 (February, 2024), pp. e1011354 [doi] [abs] .
  2. Sanzeni, A; Palmigiano, A; Nguyen, TH; Luo, J; Nassi, JJ; Reynolds, JH; Histed, MH; Miller, KD; Brunel, N, Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys., Neuron, vol. 111 no. 24 (December, 2023), pp. 4102-4115.e9 [2022.07.13.499597v1], [doi] [abs] .
  3. Bachschmid-Romano, L; Hatsopoulos, NG; Brunel, N, Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex., Elife, vol. 12 (May, 2023) [2022.02.19.481140v1], [doi] [abs] .
  4. Goldt, S; Krzakala, F; Zdeborová, L; Brunel, N, Bayesian reconstruction of memories stored in neural networks from their connectivity., PLoS Comput Biol, vol. 19 no. 1 (January, 2023), pp. e1010813 [2105.07416], [doi] [abs] .
  5. Pereira-Obilinovic, U; Aljadeff, J; Brunel, N, Forgetting Leads to Chaos in Attractor Networks, Physical Review X, vol. 13 no. 1 (January, 2023) [2112.00119], [doi] [abs] .
  6. Brunel, N; Monasson, R; Sompolinsky, H; Leo van Hemmen, J, From the Statistical Physics of Disordered Systems to Neuroscience, in Spin Glass Theory and Far Beyond: Replica Symmetry Breaking after 40 Years (January, 2023), pp. 499-521 [doi] [abs] .
  7. De Pittà, M; Brunel, N, Multiple forms of working memory emerge from synapse-astrocyte interactions in a neuron-glia network model., Proc Natl Acad Sci U S A, vol. 119 no. 43 (October, 2022), pp. e2207912119 [doi] [abs] .
  8. Abed Zadeh, A; Turner, BD; Calakos, N; Brunel, N, Non-monotonic effects of GABAergic synaptic inputs on neuronal firing., PLoS Comput Biol, vol. 18 no. 6 (June, 2022), pp. e1010226 [doi] [abs] .
  9. Feng, Y; Brunel, N, Storage capacity of networks with discrete synapses and sparsely encoded memories., Phys Rev E, vol. 105 no. 5-1 (May, 2022), pp. 054408 [doi] [abs] .
  10. Sanzeni, A; Histed, MH; Brunel, N, Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons., Phys Rev X, vol. 12 no. 1 (2022) [doi] [abs] .
  11. Feng, Y; Brunel, N, Storage capacity of networks with discrete synapses and sparsely encoded memories (December, 2021) [abs] .
  12. Pereira-Obilinovic, U; Aljadeff, J; Brunel, N, Forgetting leads to chaos in attractor networks (November, 2021) [abs] .
  13. Aljadeff, J; Gillett, M; Pereira Obilinovic, U; Brunel, N, From synapse to network: models of information storage and retrieval in neural circuits., Curr Opin Neurobiol, vol. 70 (October, 2021), pp. 24-33 [doi] [abs] .
  14. Goldt, S; Krzakala, F; Zdeborová, L; Brunel, N, Bayesian reconstruction of memories stored in neural networks from their connectivity, PLOS Computational Biology 19(1): e1010813 2023 (May, 2021) [abs] .
  15. Inglebert, Y; Aljadeff, J; Brunel, N; Debanne, D, Synaptic plasticity rules with physiological calcium levels., Proc Natl Acad Sci U S A, vol. 117 no. 52 (December, 2020), pp. 33639-33648 [doi] [abs] .
  16. Gillett, M; Pereira, U; Brunel, N, Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning., Proc Natl Acad Sci U S A, vol. 117 no. 47 (November, 2020), pp. 29948-29958 [doi] [abs] .
  17. Sanzeni, A; Histed, MH; Brunel, N, Emergence of irregular activity in networks of strongly coupled conductance-based neurons (September, 2020) [abs] .
  18. Sanzeni, A; Histed, MH; Brunel, N, Response nonlinearities in networks of spiking neurons., PLoS Comput Biol, vol. 16 no. 9 (September, 2020), pp. e1008165 [doi] [abs] .
  19. Sanzeni, A; Akitake, B; Goldbach, HC; Leedy, CE; Brunel, N; Histed, MH, Inhibition stabilization is a widespread property of cortical networks., Elife, vol. 9 (June, 2020) [doi] [abs] .
  20. Fore, TR; Taylor, BN; Brunel, N; Hull, C, Acetylcholine Modulates Cerebellar Granule Cell Spiking by Regulating the Balance of Synaptic Excitation and Inhibition., J Neurosci, vol. 40 no. 14 (April, 2020), pp. 2882-2894 [doi] [abs] .
  21. Oleskiw, TD; Bair, W; Shea-Brown, E; Brunel, N, Firing rate of the leaky integrate-and-fire neuron with stochastic conductance-based synaptic inputs with short decay times (February, 2020) [abs] .
  22. Vaz, AP; Inati, SK; Brunel, N; Zaghloul, KA, Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory., Science, vol. 363 no. 6430 (March, 2019), pp. 975-978, American Association for the Advancement of Science (AAAS) [doi] [abs] .
  23. Pereira, U; Brunel, N, Unsupervised Learning of Persistent and Sequential Activity., Front Comput Neurosci, vol. 13 (2019), pp. 97 [doi] [abs] .
  24. Bouvier, G; Aljadeff, J; Clopath, C; Bimbard, C; Ranft, J; Blot, A; Nadal, J-P; Brunel, N; Hakim, V; Barbour, B, Cerebellar learning using perturbations., Elife, vol. 7 (November, 2018), pp. e31599 [doi] [abs] .
  25. Pereira, U; Brunel, N, Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data., Neuron, vol. 99 no. 1 (July, 2018), pp. 227-238.e4 [doi] [abs] .
  26. Martí, D; Brunel, N; Ostojic, S, Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks., Phys Rev E, vol. 97 no. 6-1 (June, 2018), pp. 062314 [doi] [abs] .
  27. Tartaglia, EM; Brunel, N, Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons., Sci Rep, vol. 7 no. 1 (September, 2017), pp. 11916 [doi] [abs] .
  28. Titley, HK; Brunel, N; Hansel, C, Toward a Neurocentric View of Learning., Neuron, vol. 95 no. 1 (July, 2017), pp. 19-32 [doi] [abs] .
  29. Zampini, V; Liu, JK; Diana, MA; Maldonado, PP; Brunel, N; Dieudonné, S, Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit., Elife, vol. 5 (September, 2016) [doi] [abs] .
  30. De Pittà, M; Brunel, N; Volterra, A, Astrocytes: Orchestrating synaptic plasticity?, Neuroscience, vol. 323 (May, 2016), pp. 43-61 [doi] [abs] .
  31. Brunel, N, Is cortical connectivity optimized for storing information?, Nat Neurosci, vol. 19 no. 5 (May, 2016), pp. 749-755 [doi] [abs] .
  32. Bouvier, G; Higgins, D; Spolidoro, M; Carrel, D; Mathieu, B; Léna, C; Dieudonné, S; Barbour, B; Brunel, N; Casado, M, Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors., Cell Rep, vol. 15 no. 1 (April, 2016), pp. 104-116 [doi] [abs] .
  33. Dubreuil, AM; Brunel, N, Storing structured sparse memories in a multi-modular cortical network model., J Comput Neurosci, vol. 40 no. 2 (April, 2016), pp. 157-175 [doi] [abs] .
  34. De Pittà, M; Brunel, N, Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study., Neural Plast, vol. 2016 (2016), pp. 7607924 [doi] [abs] .
  35. Brunel, N, Basic Neuron and Network Models, in FROM NEURON TO COGNITION VIA COMPUTATIONAL NEUROSCIENCE (2016), pp. 73-99 .
  36. Lim, S; McKee, JL; Woloszyn, L; Amit, Y; Freedman, DJ; Sheinberg, DL; Brunel, N, Inferring learning rules from distributions of firing rates in cortical neurons., Nat Neurosci, vol. 18 no. 12 (December, 2015), pp. 1804-1810 [doi] [abs] .
  37. Alemi, A; Baldassi, C; Brunel, N; Zecchina, R, A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks., PLoS Comput Biol, vol. 11 no. 8 (August, 2015), pp. e1004439 [doi] [abs] .
  38. Ostojic, S; Szapiro, G; Schwartz, E; Barbour, B; Brunel, N; Hakim, V, Neuronal morphology generates high-frequency firing resonance., J Neurosci, vol. 35 no. 18 (May, 2015), pp. 7056-7068 [doi] [abs] .
  39. Tartaglia, EM; Brunel, N; Mongillo, G, Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli., PLoS Comput Biol, vol. 11 no. 2 (February, 2015), pp. e1004059 [doi] [abs] .
  40. Brunel, N; Hakim, V, Population Density Model, in Encyclopedia of Computational Neuroscience, edited by Jaeger, D; Jung, R (2015), pp. 2447-2465, Springer New York [doi] .
  41. Barbieri, F; Mazzoni, A; Logothetis, NK; Panzeri, S; Brunel, N, Stimulus dependence of local field potential spectra: experiment versus theory., J Neurosci, vol. 34 no. 44 (October, 2014), pp. 14589-14605 [doi] [abs] .
  42. Higgins, D; Graupner, M; Brunel, N, Memory maintenance in synapses with calcium-based plasticity in the presence of background activity., PLoS Comput Biol, vol. 10 no. 10 (October, 2014), pp. e1003834 [doi] [abs] .
  43. Dubreuil, AM; Amit, Y; Brunel, N, Memory capacity of networks with stochastic binary synapses., PLoS Comput Biol, vol. 10 no. 8 (August, 2014), pp. e1003727 [doi] [abs] .
  44. Clopath, C; Badura, A; De Zeeuw, CI; Brunel, N, A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice., J Neurosci, vol. 34 no. 21 (May, 2014), pp. 7203-7215 [doi] [abs] .
  45. Brunel, N; Hakim, V; Richardson, MJE, Single neuron dynamics and computation., Curr Opin Neurobiol, vol. 25 (April, 2014), pp. 149-155 [doi] [abs] .
  46. Hertäg, L; Durstewitz, D; Brunel, N, Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise., Front Comput Neurosci, vol. 8 (2014), pp. 116 [doi] [abs] .
  47. Tartaglia, EM; Mongillo, G; Brunel, N, On the relationship between persistent delay activity, repetition enhancement and priming., Front Psychol, vol. 5 (2014), pp. 1590 [doi] [abs] .
  48. Brunel, N; Hakim, V, Fokker-Planck Equation, in Encyclopedia of Computational Neuroscience (2014), pp. 1-5, Springer New York [doi] .
  49. Brunel, N, Dynamics of neural networks, in Principles of Neural Coding (January, 2013), pp. 489-512, CRC Press [doi] [abs] .
  50. Brunel, N; Hakim, V, Fokker-Planck Equation, in Encyclopedia of Computational Neuroscience, edited by Jaeger, D; Jung, R (2013), pp. 1-6, Springer New York [doi] .
  51. Clopath, C; Brunel, N, Optimal properties of analog perceptrons with excitatory weights., PLoS Comput Biol, vol. 9 no. 2 (2013), pp. e1002919 [doi] [abs] .
  52. Brunel, N; Hakim, V, Population Density Models, in Encyclopedia of Computational Neuroscience (2013), pp. 1-24, Springer New York [doi] .
  53. Graupner, M; Brunel, N, Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location., Proc Natl Acad Sci U S A, vol. 109 no. 10 (March, 2012), pp. 3991-3996 [doi] [abs] .
  54. Clopath, C; Nadal, J-P; Brunel, N, Storage of correlated patterns in standard and bistable Purkinje cell models., PLoS Comput Biol, vol. 8 no. 4 (2012), pp. e1002448 [doi] [abs] .
  55. Roxin, A; Brunel, N; Hansel, D; Mongillo, G; van Vreeswijk, C, On the distribution of firing rates in networks of cortical neurons., J Neurosci, vol. 31 no. 45 (November, 2011), pp. 16217-16226 [doi] [abs] .
  56. Ostojic, S; Brunel, N, From spiking neuron models to linear-nonlinear models., PLoS Comput Biol, vol. 7 no. 1 (January, 2011), pp. e1001056 [doi] [abs] .
  57. Hamaguchi, K; Riehle, A; Brunel, N, Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons., J Neurophysiol, vol. 105 no. 1 (January, 2011), pp. 487-500 [doi] [abs] .
  58. Ledoux, E; Brunel, N, Dynamics of networks of excitatory and inhibitory neurons in response to time-dependent inputs., Front Comput Neurosci, vol. 5 (2011), pp. 25 [doi] [abs] .
  59. Mazzoni, A; Brunel, N; Cavallari, S; Logothetis, NK; Panzeri, S, Cortical dynamics during naturalistic sensory stimulations: experiments and models., J Physiol Paris, vol. 105 no. 1-3 (2011), pp. 2-15 [doi] [abs] .
  60. Mazzoni, A; Whittingstall, K; Brunel, N; Logothetis, NK; Panzeri, S, Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model., Neuroimage, vol. 52 no. 3 (September, 2010), pp. 956-972 [doi] [abs] .
  61. Panzeri, S; Brunel, N; Logothetis, NK; Kayser, C, Sensory neural codes using multiplexed temporal scales., Trends Neurosci, vol. 33 no. 3 (March, 2010), pp. 111-120 [doi] [abs] .
  62. Graupner, M; Brunel, N, Mechanisms of induction and maintenance of spike-timing dependent plasticity in biophysical synapse models., Front Comput Neurosci, vol. 4 (2010) [doi] [abs] .
  63. Brunel, N; Lavigne, F, Semantic priming in a cortical network model., J Cogn Neurosci, vol. 21 no. 12 (December, 2009), pp. 2300-2319 [doi] [abs] .
  64. Graupner, M; Brunel, N, A bitable synaptic model with transitions between states induced by calcium dynamics: theory vs experiment, BMC Neuroscience, vol. 10 no. S1 (September, 2009), pp. O15-O15, Springer Science and Business Media LLC [doi] .
  65. Ostojic, S; Brunel, N; Hakim, V, How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains., J Neurosci, vol. 29 no. 33 (August, 2009), pp. 10234-10253 [doi] [abs] .
  66. Ostojic, S; Brunel, N; Hakim, V, Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities., J Comput Neurosci, vol. 26 no. 3 (June, 2009), pp. 369-392 [doi] [abs] .
  67. Zillmer, R; Brunel, N; Hansel, D, Very long transients, irregular firing, and chaotic dynamics in networks of randomly connected inhibitory integrate-and-fire neurons., Phys Rev E Stat Nonlin Soft Matter Phys, vol. 79 no. 3 Pt 1 (March, 2009), pp. 031909 [doi] [abs] .
  68. Dugué, GP; Brunel, N; Hakim, V; Schwartz, E; Chat, M; Lévesque, M; Courtemanche, R; Léna, C; Dieudonné, S, Electrical coupling mediates tunable low-frequency oscillations and resonance in the cerebellar Golgi cell network., Neuron, vol. 61 no. 1 (January, 2009), pp. 126-139 [doi] [abs] .
  69. Brunel, N; Hakim, V, Neuronal Dynamics, in Encyclopedia of Complexity and Systems Science, edited by Meyers, RA (2009), pp. 6099-6116, Springer New York [doi] .
  70. Brunel, N, Modeling Point Neurons: From Hodgkin-Huxley to Integrate-and-Fire, in COMPUTATIONAL MODELING METHODS FOR NEUROSCIENTISTS (2009), pp. 161-185 .
  71. Mazzoni, A; Panzeri, S; Logothetis, NK; Brunel, N, Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons., PLoS Comput Biol, vol. 4 no. 12 (December, 2008), pp. e1000239 [doi] [abs] .
  72. Roxin, A; Hakim, V; Brunel, N, The statistics of repeating patterns of cortical activity can be reproduced by a model network of stochastic binary neurons., J Neurosci, vol. 28 no. 42 (October, 2008), pp. 10734-10745 [doi] [abs] .
  73. Barbieri, F; Brunel, N, Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex?, Front Neurosci, vol. 2 no. 1 (July, 2008), pp. 114-122 [doi] [abs] .
  74. de Solages, C; Szapiro, G; Brunel, N; Hakim, V; Isope, P; Buisseret, P; Rousseau, C; Barbour, B; Léna, C, High-frequency organization and synchrony of activity in the purkinje cell layer of the cerebellum., Neuron, vol. 58 no. 5 (June, 2008), pp. 775-788 [doi] [abs] .
  75. Brunel, N; Hakim, V, Sparsely synchronized neuronal oscillations., Chaos, vol. 18 no. 1 (March, 2008), pp. 015113 [doi] [abs] .
  76. Brunel, N, Daniel Amit (1938-2007)., Network, vol. 19 no. 1 (2008), pp. 3-8 [doi] .
  77. Battaglia, D; Brunel, N; Hansel, D, Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation., Phys Rev Lett, vol. 99 no. 23 (December, 2007), pp. 238106 [doi] [abs] .
  78. Barbour, B; Brunel, N; Hakim, V; Nadal, J-P, What can we learn from synaptic weight distributions?, Trends Neurosci, vol. 30 no. 12 (December, 2007), pp. 622-629 [doi] [abs] .
  79. Brunel, N; van Rossum, MCW, Lapicque's 1907 paper: from frogs to integrate-and-fire., Biol Cybern, vol. 97 no. 5-6 (December, 2007), pp. 337-339 [doi] [abs] .
  80. Graupner, M; Brunel, N, STDP in a bistable synapse model based on CaMKII and associated signaling pathways., PLoS Comput Biol, vol. 3 no. 11 (November, 2007), pp. e221 [doi] [abs] .
  81. Baldassi, C; Braunstein, A; Brunel, N; Zecchina, R, Efficient supervised learning in networks with binary synapses., Proc. Natl. Acad. Sci. USA, vol. 104 no. 26 (2007), pp. 11079-11084 [doi] [abs] .
  82. Barbieri, F; Brunel, N, Irregular persistent activity induced by synaptic excitatory feedback., Front Comput Neurosci, vol. 1 (2007), pp. 5 [doi] [abs] .
  83. Roxin, A; Brunel, N; Hansel, D, Rate models with delays and the dynamics of large networks of spiking neurons, Progress of Theoretical Physics Supplement, vol. 161 (June, 2006), pp. 68-85, Oxford University Press (OUP) [doi] [abs] .
  84. Brunel, N; Hansel, D, How noise affects the synchronization properties of recurrent networks of inhibitory neurons., Neural Comput, vol. 18 no. 5 (May, 2006), pp. 1066-1110 [doi] [abs] .
  85. Geisler, C; Brunel, N; Wang, X-J, Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges., J Neurophysiol, vol. 94 no. 6 (December, 2005), pp. 4344-4361 [doi] [abs] .
  86. Roxin, A; Brunel, N; Hansel, D, Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks., Phys Rev Lett, vol. 94 no. 23 (June, 2005), pp. 238103 [doi] [abs] .
  87. Fourcaud-Trocmé, N; Brunel, N, Dynamics of the instantaneous firing rate in response to changes in input statistics., J Comput Neurosci, vol. 18 no. 3 (June, 2005), pp. 311-321 [doi] [abs] .
  88. Brunel, N, Course 10 Network models of memory, vol. 80 no. C (January, 2005), pp. 407-476, Elsevier [doi] .
  89. Boucheny, C; Brunel, N; Arleo, A, A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system., J Comput Neurosci, vol. 18 no. 2 (2005), pp. 205-227 [doi] [abs] .
  90. Brunel, N; Hakim, V; Isope, P; Nadal, J-P; Barbour, B, Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell., Neuron, vol. 43 no. 5 (September, 2004), pp. 745-757 [doi] [abs] .
  91. Fourcaud-Trocmé, N; Hansel, D; van Vreeswijk, C; Brunel, N, How spike generation mechanisms determine the neuronal response to fluctuating inputs., J Neurosci, vol. 23 no. 37 (December, 2003), pp. 11628-11640 [doi] [abs] .
  92. Brunel, N, Dynamics and plasticity of stimulus-selective persistent activity in cortical network models., Cereb Cortex, vol. 13 no. 11 (November, 2003), pp. 1151-1161 [doi] [abs] .
  93. Mongillo, G; Amit, DJ; Brunel, N, Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network., Eur J Neurosci, vol. 18 no. 7 (October, 2003), pp. 2011-2024 [doi] [abs] .
  94. Brunel, N; Latham, PE, Firing rate of the noisy quadratic integrate-and-fire neuron., Neural Comput, vol. 15 no. 10 (October, 2003), pp. 2281-2306 [doi] [abs] .
  95. Brunel, N; Wang, X-J, What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance., J Neurophysiol, vol. 90 no. 1 (July, 2003), pp. 415-430 [doi] [abs] .
  96. Brunel, N; Hakim, V; Richardson, MJE, Firing-rate resonance in a generalized integrate-and-fire neuron with subthreshold resonance., Phys Rev E Stat Nonlin Soft Matter Phys, vol. 67 no. 5 Pt 1 (May, 2003), pp. 051916 [doi] [abs] .
  97. Richardson, MJE; Brunel, N; Hakim, V, From subthreshold to firing-rate resonance., J Neurophysiol, vol. 89 no. 5 (May, 2003), pp. 2538-2554 [doi] [abs] .
  98. Brunel, N; Frégnac, Y; Meunier, C; Nadal, J-P, Neuroscience and computation., J Physiol Paris, vol. 97 no. 4-6 (2003), pp. 387-390 [doi] .
  99. Fourcaud, N; Brunel, N, Dynamics of the firing probability of noisy integrate-and-fire neurons., Neural Comput, vol. 14 no. 9 (September, 2002), pp. 2057-2110 [doi] [abs] .
  100. Brunel, N; Chance, FS; Fourcaud, N; Abbott, LF, Effects of synaptic noise and filtering on the frequency response of spiking neurons., Phys Rev Lett, vol. 86 no. 10 (March, 2001), pp. 2186-2189 [doi] [abs] .
  101. Brunel, N; Wang, XJ, Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition., J Comput Neurosci, vol. 11 no. 1 (2001), pp. 63-85 [doi] [abs] .
  102. Brunel, N, Persistent activity and the single-cell frequency-current curve in a cortical network model., Network, vol. 11 no. 4 (November, 2000), pp. 261-280 [doi] [abs] .
  103. Compte, A; Brunel, N; Goldman-Rakic, PS; Wang, XJ, Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model., Cereb Cortex, vol. 10 no. 9 (September, 2000), pp. 910-923 [doi] [abs] .
  104. Brunel, N, Phase diagrams of sparsely connected networks of excitatory and inhibitory spiking neurons, Neurocomputing, vol. 32-33 (June, 2000), pp. 307-312, Elsevier BV [doi] [abs] .
  105. Brunel, N; Wang, XJ, Fast network oscillations with intermittent principal cell firing in a model of a recurrent excitatory-inhibitory circuit, EUROPEAN JOURNAL OF NEUROSCIENCE, vol. 12 (January, 2000), pp. 79-79, BLACKWELL SCIENCE LTD .
  106. Brunel, N, Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons., J Physiol Paris, vol. 94 no. 5-6 (2000), pp. 445-463 [doi] [abs] .
  107. Brunel, N, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons., J Comput Neurosci, vol. 8 no. 3 (2000), pp. 183-208 [doi] [abs] .
  108. Brunel, N; Hakim, V, Fast global oscillations in networks of integrate-and-fire neurons with low firing rates., Neural Comput, vol. 11 no. 7 (October, 1999), pp. 1621-1671 [doi] [abs] .
  109. Brunel, N; Sergi, S, Firing frequency of leaky intergrate-and-fire neurons with synaptic current dynamics., J Theor Biol, vol. 195 no. 1 (November, 1998), pp. 87-95 [doi] [abs] .
  110. Brunel, N; Nadal, JP, Mutual information, Fisher information, and population coding., Neural Comput, vol. 10 no. 7 (October, 1998), pp. 1731-1757 [doi] [abs] .
  111. Nadal, JP; Brunel, N; Parga, N, Nonlinear feedforward networks with stochastic outputs: infomax implies redundancy reduction., Network, vol. 9 no. 2 (May, 1998), pp. 207-217 [doi] [abs] .
  112. Brunel, N; Carusi, F; Fusi, S, Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network., Network, vol. 9 no. 1 (February, 1998), pp. 123-152 [doi] [abs] .
  113. Brunel, N; Nadal, JP, Modeling memory: what do we learn from attractor neural networks?, C R Acad Sci III, vol. 321 no. 2-3 (1998), pp. 249-252 [doi] [abs] .
  114. Brunel, N; Trullier, O, Plasticity of directional place fields in a model of rodent CA3., Hippocampus, vol. 8 no. 6 (1998), pp. 651-665 [doi] [abs] .
  115. Brunel, N; Ninio, J, Time to detect the difference between two images presented side by side., Brain Res Cogn Brain Res, vol. 5 no. 4 (June, 1997), pp. 273-282 [doi] [abs] .
  116. Amit, D; Brunel, N, Dynamics of a recurrent network of spiking neurons before and following learning, Network: Computation in Neural Systems, vol. 8 no. 4 (January, 1997), pp. 373-404, Informa UK Limited [doi] [abs] .
  117. Brunel, N, Cross-correlations in sparsely connected recurrent networks of spiking neurons, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1327 (January, 1997), pp. 31-36 [doi] [abs] .
  118. Amit, DJ; Brunel, N, Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex., Cereb Cortex, vol. 7 no. 3 (1997), pp. 237-252 [doi] [abs] .
  119. Brunel, N; Nadal, J-P, Optimal tuning curves for neurons spiking as a Poisson process., edited by Verleysen, M, ESANN (1997), D-Facto public .
  120. Brunel, N, Hebbian learning of context in recurrent neural networks., Neural Comput, vol. 8 no. 8 (November, 1996), pp. 1677-1710 [doi] [abs] .
  121. Ninio, J; Brunel, N, Time to detect a single difference between two correlated images, PERCEPTION, vol. 25 (January, 1996), pp. 89-89, PION LTD .
  122. Brunel, N; Zecchina, R, A SIMPLE GEOMETRICAL BOUND FOR REPLICA SYMMETRY STABILITY IN NEURAL NETWORKS MODELS, Modern Physics Letters B, vol. 09 no. 18 (August, 1995), pp. 1159-1164, World Scientific Pub Co Pte Lt [doi] .
  123. Amit, D; Brunel, N, Learning internal representations in an attractor neural network with analogue neurons, Network: Computation in Neural Systems, vol. 6 no. 3 (August, 1995), pp. 359-388, Informa UK Limited [doi] .
  124. Brunel, N; Amit, DJ, Learning internal representations in an analog attractor neural network, edited by Amit, DJ; delGiudice, P; Denby, B; Rolls, ET; Treves, A, INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, SUPPLEMENTARY ISSUE, 1995 (January, 1995), pp. 19-23, WORLD SCIENTIFIC PUBL CO PTE LTD .
  125. Brunel, N, Quantitative modeling of local Hebbian reverberations in primate cortex, edited by Amit, DJ; delGiudice, P; Denby, B; Rolls, ET; Treves, A, INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, SUPPLEMENTARY ISSUE, 1995 (January, 1995), pp. 13-17, WORLD SCIENTIFIC PUBL CO PTE LTD .
  126. Brunel, N, Storage capacity of neural networks: Effect of the fluctuations of the number of active neurons per memory, Journal of Physics A: Mathematical and General, vol. 27 no. 14 (December, 1994), pp. 4783-4789, IOP Publishing [doi] [abs] .
  127. Brunel, N, Dynamics of an attractor neural network converting temporal into spatial correlations, Network: Computation in Neural Systems, vol. 5 no. 4 (November, 1994), pp. 449-470, Informa UK Limited [doi] .
  128. Amit, DJ; Brunel, N; Tsodyks, MV, Correlations of cortical Hebbian reverberations: theory versus experiment., J Neurosci, vol. 14 no. 11 Pt 1 (November, 1994), pp. 6435-6445 [doi] [abs] .
  129. Brunel, N; Zecchina, R, Response functions improving performance in analog attractor neural networks., Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, vol. 49 no. 3 (March, 1994), pp. R1823-R1826 [doi] .
  130. Brunel, N, Effect of synapse dilution on the memory retrieval in structured attractor neural networks, Journal de Physique I, vol. 3 no. 8 (August, 1993), pp. 1693-1715, EDP Sciences [doi] .
  131. Amit, DJ; Brunel, N, Adequate input for learning in attractor neural networks, Network: Computation in Neural Systems, vol. 4 no. 2 (January, 1993), pp. 177-194, Informa UK Limited [doi] [abs] .
  132. Brunel, N; Nadal, J-P; Toulouse, G, Information capacity of a perceptron, Journal of Physics A: Mathematical and General, vol. 25 no. 19 (December, 1992), pp. 5017-5038, IOP Publishing [doi] [abs] .
Papers Submitted
  1. A Sanzeni, M Histed and N Brunel, Emergence of irregular states in networks with conductance-based synapses, Phys Rev X (2021) .
Preprints
  1. Li, Y; An, X; Qian, Y; Xu, XH; Zhao, S; Mohan, H; Bachschmid-Romano, L; Brunel, N; Whishaw, IQ; Huang, ZJ, Cortical network and projection neuron types that articulate serial order in a skilled motor behavior. (October, 2023) [doi] .

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