*/** equal contribution

  1. Burkitt AN, Gilson M, van Hemmen JL (2007)
    Spike-timing-dependent plasticity for neurons with recurrent connections. Biol Cybern 96: 533-546; doi: 10.1007/s00422-007-0148-2
  2. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL (2009)
    Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks I: Input selectivity - strengthening correlated input pathways. Biol Cybern 101: 81-102; doi: 10.1007/s00422-009-0319-4
  3. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL (2009)
    Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks II: Input selectivity - symmetry breaking. Biol Cybern 101: 103-114; doi: 10.1007/s00422-009-0320-y
  4. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL (2009)
    Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks III: Partially connected neurons driven by spontaneous activity. Biol Cybern 101: 411-426; doi: 10.1007/s00422-009-0343-4
  5. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL (2009)
    Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: Structuring synaptic pathways among recurrent connections. Biol Cybern 101: 427-444; doi: 10.1007/s00422-009-0346-1
  6. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL (2010)
    Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence. Biol Cybern 103: 365-386; doi: 10.1007/s00422-010-0405-7
  7. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL (2010)
    Representation of input structure in synaptic weights by spike-timing-dependent plasticity. Phys Rev E 82: 021912; doi: 10.1103/PhysRevE.82.021912
  8. Gilson M, Burkitt AN, van Hemmen JL (2010)
    STDP in recurrent neuronal networks. Front Comput Neurosci 4: 23; doi: 10.3389/fncom.2010.00023
  9. Gilson M, Fukai T (2011)
    Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma. PLoS ONE 6: e25339; doi: 10.1371/journal.pone.0025339
  10. Gilson M*, Masquelier T*, Hugues E (2011)
    STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7: e1002231; doi: 10.1371/journal.pcbi.1002231
  11. Gilson M*, Bürck M*, Burkitt AN, van Hemmen JL (2012)
    Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity. Neural Comput 24: 2251-2279; doi: 10.1162/NECO_a_00331
  12. Gilson M, Fukai T, Burkitt AN (2012)
    Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity. PLoS Comput Biol 8: e1002584; doi: 10.1371/journal.pcbi.1002584
  13. Kerr RR, Burkitt AN, Thomas DA, Gilson M, Grayden DB (2013)
    Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs. PLoS Comput Biol 9: e1002897; doi: 10.1371/journal.pcbi.1002897
  14. Vogels TP, Froemke RC, Doyon N, Gilson M, Haas JS, Liu R, Maffei A, Miller P, Wierenga CJ, Woodin MA, Zenke F, Sprekeler H (2013)
    Inhibitory synaptic plasticity: spike timing-dependence and putative network function. Front Neural Circuits 7: 119; doi: 10.3389/fncir.2013.00119
  15. Kerr RR, Grayden DB, Thomas DA, Gilson M, Burkitt AN (2014)
    Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates. PLoS ONE 9: e87123; doi: 10.1371/journal.pone.0087123
  16. Kerr RR, Grayden DB, Thomas DA, Gilson M, Burkitt AN (2014)
    Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence. Front Neural Circuits 8: 94; doi: 10.3389/fncir.2014.00094
  17. Kleberg FI, Fukai T, Gilson M (2014)
    Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity. Front Comput Neurosci 8: 53; doi: 10.3389/fncom.2014.00053
  18. Borovkov K, Decrouez G, Gilson M (2014)
    On stationary distributions of stochastic neural networks. J Appl Probab 51: 837-857; doi: 10.1239/jap/1409932677
  19. Yger P, Gilson M (2015)
    Models of metaplasticity: a review of concepts. Front Comput Neurosci 9: 138; doi: 10.3389/fncom.2015.00138
  20. Gilson M, Moreno-Bote R, Ponce-Alvarez A, Ritter P, Deco G (2016)
    Estimation of directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome. PLoS Comput Biol 12: e1004762; doi: 10.1371/journal.pcbi.1004762
  21. Gilson M*, Tauste Campo A*, Chen X, Thiele A, Deco G (2017)
    Non-parametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data. Netw Neurosci 1: 357-380; doi: 10.1162/NETN_a_00019
  22. Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G (2017)
    Resting state networks in empirical and simulated dynamic functional connectivity. Neuroimage 159: 388-402 doi: 10.1016/j.neuroimage.2017.07.065
  23. Rolls ET*, Cheng W*, Gilson M*, Qiu J*, Hu Z*, Ruan H, Li Y, Huang C-C, Yang AC, Tsai S-J, Zhang X, Zhuang K, Lin C-P, Deco G, Xie P, Feng J (2018)
    Effective connectivity in depression. Biol Psychiatry CNNI 3: 187-197; doi: 10.1016/j.bpsc.2017.10.004
  24. Gilson M (2018)
    Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective. Biol Cybern 112: 153-161; doi: 10.1007/s00422-017-0741-y; biorxiv preprint
  25. Senden M*, Reuter N*, van den Heuvel M, Goebel R, Deco G, Gilson M (2018)
    Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication. Hum Brain Mapp 39: 1246-1262; doi: 10.1002/hbm.23913; biorxiv preprint
  26. Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G (2018)
    Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance. Neuroimage 171: 40-54; doi: 10.1016/j.neuroimage.2017.12.074
  27. Pallarés V*, Insabato A*, Sanjuán A, Kühn S, Mantini D, Deco G**, Gilson M** (2018)
    Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity. Neuroimage 178: 238-254; doi: 10.1016/j.neuroimage.2018.04.070
  28. Gilson M, Deco G, Friston K, Hagmann P, Mantini D, Betti V, Romani GL, Corbetta M (2018)
    Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions. Neuroimage 180: 534-546; doi: 10.1016/j.neuroimage.2017.09.061
  29. Gilson M, Kouvaris NE, Deco G, Zamora-López G (2018)
    Framework based on communicability and flow to analyze complex network dynamics. Phys Rev E 97: 052301; doi: 10.1103/PhysRevE.97.052301; arxiv preprint
  30. Demirtaş M, Ponce-Alvarez A, Gilson M, Hagmann P, Mantini D, Betti V, Romani GL, Friston K, Corbetta M, Deco G (2019)
    Distinct modes of functional connectivity induced by movie-watching. Neuroimage 184: 335-348; doi: 10.1016/j.neuroimage.2018.09.042
  31. Gilson M, Kouvaris NE, Deco G, Mangin J-F, Poupon C, Lefranc S, Rivière D, Zamora-López G (2019)
    Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability. Neuroimage 201: 116007; doi: 10.1016/j.neuroimage.2019.116007; biorxiv preprint
  32. Rolls ET, Zhou Y, Cheng W, Gilson M, Deco G, Feng J (2020)
    Effective connectivity in autism. Autism Res 13: 32-44; doi: 10.1002/aur.2235
  33. Rolls ET, Cheng W, Gilson M, Gong W, Deco G, Lo CZ, Yang AC, Tsai SJ, Liu ME, Lin CP, Feng J (2020)
    Beyond the disconnectivity hypothesis of schizophrenia. Cereb Cortex 30: 1213-1233; doi: 10.1093/cercor/bhz161
  34. Gilson M, Zamora-López G, Pallarés V, Adhikari MH, Senden M, Tauste Campo A, Mantini D, Corbetta M, Deco G, Insabato A (2020)
    Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Netw Neurosci 4: 338-373; doi: 10.1162/netn_a_00117
  35. Gravel N, Renken RJ, Harvey BM, Deco G, Cornelissen FW, Gilson M (2020)
    Propagation of BOLD activity reveals directed interactions across human visual cortex. Cereb Cortex 30: 5899–5914; doi: 10.1093/cercor/bhaa165; biorxiv preprint
  36. Gilson M, Pfister J-P (2020)
    Propagation of moments in Hawkes networks. SIAM J Appl Dyn Syst 19: 828–859; doi: 10.1137/18M1220030
  37. Gilson M*, Dahmen D*, Moreno-Bote R, Insabato A, Helias M (2020)
    The covariance perceptron: A new framework for classification and processing of time series in recurrent neural networks. PLoS Comput Biol 16: e1008127; doi: 10.1371/journal.pcbi.1008127
  38. Dahmen D, Gilson M**, Helias M** (2020)
    Capacity of the covariance perceptron. J Phys A 53: 354002; doi: 10.1088/1751-8121/ab82dd
  39. Adhikari MH, Griffis J, Siegel JS, Thiebaut de Schotten M, Deco G, Instabato A, Gilson M**, Corbetta M** (2021)
    Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke. Brain Commun 3: fcab233; doi: 10.1093/braincomms/fcab233; medrxiv preprint
  40. De Filippi E, Escrichs A, Càmara E, Garrido C, Marins T, Sánchez-Fibla M, Gilson M, Deco G (2022)
    Meditation-induced effects on whole-brain structural and effective connectivity. Brain Struct Funct 227: 2087–2102; doi: 10.1007/s00429-022-02496-9
  41. De Filippi E, Marins T, Escrichs A, Gilson M, Moll J, Tovar-Moll F, Deco G (2022)
    One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity. Cereb Cortex Commun 3: tgac027; doi: 10.1093/texcom/tgac027
  42. Kobeleva X, Varoquaux G, Dagher A, Adhikari M, Grefkes CC, Gilson M (2022)
    Advancing brain network models to reconcile functional neuroimaging and clinical research. Neuroimage Clin 36: 103262; doi: 10.1016/j.nicl.2022.103262; psyarxiv preprint
  43. Gilson M, Tagliazucchi E, Cofré R (2023)
    Entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain. Phys Rev E 107: 024121
  44. Panda R, López-González A, Gilson M, Gosseries O, Thibaut A, Frasso G, Cecconi B, Escrichs A, GIGA group collaborators, Deco G, Laureys S, Zamora-López G, Annen J (2023)
    Whole‐brain analyses indicate the impairment of posterior integration and thalamo‐frontotemporal broadcasting in disorders of consciousness. Hum Brain Mapp 44: 4352–4371; doi: 10.1002/hbm.26386
  45. Nestler S, Helias M**, Gilson M** (2023)
    Neuronal architecture extracts statistical temporal patterns. Phys Rev Res 5: 033177; doi: 10.1103/PhysRevResearch.5.033177
  46. Zamora-López G, Gilson M (accepted)
    An integrative dynamical perspective for graph theory and the study of complex networks. Chaos; arxiv preprint


  1. Nestler S, Keup C, Dahmen D, Gilson M, Rauhut H, Helias M (2023)
    Unfolding recurrence by Green's functions for optimized reservoir computing. NeurIPS; link to proceedings arxiv preprint
  2. Lawrie S, Moreno-Bote R, Gilson M (2022)
    Covariance features improve low-resource reservoir computing performance in multivariate time series classification. 5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2021)


  1. Insabato A, Deco G, Gilson M (2019)
    Imaging Connectomics and the Understanding of Brain Diseases. in Adv Exp Med Biol; 1192: 139-158; doi: 10.1007/978-981-32-9721-0_8