Key publications

  • Iavarone E., Yi J., Shi Y., Zandt B.J., O’Reilly C., Van Geit W., Rössert C., Markram, H., Hill, S.L. (2019) Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLOS Computational Biology 15(5): 1-23. e1006753.
  • Hill S., How do we know what we know? Discovering neuroscience data sets through minimal metadata (2016). Nat Rev Neurosci 12, 735.
  • H. Markram, E. Muller, S. Ramaswamy, Michael W. Reimann, M. Abdellah, Carlos A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, Guy Antoine A. Kahou, Thomas K. Berger, A. Bilgili, N. Buncic, A. Chalimourda, G. Chindemi, J.-D. Courcol, F. Delalondre, V. Delattre, S. Druckmann, R. Dumusc, J. Dynes, S. Eilemann, E. Gal, Michael E. Gevaert, J.-P. Ghobril, A. Gidon, Joe W. Graham, A. Gupta, V. Haenel, E. Hay, T. Heinis, Juan B. Hernando, M. Hines, L. Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim, James G. King, Z. Kisvarday, P. Kumbhar, S. Lasserre, J.-V. Le Bé, Bruno R.C. Magalhães, A. Merchán-Pérez, J. Meystre, Benjamin R. Morrice, J. Muller, A. Muñoz-Céspedes, S. Muralidhar, K. Muthurasa, D. Nachbaur, Taylor H. Newton, M. Nolte, A. Ovcharenko, J. Palacios, L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.-R. Rodríguez, Juan L. Riquelme, C. Rössert, K. Sfyrakis, Y. Shi, Julian C. Shillcock, G. Silberberg, R. Silva, F. Tauheed, M. Telefont, M. Toledo-Rodriguez, T. Tränkler, W. Van Geit, Jafet V. Díaz, R. Walker, Y. Wang, Stefano M. Zaninetta, J. DeFelipe, Sean L. Hill, I. Segev, and F. Schürmann, Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163, 2015, 456-492.
  • S. Ramaswamy, J.-D. Courcol, M. Abdellah, S.R. Adaszewski, N. Antille, S. Arsever, G. Atenekeng, A. Bilgili, Y. Brukau, A. Chalimourda, G. Chindemi, F. Delalondre, R. Dumusc, S. Eilemann, M.E. Gevaert, P. Gleeson, J.W. Graham, J.B. Hernando, L. Kanari, Y. Katkov, D. Keller, J.G. King, R. Ranjan, M.W. Reimann, C. Rössert, Y. Shi, J.C. Shillcock, M. Telefont, W. Van Geit, J. Villafranca Diaz, R. Walker, Y. Wang, S.M. Zaninetta, J. DeFelipe, S.L. Hill, J. Muller, I. Segev, F. Schürmann, E.B. Muller, and H. Markram, The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex. Front. Neural Circuits, 2015, 44.


Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity

C. O’Reilly; E. Iavarone; J. Yi; S. L. Hill 

Neuroscience And Biobehavioral Reviews. 2021-07-01. Vol. 126, p. 213-235. DOI : 10.1016/j.neubiorev.2021.03.015.


GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons

A. Bryson; R. J. Hatch; B-J. Zandt; C. Rossert; S. F. Berkovic et al. 

Proceedings Of The National Academy Of Sciences Of The United States Of America. 2020-02-11. Vol. 117, num. 6, p. 3192-3202. DOI : 10.1073/pnas.1906369117.

A computational biophysical model of thalamic and reticular nucleus microcircuitry: from neurons to emergent network dynamics

E. Iavarone / S. L. Hill (Dir.)  

Lausanne, EPFL, 2020. 


Nicotine increases sleep spindle activity

C. O’Reilly; F. Chapotot; F. Pittau; N. Mella; F. Picard 

Journal Of Sleep Research. 2019-08-01. Vol. 28, num. 4, p. e12800. DOI : 10.1111/jsr.12800.

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

M. Shardlow; M. Ju; M. Li; C. O’Reilly; E. Iavarone et al. 

Neuroinformatics. 2019-07-01. Vol. 17, num. 3, p. 391-406. DOI : 10.1007/s12021-018-9404-y.

Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization

E. Combrisson; R. Vallat; C. O’Reilly; M. Jas; A. Pascarella et al. 

Frontiers In Neuroinformatics. 2019-03-22. Vol. 13, p. 14. DOI : 10.3389/fninf.2019.00014.


Towards a supervised classification of neocortical interneuron morphologies

B. Mihaljevic; P. Larranaga; R. Benavides-Piccione; S. Hill; J. DeFelipe et al. 

Bmc Bioinformatics. 2018-12-17. Vol. 19, p. 511. DOI : 10.1186/s12859-018-2470-1.


Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

A. I. R. Maas; D. K. Menon; P. D. Adelson; N. Andelic; M. J. Bell et al. 

Lancet Neurology. 2017. Vol. 16, num. 12, p. 987-1048. DOI : 10.1016/S1474-4422(17)30371-X.

Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies

C. O’Reilly; J. D. Lewis; M. Elsabbagh 

PLoS One. 2017. Vol. 12, num. 5, p. e0175870. DOI : 10.1371/journal.pone.0175870.

Sleep-dependent consolidation of face recognition and its relationship to REM sleep duration, REM density and Stage 2 sleep spindles

E. Solomonova; P. Stenstrom; E. Schon; A. Duquette; S. Dube et al. 

Journal Of Sleep Research. 2017. Vol. 26, num. 3, p. 318-321. DOI : 10.1111/jsr.12520.

Semi-automatic 3D morphological reconstruction of neurons with densely branching morphology: Application to retinal All amacrine cells imaged with multi -photon excitation microscopy

B-J. Zandt; A. Losnegard; E. Hodneland; M. L. Veruki; A. Lundervold et al. 

Journal Of Neuroscience Methods. 2017. Vol. 279, p. 101-118. DOI : 10.1016/j.jneumeth.2017.01.008.

A Framework for Collaborative Curation of Neuroscientific Literature

C. O’Reilly; E. Iavarone; S. L. Hill 

Frontiers in Neuroinformatics. 2017. Vol. 11, p. 27. DOI : 10.3389/fninf.2017.00027.

Editorial: Sleep Spindles: Breaking the Methodological Wall

C. O’Reilly; S. C. Warby; T. Nielsen 

Frontiers In Human Neuroscience. 2017. Vol. 10, p. 672. DOI : 10.3389/fnhum.2016.00672.


The Resource Identification Initiative: A cultural shift in publishing

A. Bandrowski; M. Brush; J. S. Grethe; M. A. Haendel; D. N. Kennedy et al. 

Journal of Comparative Neurology. 2016. Vol. 524, num. 1, p. 8-22. DOI : 10.1002/cne.23913.

Comments and General Discussion on “The Anatomical Problem Posed by Brain Complexity and Size: A Potential Solution”

J. Defelipe; R. J. Douglas; S. L. Hill; E. S. Lein; K. A. C. Martin et al. 

Frontiers in Neuroanatomy. 2016. Vol. 10, p. 60. DOI : 10.3389/fnana.2016.00060.

Data Publications Correlate with Citation Impact

F. Leitner; C. Bielza; S. L. Hill; P. Larrañaga 

Frontiers in Neuroscience. 2016. Vol. 10, p. 419. DOI : 10.3389/fnins.2016.00419.

Brainhack: a collaborative workshop for the open neuroscience community

R. C. Craddock; D. S. Margulies; P. Bellec; B. N. Nichols; S. Alcauter et al. 

2016. Brainhack. DOI : 10.1186/s13742-016-0121-x.

Grand Challenges for Global Brain Sciences

J. T. Vogelstein; K. Amunts; A. Andreou; D. Angelaki; G. Ascoli et al. 

2016. Global Brain Workshop 2016, Johns Hopkins University in Baltimore, MD USA, April 7-8, 2016.

How do we know what we know? Discovering neuroscience data sets through minimal metadata

S. Hill 

Nature Reviews Neuroscience. 2016. Vol. 17, p. 735–736. DOI : 10.1038/nrn.2016.134.

Reconstructing the brain: from image stacks to neuron synthesis

J. C. Shillcock; M. Hawrylycz; S. Hill; H. Peng 

Brain Informatics. 2016. Vol. 3, p. 205-209. DOI : 10.1007/s40708-016-0041-7.


Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI)

A. I. Maas; D. K. Menon; E. W. Steyerberg; G. Citerio; F. Lecky et al. 

Neurosurgery. 2015. Vol. 76, num. 1, p. 67-80. DOI : 10.1227/NEU.0000000000000575.

The Resource Identification Initiative: A cultural shift in publishing

A. Bandrowski; M. Brush; J. S. Grethe; M. A. Haendel; D. N. Kennedy et al. 

F1000Research. 2015. Vol. 4, p. 134. DOI : 10.12688/f1000research.6555.1.

Feeding the human brain model

P. Tiesinga; R. Bakker; S. Hill; J. G. Bjaalie 

Current Opinion in Neurobiology. 2015. Vol. 32, p. 107-114. DOI : 10.1016/j.conb.2015.02.003.

BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

H. Peng; M. Hawrylycz; J. Roskams; S. Hill; N. Spruston et al. 

Neuron. 2015. Vol. 87, num. 2, p. 252-256. DOI : 10.1016/j.neuron.2015.06.036.

Brain Informatics and Health

Y. Guo; K. Friston; F. Aldo; S. Hill; H. Peng 

2015. International Conference on Brain Informatics and Health, BIH 2015.

Agile text mining with Sherlok

R. Richardet; J-C. Chappelier; S. Tripathy; S. Hill 

2015. 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 29 October – 1 November 2015. p. 1479-1484. DOI : 10.1109/BigData.2015.7363910.

The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex

S. Ramaswamy; J-D. Courcol; M. Abdellah; S. R. Adaszewski; N. Antille et al. 

Frontiers In Neural Circuits. 2015. Vol. 9, p. 44. DOI : 10.3389/fncir.2015.00044.

Automatic target validation based on neuroscientific literature mining for tractography

X. Vasques; R. Richardet; S. L. Hill; D. Slater; J-C. Chappelier et al. 

Frontiers In Neuroanatomy. 2015. Vol. 9, p. 66. DOI : 10.3389/fnana.2015.00066.

Large-scale extraction of brain connectivity from the neuroscientific literature

R. Richardet; J-C. Chappelier; M. Telefont; S. Hill 

Bioinformatics (Oxford, England). 2015. Vol. 31, num. 10, p. 1640-7. DOI : 10.1093/bioinformatics/btv025.


Cortical Columns, Models of

S. Hill 

Encyclopedia of Computational Neuroscience; New York: Springer, 2014. p. 868-871.

Interoperable atlases of the human brain

K. Amunts; M. Hawrylycz; D. Van Essen; J. Van Horn; N. Harel et al. 

NeuroImage. 2014. Vol. 99, p. 525-532. DOI : 10.1016/j.neuroimage.2014.06.010.

The In-Silico Neocortical Microcircuit

M. Reimann / H. Markram; S. Hill (Dir.)  

Lausanne, EPFL, 2014. 


Correction: Effective Stimuli for Constructing Reliable Neuron Models

S. Druckmann; T. K. Berger; F. Schuermann; S. Hill; H. Markram et al. 

PLoS Computational Biology. 2013. Vol. 9, num. 6. DOI : 10.1371/annotation/c002fbe1-712c-4608-9747-f1185f0b7cf4.

A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis

S. Druckmann; S. Hill; F. Schuermann; H. Markram; I. Segev 

Cerebral Cortex. 2013. Vol. 23, num. 12, p. 2994-3006. DOI : 10.1093/cercor/bhs290.

A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents

M. W. Reimann; C. A. Anastassiou; R. Perin; S. L. Hill; H. Markram et al. 

Neuron. 2013. Vol. 79, num. 2, p. 375-390. DOI : 10.1016/j.neuron.2013.05.023.

New insights into the classification and nomenclature of cortical GABAergic interneurons

J. Defelipe; P. L. Lopez-Cruz; R. Benavides-Piccione; C. Bielza; P. Larranaga et al. 

Nature Reviews Neuroscience. 2013. Vol. 14, num. 3, p. 202-216. DOI : 10.1038/nrn3444.


Computational Neuroscience Ontology: a new tool to provide semantic meaning to your models

Y. Le Franc; A. P. Davison; P. Gleeson; F. T. Imam; B. Kriener et al. 

Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA, July 21-26, 2012.

A neuron membrane mesh representation for visualization of electrophysiological simulations

S. Lasserre; J. Hernando; S. Hill; F. Schürmann; P. d. M. Anasagasti et al. 

IEEE transactions on visualization and computer graphics. 2012. Vol. 18, num. 2, p. 214-27. DOI : 10.1109/TVCG.2011.55.

Intrinsic morphological diversity of thick-tufted layer 5 pyramidal neurons ensures robust and invariant properties of in silico synaptic connections

S. Ramaswamy; S. L. Hill; J. G. King; F. Schürmann; Y. Wang et al. 

The Journal of physiology. 2012. Vol. 590, num. Pt 4, p. 737-52. DOI : 10.1113/jphysiol.2011.219576.

Combinatorial expression rules of ion channel genes in juvenile rat (Rattus norvegicus) neocortical neurons

G. Khazen; S. L. Hill; F. Schürmann; H. Markram 

PloS One. 2012. Vol. 7, num. 4, p. e34786. DOI : 10.1371/journal.pone.0034786.

Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits

S. L. Hill; Y. Wang; I. Riachi; F. Schürmann; H. Markram 

Proceedings of the National Academy of Sciences of the United States of America. 2012. Vol. 109, num. 42, p. E2885-94. DOI : 10.1073/pnas.1202128109.


NineML: the network interchange for neuroscience modeling language

I. Raikov; R. Cannon; R. Clewley; H. Cornelis; A. Davison et al. 

Twentieth Annual Computational Neuroscience Meeting: CNS*2011, Stockholm, Sweden, July 23-28, 2011.

Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties

E. Hay; S. Hill; F. Schürmann; H. Markram; I. Segev 

PLoS computational biology. 2011. Vol. 7, num. 7, p. e1002107. DOI : 10.1371/journal.pcbi.1002107.

Channelpedia: an integrative and interactive database for ion channels

R. Ranjan; G. Khazen; L. Gambazzi; S. Ramaswamy; S. L. Hill et al. 

Frontiers in neuroinformatics. 2011. Vol. 5, p. 36. DOI : 10.3389/fninf.2011.00036.

Effective Stimuli for Constructing Reliable Neuron Models

S. Druckmann; T. K. Berger; F. Schuermann; S. Hill; H. Markram et al. 

Plos Computational Biology. 2011. Vol. 7, num. 8, p. e1002133. DOI : 10.1371/journal.pcbi.1002133.

Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column

S. Ramaswamy / H. Markram; S. L. Hill (Dir.)  

Lausanne, EPFL, 2011. 


A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON

J. G. King; M. Hines; S. Hill; P. H. Goodman; H. Markram et al. 

Frontiers in neuroinformatics. 2009. Vol. 3, p. 10. DOI : 10.3389/neuro.11.010.2009.

Breakdown of Effective Connectivity During Slow Wave Sleep: Investigating the Mechanism Underlying a Cortical Gate Using Large-Scale Modeling

S. K. Esser; S. Hill; G. Tononi 

Journal Of Neurophysiology. 2009. Vol. 102, p. 2096-2111. DOI : 10.1152/jn.00059.2009.


Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

S. Druckmann; T. K. Berger; S. Hill; F. Schürmann; H. Markram et al. 

Biological cybernetics. 2008. Vol. 99, num. 4-5, p. 371-9. DOI : 10.1007/s00422-008-0269-2.