Henry Markram started a dual scientific and medical career at the University of Cape Town, in South Africa. His scientific work in the 80s revealed the polymodal receptive fields of pontomedullary reticular formation neurons in vivo and how acetylcholine re-organized these sensory maps.
He moved to Israel in 1988 and obtained his PhD at the Weizmann Institute where he discovered a link between acetylcholine and memory mechanisms by being the first to show that acetylcholine modulates the NMDA receptor in vitro studies, and thereby gates which synapses can undergo synaptic plasticity. He was also the first to characterize the electrical and anatomical properties of the cholinergic neurons in the medial septum diagonal band.
He carried out a first postdoctoral study as a Fulbright Scholar at the NIH, on the biophysics of ion channels on synaptic vesicles using sub-fractionation methods to isolate synaptic vesicles and patch-clamp recordings to characterize the ion channels. He carried out a second postdoctoral study at the Max Planck Institute, as a Minerva Fellow, where he discovered that individual action potentials propagating back into dendrites also cause pulsed influx of Ca2+ into the dendrites and found that sub-threshold activity could also activated a low threshold Ca2+ channel. He developed a model to show how different types of electrical activities can divert Ca2+ to activate different intracellular targets depending on the speed of Ca2+ influx an insight that helps explain how Ca2+ acts as a universal second messenger. His most well known discovery is that of the millisecond watershed to judge the relevance of communication between neurons marked by the back-propagating action potential. This phenomenon is now called Spike Timing Dependent Plasticity (STDP), which many laboratories around the world have subsequently found in multiple brain regions and many theoreticians have incorporated as a learning rule. At the Max-Planck he also started exploring the micro-anatomical and physiological principles of the different neurons of the neocortex and of the mono-synaptic connections that they form - the first step towards a systematic reverse engineering of the neocortical microcircuitry to derive the blue prints of the cortical column in a manner that would allow computer model reconstruction.
He received a tenure track position at the Weizmann Institute where he continued the reverse engineering studies and also discovered a number of core principles of the structural and functional organization such as differential signaling onto different neurons, models of dynamic synapses with Misha Tsodyks, the computational functions of dynamic synapses, and how GABAergic neurons map onto interneurons and pyramidal neurons. A major contribution during this period was his discovery of Redistribution of Synaptic Efficacy (RSE), where he showed that co-activation of neurons does not only alter synaptic strength, but also the dynamics of transmission. At the Weizmann, he also found the tabula rasa principle which governs the random structural connectivity between pyramidal neurons and a non-random functional connectivity due to target selection. Markram also developed a novel computation framework with Wolfgang Maass to account for the impact of multiple time constants in neurons and synapses on information processing called liquid computing or high entropy computing.
In 2002, he was appointed Full professor at the EPFL where he founded and directed the Brain Mind Institute. During this time Markram continued his reverse engineering approaches and developed a series of new technologies to allow large-scale multi-neuron patch-clamp studies. Markrams lab discovered a novel microcircuit plasticity phenomenon where connections are formed and eliminated in a Darwinian manner as apposed to where synapses are strengthening or weakened as found for LTP. This was the first demonstration that neural circuits are constantly being re-wired and excitation can boost the rate of re-wiring.
At the EPFL he also completed the much of the reverse engineering studies on the neocortical microcircuitry, revealing deeper insight into the circuit design and built databases of the blue-print of the cortical column. In 2005 he used these databases to launched the Blue Brain Project. The BBP used IBMs most advanced supercomputers to reconstruct a detailed computer model of the neocortical column composed of 10000 neurons, more than 340 different types of neurons distributed according to a layer-based recipe of composition and interconnected with 30 million synapses (6 different types) according to synaptic mapping recipes. The Blue Brain team built dozens of applications that now allow automated reconstruction, simulation, visualization, analysis and calibration of detailed microcircuits. This Proof of Concept completed, Markrams lab has now set the agenda towards whole brain and molecular modeling.
With an in depth understanding of the neocortical microcircuit, Markram set a path to determine how the neocortex changes in Autism. He found hyper-reactivity due to hyper-connectivity in the circuitry and hyper-plasticity due to hyper-NMDA expression. Similar findings in the Amygdala together with behavioral evidence that the animal model of autism expressed hyper-fear led to the novel theory of Autism called the Intense World Syndrome proposed by Henry and Kamila Markram. The Intense World Syndrome claims that the brain of an Autist is hyper-sensitive and hyper-plastic which renders the world painfully intense and the brain overly autonomous. The theory is acquiring rapid recognition and many new studies have extended the findings to other brain regions and to other models of autism.
Markram aims to eventually build detailed computer models of brains of mammals to pioneer simulation-based research in the neuroscience which could serve to aggregate, integrate, unify and validate our knowledge of the brain and to use such a facility as a new tool to explore the emergence of intelligence and higher cognitive functions in the brain, and explore hypotheses of diseases as well as treatments.
J. S. Coggan; D. Keller; H. Markram; F. Schurmann; P. J. Magistretti : Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling; Journal Of Theoretical Biology. 2020-02-21. DOI : 10.1016/j.jtbi.2019.110123.
R. Ranjan; E. Logette; M. Marani; M. Herzog; V. Tache et al. : A Kinetic Map of the Homomeric Voltage-gated Potassium Channel (Kv) Family. 2020-02-07. 64th Annual Meeting of the Biophysical-Society, San Diego, CA, Feb 15-19, 2020. p. 108A-108A.
N. Barros-Zulaica; J. Rahmon; G. Chindemi; R. Perin; H. Markram et al. : Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex; Frontiers In Synaptic Neuroscience. 2019-10-15. DOI : 10.3389/fnsyn.2019.00029.
M. W. Reimann; M. Geyaert; Y. Shi; H. Lu; H. Markram et al. : A null model of the mouse whole-neocortex micro-connectome; Nature Communications. 2019-08-29. DOI : 10.1038/s41467-019-11630-x.
F. Casalegno; T. Newton; R. Daher; M. Abdelaziz; A. Lodi-Rizzini et al. : Caries Detection with Near-Infrared Transillumination Using Deep Learning; Journal of Dental Research. 2019-08-26. DOI : 10.1177/0022034519871884.
M. Nolte; M. W. Reimann; J. G. King; H. Markram; E. B. Muller : Cortical reliability amid noise and chaos; Nature Communications. 2019-08-22. DOI : 10.1038/s41467-019-11633-8.
R. Ranjan; E. Logette; M. Marani; M. Herzog; V. Tache et al. : A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family; Frontiers in Cellular Neuroscience. 2019-08-20. DOI : 10.3389/fncel.2019.00358.
D. Keller; J. Meystre; R. V. Veettil; O. Burri; R. Guiet et al. : A Derived Positional Mapping of Inhibitory Subtypes in the Somatosensory Cortex; Frontiers In Neuroanatomy. 2019-08-06. DOI : 10.3389/fnana.2019.00078.
E. Iavarone; J. Yi; Y. Shi; B.-J. Zandt; C. O'Reilly et al. : Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons; PLoS Computational Biology. 2019-05-16. DOI : 10.1371/journal.pcbi.1006753.
X. Fan; H. Markram : A Brief History of Simulation Neuroscience; Frontiers In Neuroinformatics. 2019-05-07. DOI : 10.3389/fninf.2019.00032.
C. Colangelo; P. Shichkova; D. Keller; H. Markram; S. Ramaswamy : Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex; Frontiers In Neural Circuits. 2019-04-12. DOI : 10.3389/fncir.2019.00024.
L. Kanari; S. Ramaswamy; Y. Shi; S. Morand; J. Meystre et al. : Objective Morphological Classification of Neocortical Pyramidal Cells; Cerebral Cortex. 2019-04-01. DOI : 10.1093/cercor/bhy339.
M. R. Favre; H. Markram; K. Markram : Individual differences in sensory sensitivity: Further lessons from an Autism model; Cognitive Neuroscience. 2019-03-30. DOI : 10.1080/17588928.2019.1592143.
C. Ero; M.-O. Gewaltig; D. Keller; H. Markram : A Cell Atlas for the Mouse Brain (vol 12, 84, 2018); Frontiers In Neuroinformatics. 2019-02-19. DOI : 10.3389/fninf.2019.00007.
C. Eroe; M.-O. Gewaltig; D. Keller; H. Markram : A Cell Atlas for the Mouse Brain; Frontiers In Neuroinformatics. 2018-11-28. DOI : 10.3389/fninf.2018.00084.
D. Keller; C. Ero; H. Markram : Cell Densities in the Mouse Brain: A Systematic Review; Frontiers In Neuroanatomy. 2018-10-23. DOI : 10.3389/fnana.2018.00083.
S. Ramaswamy; C. Colangelo; H. Markram : Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity; Frontiers In Neural Circuits. 2018-10-09. DOI : 10.3389/fncir.2018.00077.
J. S. Coggan; C. Cali; D. Keller; M. Agus; D. Boges et al. : A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble; Frontiers In Neuroscience. 2018-09-25. DOI : 10.3389/fnins.2018.00664.
R. Migliore; C. A. Lupascu; L. L. Bologna; A. Romani; J.-D. Courcol et al. : The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow; Plos Computational Biology. 2018-09-01. DOI : 10.1371/journal.pcbi.1006423.
H. Markram; E. B. Muller; S. L. Hill; F. Schuermann ; Reconstruction and simulation of neocortical microcircuitry. US2018101660 . 2018.
M. W. Reimann; M. C. Nolte; H. Markram; K. P. Hess Bellwald; R. Levi ; Generating and identifying functional subnetworks within structural networks. US2018197069 . 2018.
H. Markram; W. Gerstner; M.-O. Gewaltig; C. Rössert; E. B. Muller et al. ; Simplification of neural network models. US2018285716 . 2018.
M. Abdellah; J. Hernando; S. Eilemann; S. Lapere; N. Antille et al. : NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks; BIOINFORMATICS. 2018. DOI : 10.1093/bioinformatics/bty231.
L. Kanari; P. Dlotko; M. Scolamiero; R. Levi; J. Shillcock et al. : A Topological Representation of Branching Neuronal Morphologies; NEUROINFORMATICS. 2018. DOI : 10.1007/s12021-017-9341-1.
M. Doron; G. Chindemi; E. Muller; H. Markram; I. Segev : Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons; Cell Reports. 2017. DOI : 10.1016/j.celrep.2017.10.035.
M. Abdellah; J. Hernando; N. Antille; S. Eilemann; H. Markram et al. : Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies; Bmc Bioinformatics. 2017. DOI : 10.1186/s12859-017-1788-4.
M. W. Reimann; A.-L. Horlemann; S. Ramaswamy; E. B. Muller; H. Markram : Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity; Cerebral Cortex. 2017. DOI : 10.1093/cercor/bhx150.
J. Coggan; D. Keller; C. Cal; H. Lehvaslaiho; F. Schurmann et al. : Modeling the metabolic response of astrocytes to neuronal activity. 2017. 13th European Meeting on Glial Cells in Health and Disease, Edinburgh, SCOTLAND, JUL 08-11, 2017. p. E280-E280.
M. W. Reimann; M. Nolte; M. Scolamiero; K. Turner; R. Perin et al. : Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function; Frontiers In Computational Neuroscience. 2017. DOI : 10.3389/fncom.2017.00048.
E. Gal; M. London; A. Globerson; S. Ramaswamy; M. W. Reimann et al. : Rich cell-type-specific network topology in neocortical microcircuitry; Nature Neuroscience. 2017. DOI : 10.1038/nn.4576.
M. Abdellah; A. Bilgili; S. Eilemann; J. Shillcock; H. Markram et al. : Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation; Bmc Bioinformatics. 2017. DOI : 10.1186/s12859-016-1444-4.
O. Amsalem; W. Van Geit; E. Muller; H. Markram; I. Segev : From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket Cells; Cerebral Cortex. 2016-06-09. DOI : 10.1093/cercor/bhw166.
J. V. Raimondo; H. Tomes; A. Irkle; L. Kay; L. Kellaway et al. : Tight Coupling of Astrocyte pH Dynamics to Epileptiform Activity Revealed by Genetically Encoded pH Sensors; Journal Of Neuroscience. 2016. DOI : 10.1523/Jneurosci.0664-16.2016.
W. Van Geit; M. Gevaert; G. Chindemi; C. Roessert; J.-D. Courcol et al. : BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience; Frontiers In Neuroinformatics. 2016. DOI : 10.3389/fninf.2016.00017.
M. Reimann; H. Markram; F. Schürmann; E. B. Muller; S. Hill et al. ; Structural to functional synaptic conversion. US9165244 ; US2014108315 . 2015.
M. Abdellah; A. Bilgili; S. Eilemann; H. Markram; F. Schürmann : Physically-based in silico light sheet microscopy for visualizing fluorescent brain models; BMC bioinformatics. 2015. DOI : 10.1186/1471-2105-16-S11-S8.
S. Ramaswamy; J.-D. Courcol; M. Abdellah; S. R. Adaszewski; N. Antille et al. : The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex; Frontiers In Neural Circuits. 2015. DOI : 10.3389/fncir.2015.00044.
M. W. Reimann; J. G. King; E. B. Muller; S. Ramaswamy; H. Markram : An algorithm to predict the connectome of neural microcircuits; Frontiers In Computational Neuroscience. 2015. DOI : 10.3389/fncom.2015.00120.
H. Markram; E. Muller; S. Ramaswamy; M. W. Reimann; M. Abdellah et al. : Reconstruction and Simulation of Neocortical Microcircuitry; Cell. 2015. DOI : 10.1016/j.cell.2015.09.029.
S. Ramaswamy; H. Markram : Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron; Frontiers In Cellular Neuroscience. 2015. DOI : 10.3389/fncel.2015.00233.
D. Keller; N. Babai; O. Kochubey; Y. Han; H. Markram et al. : An Exclusion Zone for Ca2+ Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS Synapse; PLoS computational biology. 2015. DOI : 10.1371/journal.pcbi.1004253.
V. Delattre; D. Keller; M. Perich; H. Markram; E. B. Muller : Network-timing-dependent plasticity; Frontiers in cellular neuroscience. 2015. DOI : 10.3389/fncel.2015.00220.
M. R. Favre; D. La Mendola; J. Meystre; D. Christodoulou; M. Cochrane et al. : Predictable enriched environment prevents development of hyper-emotionality in the VPA rat model of autism; Frontiers in Neuroscience. 2015. DOI : 10.3389/fnins.2015.00127.
C. Anastassiou; R. d. C. Perin; G. Buzsaki; H. Markram; C. Koch : Cell-type- and activity-dependent extracellular correlates of intracellular spiking; Journal of Neurophysiology. 2015. DOI : 10.1152/jn.00628.2014.
I. Costantini; J.-P. Ghobril; A. P. Di Giovanna; A. L. A. Mascaro; L. Silvestri et al. : A versatile clearing agent for multi-modal brain imaging; Scientific Reports. 2015. DOI : 10.1038/srep09808.
R. Frackowiak; H. Markram : The future of human cerebral cartography: a novel approach; Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 2015. DOI : 10.1098/rstb.2014.0171.
S. Camacho; S. Michlig; C. De Senarclens-Bezencon; J. Meylan; J. Meystre et al. : Anti-Obesity and Anti-Hyperglycemic Effects of Cinnamaldehyde via altered Ghrelin Secretion and Functional impact on Food Intake and Gastric Emptying; Scientific Reports. 2015. DOI : 10.1038/srep07919.
G. Khazen; H. Markram; F. Schürmann; M. Telefont ; Distributor of neurons in a neocortical column. US2014107992 . 2014.
M. Toledo-Rodriguez; H. Markram : Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversity; Methods in molecular biology (Clifton, N.J.). 2014. DOI : 10.1007/978-1-4939-1096-0_8.
J. Defelipe; E. Garrido; H. Markram : The death of Cajal and the end of scientific romanticism and individualism; Trends In Neurosciences. 2014. DOI : 10.1016/j.tins.2014.08.002.
M. Pezzoli; A. Elhamdani; S. Camacho; J. Meystre; S. M. Gonzalez et al. : Dampened neural activity and abolition of epileptic-like activity in cortical slices by active ingredients of spices; Scientific Reports. 2014. DOI : 10.1038/srep06825.
S. Muralidhar; Y. Wang; H. Markram : Synaptic and cellular organization of layer 1 of the developing rat somatosensory cortex; Frontiers In Neuroanatomy. 2014. DOI : 10.3389/fnana.2013.00052.
F. Tauheed; T. Heinis; F. Schürmann; H. Markram; A. Ailamaki : OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets. 2014. 30st International Conference on Data Engineering (ICDE '14), Chicago, USA, April, 2014.
S. Druckmann; T. K. Berger; F. Schuermann; S. Hill; H. Markram et al. : Correction: Effective Stimuli for Constructing Reliable Neuron Models; PLoS Computational Biology. 2013. DOI : 10.1371/annotation/c002fbe1-712c-4608-9747-f1185f0b7cf4.
H. Markram : Seven challenges for neuroscience; Functional neurology. 2013. DOI : 10.11138/FNeur/2013.28.3.145.
R. Perin; H. Markram : A computer-assisted multi-electrode patch-clamp system; Journal of visualized experiments : JoVE. 2013. DOI : 10.3791/50630.
S. Druckmann; S. Hill; F. Schuermann; H. Markram; I. Segev : A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis; Cerebral Cortex. 2013. DOI : 10.1093/cercor/bhs290.
V. Delattre; D. La Mendola; J. Meystre; H. Markram; K. Markram : Nlgn4 knockout induces network hypo-excitability in juvenile mouse somatosensory cortex in vitro; Scientific Reports. 2013. DOI : 10.1038/srep02897.
M. R. Favre; T. R. Barkat; D. Lamendola; G. Khazen; H. Markram et al. : General developmental health in the VPA-rat model of autism; Frontiers In Behavioral Neuroscience. 2013. DOI : 10.3389/fnbeh.2013.00088.
M. W. Reimann; C. A. Anastassiou; R. Perin; S. L. Hill; H. Markram et al. : A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents; Neuron. 2013. DOI : 10.1016/j.neuron.2013.05.023.
E. R. Kandel; H. Markram; P. M. Matthews; R. Yuste; C. Koch : Neuroscience thinks big (and collaboratively); Nature Reviews Neuroscience. 2013. DOI : 10.1038/nrn3578.
E. Hay; F. Schuermann; H. Markram; I. Segev : Preserving axosomatic spiking features despite diverse dendritic morphology; Journal Of Neurophysiology. 2013. DOI : 10.1152/jn.00048.2013.
R. Perin; M. Telefont; H. Markram : Computing the size and number of neuronal clusters in local circuits; Frontiers In Neuroanatomy. 2013. DOI : 10.3389/fnana.2013.00001.
A. Loebel; J.-V. Le Be; M. J. E. Richardson; H. Markram; A. V. M. Herz : Matched Pre- and Post-Synaptic Changes Underlie Synaptic Plasticity over Long Time Scales; Journal Of Neuroscience. 2013. DOI : 10.1523/Jneurosci.3740-12.2013.
J. Griggs; H. Markram : One minute with ... Henry Markram; New Scientist. 2013.
J. Defelipe; P. L. Lopez-Cruz; R. Benavides-Piccione; C. Bielza; P. Larranaga et al. : New insights into the classification and nomenclature of cortical GABAergic interneurons; Nature Reviews Neuroscience. 2013. DOI : 10.1038/nrn3444.
M. Giugliano; R. Luthi-carter; H. Markram; L. Gambazzi; O. Goekce ; Method for in-vitro monitoring of neuronal disorders and use thereof. EP2591356 ; US2013109048 ; WO2012004778 . 2012.
S. Lasserre; J. Hernando; S. Hill; F. Schürmann; P. d. M. Anasagasti et al. : A neuron membrane mesh representation for visualization of electrophysiological simulations; IEEE transactions on visualization and computer graphics. 2012. DOI : 10.1109/TVCG.2011.55.
S. Ramaswamy; S. L. Hill; J. G. King; F. Schürmann; Y. Wang et al. : Intrinsic morphological diversity of thick-tufted layer 5 pyramidal neurons ensures robust and invariant properties of in silico synaptic connections; The Journal of physiology. 2012. DOI : 10.1113/jphysiol.2011.219576.
G. Khazen; S. L. Hill; F. Schürmann; H. Markram : Combinatorial expression rules of ion channel genes in juvenile rat (Rattus norvegicus) neocortical neurons; PloS One. 2012. DOI : 10.1371/journal.pone.0034786.
H. Markram : The human brain project; Scientific American. 2012.
H. Markram; W. Gerstner; P. J. Sjöström : Spike-timing-dependent plasticity: a comprehensive overview; Frontiers in synaptic neuroscience. 2012. DOI : 10.3389/fnsyn.2012.00002.
S. L. Hill; Y. Wang; I. Riachi; F. Schürmann; H. Markram : Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits; Proceedings of the National Academy of Sciences of the United States of America. 2012. DOI : 10.1073/pnas.1202128109.
J. V. Raimondo; H. Markram; C. J. Akerman : Short-term ionic plasticity at GABAergic synapses; Frontiers in synaptic neuroscience. 2012. DOI : 10.3389/fnsyn.2012.00005.
J. DeFelipe; H. Markram; K. S. Rockland : The neocortical column; Frontiers In Neuroanatomy. 2012. DOI : 10.3389/fnana.2012.00022.
F. Tauheed; T. Heinis; F. Schürmann; H. Markram; A. Ailamaki : SCOUT: Prefetching for Latent Structure Following Queries. 2012. 38th International Conference on Very Large Databases (VLDB '12), Istanbul, Turkey, August, 2012.
F. Tauheed; L. Biveinis; T. Heinis; F. Schürmann; H. Markram et al. : Accelerating Range Queries For Brain Simulations. 2012. 28th International Conference on Data Engineering (ICDE '12), Washington DC, USA, March, 2012. DOI : 10.1109/Icde.2012.56.
H. Markram; R. d. C. Perin; T. Berger ; Organizing neural networks. US2019354841 ; US10387767 ; US10373048 ; KR101816329 ; DK2531959 ; EP2531959 ; CN102859538 ; JP5844286 ; US2015363689 ; JP2013519140 ; CN102859538 ; US2012323833 ; EP2531959 ; KR20120123698 ; WO2011095342 . 2011.
H. Markram ; Encoding and decoding of information. CN104901702 ; KR101734596 ; JP5848414 ; CN104901702 ; CN102648582 ; US8941512 ; JP2014209794 ; JP5587412 ; JP2013500660 ; US2012313798 ; US8253607 ; CN102648582 ; EP2460276 ; US2012119926 ; KR20120046760 ; US8159373 ; WO2011012614 ; WO2011012158 ; WO2011012614 ; US2011025532 . 2011.
C. A. Anastassiou; R. Perin; H. Markram; C. Koch : Ephaptic coupling of cortical neurons; Nature neuroscience. 2011. DOI : 10.1038/nn.2727.
S. Romand; Y. Wang; M. Toledo-Rodriguez; H. Markram : Morphological development of thick-tufted layer v pyramidal cells in the rat somatosensory cortex; Frontiers in neuroanatomy. 2011. DOI : 10.3389/fnana.2011.00005.
R. Perin; T. K. Berger; H. Markram : A synaptic organizing principle for cortical neuronal groups; Proceedings of the National Academy of Sciences of the United States of America. 2011. DOI : 10.1073/pnas.1016051108.
H. Markram; R. Perin : Innate neural assemblies for lego memory; Frontiers in neural circuits. 2011. DOI : 10.3389/fncir.2011.00006.
E. Hay; S. Hill; F. Schürmann; H. Markram; I. Segev : Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties; PLoS computational biology. 2011. DOI : 10.1371/journal.pcbi.1002107.
R. Ranjan; G. Khazen; L. Gambazzi; S. Ramaswamy; S. L. Hill et al. : Channelpedia: an integrative and interactive database for ion channels; Frontiers in neuroinformatics. 2011. DOI : 10.3389/fninf.2011.00036.
H. Markram; K. Meier; T. Lippert; S. Grillner; R. Frackowiak et al. : Introducing the Human Brain Project. 2011. 2nd European Future Technologies Conference and Exhibition (FET), Budapest, HUNGARY, May 04-06, 2011. p. 39-42. DOI : 10.1016/j.procs.2011.12.015.
S. Druckmann; T. K. Berger; F. Schuermann; S. Hill; H. Markram et al. : Effective Stimuli for Constructing Reliable Neuron Models; Plos Computational Biology. 2011. DOI : 10.1371/journal.pcbi.1002133.
G. Miller; H. Markram : Newsmaker interview: Heny Markram Blue Brain founder responds to critics, clarifies his goals; Science. 2011.
H. Markram; K. Markram : Frontiers research : seek, share & create; Common knowledge : the challenge of transdisciplinarity; Lausanne, Switzerland: EPFL Press, 2011. p. 145-162.
H. Markram; W. Gerstner; P. J. Sjöström : A history of spike-timing-dependent plasticity; Frontiers in Synaptic Neuroscience. 2011. DOI : 10.3389/fnsyn.2011.00004.
K. Markram; H. Markram : The intense world theory - a unifying theory of the neurobiology of autism; Frontiers in human neuroscience. 2010. DOI : 10.3389/fnhum.2010.00224.
T. K. Berger; G. Silberberg; R. Perin; H. Markram : Brief Bursts Self-Inhibit and Correlate the Pyramidal Network; Plos Biology. 2010. DOI : 10.1371/journal.pbio.1000473.
L. Gambazzi; O. Gokce; T. Seredenina; E. Katsyuba; H. Runne et al. : Diminished activity-dependent brain-derived neurotrophic factor expression underlies cortical neuron microcircuit hypoconnectivity resulting from exposure to mutant huntingtin fragments; The Journal of pharmacology and experimental therapeutics. 2010. DOI : 10.1124/jpet.110.167551.
G. T. Silva; J.-V. Le Bé; I. Riachi; T. Rinaldi; K. Markram et al. : Enhanced long-term microcircuit plasticity in the valproic Acid animal model of autism; Frontiers in synaptic neuroscience. 2009. DOI : 10.3389/neuro.19.001.2009.
J. G. King; M. Hines; S. Hill; P. H. Goodman; H. Markram et al. : A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON; Frontiers in neuroinformatics. 2009. DOI : 10.3389/neuro.11.010.2009.
A. Loebel; G. Silberberg; D. Helbig; H. Markram; M. Tsodyks et al. : Multiquantal release underlies the distribution of synaptic efficacies in the neocortex; Frontiers in computational neuroscience. 2009. DOI : 10.3389/neuro.10.027.2009.
G. Cellot; E. Cilia; S. Cipollone; V. Rancic; A. Sucapane et al. : Carbon nanotubes might improve neuronal performance by favouring electrical shortcuts; Nature Nanotechnology. 2009. DOI : 10.1038/NNANO.2008.374.
T. K. Berger; R. Perin; G. Silberberg; H. Markram : Frequency-dependent disynaptic inhibition in the pyramidal network: a ubiquitous pathway in the developing rat neocortex; Journal Of Physiology-London. 2009. DOI : 10.1113/jphysiol.2009.176552.
S. Gawad; M. Giugliano; M. O. Heuschkel; B. Wessling; H. Markram et al. : Substrate arrays of iridium oxide microelectrodes for in-vitro neuronal interfacing; Frontiers in Neuroengineering. 2009. DOI : 10.3389/neuro.16.001.2009.
M. L. Hines; H. Markram; F. Schürmann : Fully implicit parallel simulation of single neurons; Journal of computational neuroscience. 2008. DOI : 10.1007/s10827-008-0087-5.
Teaching & PhD
Life Sciences Engineering