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.
Synthesis of branching morphologiesWO2021078588 . 2021.
Thalamic microcircuitry: neurons, synapses, and circuit motifs in receptive field structure and sensory processingLausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-7614.
Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVisBioinformatics. 2020-07-01. DOI : 10.1093/bioinformatics/btaa461.
Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signallingJournal Of Theoretical Biology. 2020-02-21. DOI : 10.1016/j.jtbi.2019.110123.
A Kinetic Map of the Homomeric Voltage-gated Potassium Channel (Kv) Family2020-02-07. 64th Annual Meeting of the Biophysical-Society, San Diego, CA, Feb 15-19, 2020. p. 108A-108A.
Impact of higher order network structure on emergent cortical activityNetwork Neuroscience. 2020-01-01. DOI : 10.1162/netn_a_00124.
Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the NeocortexFrontiers In Synaptic Neuroscience. 2019-10-15. DOI : 10.3389/fnsyn.2019.00029.
A null model of the mouse whole-neocortex micro-connectomeNature Communications. 2019-08-29. DOI : 10.1038/s41467-019-11630-x.
Caries Detection with Near-Infrared Transillumination Using Deep LearningJournal of Dental Research. 2019-08-26. DOI : 10.1177/0022034519871884.
Cortical reliability amid noise and chaosNature Communications. 2019-08-22. DOI : 10.1038/s41467-019-11633-8.
A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) FamilyFrontiers in Cellular Neuroscience. 2019-08-20. DOI : 10.3389/fncel.2019.00358.
A Derived Positional Mapping of Inhibitory Subtypes in the Somatosensory CortexFrontiers In Neuroanatomy. 2019-08-06. DOI : 10.3389/fnana.2019.00078.
Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neuronsPLoS Computational Biology. 2019-05-16. DOI : 10.1371/journal.pcbi.1006753.
A Brief History of Simulation NeuroscienceFrontiers In Neuroinformatics. 2019-05-07. DOI : 10.3389/fninf.2019.00032.
Cellular, Synaptic and Network Effects of Acetylcholine in the NeocortexFrontiers In Neural Circuits. 2019-04-12. DOI : 10.3389/fncir.2019.00024.
Objective Morphological Classification of Neocortical Pyramidal CellsCerebral Cortex. 2019-04-01. DOI : 10.1093/cercor/bhy339.
Individual differences in sensory sensitivity: Further lessons from an Autism modelCognitive Neuroscience. 2019-03-30. DOI : 10.1080/17588928.2019.1592143.
A Cell Atlas for the Mouse Brain (vol 12, 84, 2018)Frontiers In Neuroinformatics. 2019-02-19. DOI : 10.3389/fninf.2019.00007.
In Silico Voltage-Sensitive Dye Imaging: A Model-Based Approach for Bridging Scales of Cortical ActivityLausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9848.
Reverse engineering the motor control systemLausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9599.
Untangling emergent cortical dynamics: neurons from networks, noise from chaosLausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9616.
A Cell Atlas for the Mouse BrainFrontiers In Neuroinformatics. 2018-11-28. DOI : 10.3389/fninf.2018.00084.
Cell Densities in the Mouse Brain: A Systematic ReviewFrontiers In Neuroanatomy. 2018-10-23. DOI : 10.3389/fnana.2018.00083.
Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network ActivityFrontiers In Neural Circuits. 2018-10-09. DOI : 10.3389/fncir.2018.00077.
A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular EnsembleFrontiers In Neuroscience. 2018-09-25. DOI : 10.3389/fnins.2018.00664.
The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflowPlos Computational Biology. 2018-09-01. DOI : 10.1371/journal.pcbi.1006423.
Reconstruction and simulation of neocortical microcircuitryUS2018101660 . 2018.
Generating and identifying functional subnetworks within structural networksUS2018197069 . 2018.
Simplification of neural network modelsUS2018285716 . 2018.
Data-driven reconstruction of a point neuron mouse brainLausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8962.
NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacksBIOINFORMATICS. 2018. DOI : 10.1093/bioinformatics/bty231.
A Topological Representation of Branching Neuronal MorphologiesNEUROINFORMATICS. 2018. DOI : 10.1007/s12021-017-9341-1.
Neuronal morphologies: the shapes of thoughtsLausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8255.
Towards a unified understanding of synaptic plasticityLausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8186.
Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical NeuronsCell Reports. 2017. DOI : 10.1016/j.celrep.2017.10.035.
In Silico Brain Imaging Physically-plausible Methods for Visualizing Neocortical MicrocircuitryLausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-8161.
Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studiesBmc Bioinformatics. 2017. DOI : 10.1186/s12859-017-1788-4.
Using the Green's function to simplify and understand dendritesLausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7869.
Morphological Diversity Strongly Constrains Synaptic Connectivity and PlasticityCerebral Cortex. 2017. DOI : 10.1093/cercor/bhx150.
Modeling the metabolic response of astrocytes to neuronal activity2017. 13th European Meeting on Glial Cells in Health and Disease, Edinburgh, SCOTLAND, JUL 08-11, 2017. p. E280-E280.
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and FunctionFrontiers In Computational Neuroscience. 2017. DOI : 10.3389/fncom.2017.00048.
Rich cell-type-specific network topology in neocortical microcircuitryNature Neuroscience. 2017. DOI : 10.1038/nn.4576.
Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentationBmc Bioinformatics. 2017. DOI : 10.1186/s12859-016-1444-4.
From Neuron Biophysics to Orientation Selectivity in Electrically Coupled Networks of Neocortical L2/3 Large Basket CellsCerebral Cortex. 2016-06-09. DOI : 10.1093/cercor/bhw166.
Tight Coupling of Astrocyte pH Dynamics to Epileptiform Activity Revealed by Genetically Encoded pH SensorsJournal Of Neuroscience. 2016. DOI : 10.1523/Jneurosci.0664-16.2016.
BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in NeuroscienceFrontiers In Neuroinformatics. 2016. DOI : 10.3389/fninf.2016.00017.
Agile in-litero experimentsLausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6809.
Structural to functional synaptic conversionUS9165244 ; US2014108315 . 2015.
Physically-based in silico light sheet microscopy for visualizing fluorescent brain modelsBMC bioinformatics. 2015. DOI : 10.1186/1471-2105-16-S11-S8.
The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortexFrontiers In Neural Circuits. 2015. DOI : 10.3389/fncir.2015.00044.
An algorithm to predict the connectome of neural microcircuitsFrontiers In Computational Neuroscience. 2015. DOI : 10.3389/fncom.2015.00120.
Reconstruction and Simulation of Neocortical MicrocircuitryCell. 2015. DOI : 10.1016/j.cell.2015.09.029.
Anatomy and physiology of the thick-tufted layer 5 pyramidal neuronFrontiers In Cellular Neuroscience. 2015. DOI : 10.3389/fncel.2015.00233.
An Exclusion Zone for Ca2 Channels around Docked Vesicles Explains Release Control by Multiple Channels at a CNS SynapsePLoS computational biology. 2015. DOI : 10.1371/journal.pcbi.1004253.
Network-timing-dependent plasticityFrontiers in cellular neuroscience. 2015. DOI : 10.3389/fncel.2015.00220.
Predictable enriched environment prevents development of hyper-emotionality in the VPA rat model of autismFrontiers in Neuroscience. 2015. DOI : 10.3389/fnins.2015.00127.
Cell-type- and activity-dependent extracellular correlates of intracellular spikingJournal of Neurophysiology. 2015. DOI : 10.1152/jn.00628.2014.
A versatile clearing agent for multi-modal brain imagingScientific Reports. 2015. DOI : 10.1038/srep09808.
The future of human cerebral cartography: a novel approachPhilosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 2015. DOI : 10.1098/rstb.2014.0171.
Large Volume Imaging of Rodent Brain Anatomy with Emphasis on Selective Plane Illumination MicroscopyLausanne, EPFL, 2015. DOI : 10.5075/epfl-thesis-6533.
Anti-Obesity and Anti-Hyperglycemic Effects of Cinnamaldehyde via altered Ghrelin Secretion and Functional impact on Food Intake and Gastric EmptyingScientific Reports. 2015. DOI : 10.1038/srep07919.
Distributor of neurons in a neocortical columnUS2014107992 . 2014.
Single-cell RT-PCR, a technique to decipher the electrical, anatomical, and genetic determinants of neuronal diversityMethods in molecular biology (Clifton, N.J.). 2014. DOI : 10.1007/978-1-4939-1096-0_8.
The death of Cajal and the end of scientific romanticism and individualismTrends In Neurosciences. 2014. DOI : 10.1016/j.tins.2014.08.002.
Dampened neural activity and abolition of epileptic-like activity in cortical slices by active ingredients of spicesScientific Reports. 2014. DOI : 10.1038/srep06825.
Scalable Exploration of Spatial Data in Large-Scale Scientific SimulationsLausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6125.
The In-Silico Neocortical MicrocircuitLausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6168.
Synaptic and cellular organization of layer 1 of the developing rat somatosensory cortexFrontiers In Neuroanatomy. 2014. DOI : 10.3389/fnana.2013.00052.
Visualizing the similarity and pedigree of NEURON ion channel models available on ModelDBCOSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets2014. 30st International Conference on Data Engineering (ICDE '14), Chicago, USA, April, 2014.
Correction: Effective Stimuli for Constructing Reliable Neuron ModelsPLoS Computational Biology. 2013. DOI : 10.1371/annotation/c002fbe1-712c-4608-9747-f1185f0b7cf4.
Seven challenges for neuroscienceFunctional neurology. 2013. DOI : 10.11138/FNeur/2013.28.3.145.
A computer-assisted multi-electrode patch-clamp systemJournal of visualized experiments : JoVE. 2013. DOI : 10.3791/50630.
A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical AnalysisCerebral Cortex. 2013. DOI : 10.1093/cercor/bhs290.
Nlgn4 knockout induces network hypo-excitability in juvenile mouse somatosensory cortex in vitroScientific Reports. 2013. DOI : 10.1038/srep02897.
Hyper-emotional neurophysiology in a rat model of autismLausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5996.
Synaptic and Cellular Organization of Layer 1 of the Developing Rat Somatosensory CortexLausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5902.
Network Activity and PlasticityLausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5901.
General developmental health in the VPA-rat model of autismFrontiers In Behavioral Neuroscience. 2013. DOI : 10.3389/fnbeh.2013.00088.
A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane CurrentsNeuron. 2013. DOI : 10.1016/j.neuron.2013.05.023.
Neuroscience thinks big (and collaboratively)Nature Reviews Neuroscience. 2013. DOI : 10.1038/nrn3578.
Preserving axosomatic spiking features despite diverse dendritic morphologyJournal Of Neurophysiology. 2013. DOI : 10.1152/jn.00048.2013.
Computing the size and number of neuronal clusters in local circuitsFrontiers In Neuroanatomy. 2013. DOI : 10.3389/fnana.2013.00001.
Matched Pre- and Post-Synaptic Changes Underlie Synaptic Plasticity over Long Time ScalesJournal Of Neuroscience. 2013. DOI : 10.1523/Jneurosci.3740-12.2013.
One minute with ... Henry MarkramNew Scientist. 2013.
New insights into the classification and nomenclature of cortical GABAergic interneuronsNature Reviews Neuroscience. 2013. DOI : 10.1038/nrn3444.
Method for in-vitro monitoring of neuronal disorders and use thereofEP2591356 ; US2013109048 ; WO2012004778 . 2012.
A neuron membrane mesh representation for visualization of electrophysiological simulationsIEEE transactions on visualization and computer graphics. 2012. 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 connectionsThe Journal of physiology. 2012. DOI : 10.1113/jphysiol.2011.219576.
Combinatorial expression rules of ion channel genes in juvenile rat (Rattus norvegicus) neocortical neuronsPloS One. 2012. DOI : 10.1371/journal.pone.0034786.
The human brain projectScientific American. 2012.
Spike-timing-dependent plasticity: a comprehensive overviewFrontiers in synaptic neuroscience. 2012. DOI : 10.3389/fnsyn.2012.00002.
Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuitsProceedings of the National Academy of Sciences of the United States of America. 2012. DOI : 10.1073/pnas.1202128109.
Short-term ionic plasticity at GABAergic synapsesFrontiers in synaptic neuroscience. 2012. DOI : 10.3389/fnsyn.2012.00005.
The neocortical columnFrontiers In Neuroanatomy. 2012. DOI : 10.3389/fnana.2012.00022.
SCOUT: Prefetching for Latent Structure Following Queries2012. 38th International Conference on Very Large Databases (VLDB '12), Istanbul, Turkey, August, 2012.
Accelerating Range Queries For Brain Simulations2012. 28th International Conference on Data Engineering (ICDE '12), Washington DC, USA, March, 2012. DOI : 10.1109/Icde.2012.56.
Organizing neural networksUS2019354841 ; US10387767 ; US10373048 ; KR101816329 ; DK2531959 ; EP2531959 ; CN102859538 ; JP5844286 ; US2015363689 ; JP2013519140 ; CN102859538 ; US2012323833 ; EP2531959 ; KR20120123698 ; WO2011095342 . 2011.
Encoding and decoding of informationES2823253 ; EP3771103 ; EP2460276 ; CN104901702 ; KR101734596 ; JP5848414 ; CN104901702 ; CN102648582 ; US8941512 ; JP2014209794 ; JP5587412 ; JP2013500660 ; US2012313798 ; US8253607 ; CN102648582 ; EP2460276 ; US2012119926 ; KR20120046760 ; US8159373 ; WO2011012614 ; WO2011012158 ; WO2011012614 ; US2011025532 . 2011.
Ephaptic coupling of cortical neuronsNature neuroscience. 2011. DOI : 10.1038/nn.2727.
Teaching & PhD
Life Sciences Engineering