BiographyHenry 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.
A theory of memory consolidation and synaptic pruning in cortical circuitsLausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-10094.
Principles of Network Plasticity in Neocortical MicrocircuitsLausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-10137.
Controlling morpho-electrophysiological variability of neurons with detailed biophysical modelsbioRxiv. 2023. DOI : 10.1101/2023.04.06.535923.
Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentationbioRxiv. 2023. DOI : 10.1101/2023.05.17.541168.
Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus.bioRxiv. 2023. DOI : 10.1101/2023.05.17.541167.
Breakdown and rejuvenation of aging brain energy metabolismbioRxiv. 2023. DOI : 10.1101/2023.08.30.555341.
Of mice and men: Increased dendritic complexity gives rise to unique human networks2023
Molecular reconstruction and simulation of the Neuron-Glia-Vasculature systemLausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-9336.
Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleepCell Reports. 2023. DOI : 10.1016/j.celrep.2023.112200.
Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven scienceSemantic Web. 2023. DOI : 10.3233/SW-222974.
Digital reconstruction of the mammalian spinal cord: from anatomical reference volume to cell type atlas of the mouse spinal cordLausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-9198.
Mapping of morpho-electric features to molecular identity of cortical inhibitory neuronsPlos Computational Biology. 2023-01-01. DOI : 10.1371/journal.pcbi.1010058.
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscienceBriefings In Bioinformatics. 2023-01-01. DOI : 10.1093/bib/bbac491.
Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer's diseaseGlia. 2022-12-20. DOI : 10.1002/glia.24317.
A method to estimate the cellular composition of the mouse brain from heterogeneous datasetsPlos Computational Biology. 2022-12-01. DOI : 10.1371/journal.pcbi.1010739.
Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learningBiological Cybernetics. 2022-08-11. DOI : 10.1007/s00422-022-00940-x.
Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortexCerebral Cortex. 2022-07-08. DOI : 10.1093/cercor/bhac246.
A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortexNature Communications. 2022-06-01. DOI : 10.1038/s41467-022-30214-w.
Representing stimulus information in an energy metabolism pathwayJournal Of Theoretical Biology. 2022-05-07. DOI : 10.1016/j.jtbi.2022.111090.
Computational synthesis of cortical dendritic morphologiesCell Reports. 2022-04-05. DOI : 10.1016/j.celrep.2022.110586.
Modeling and simulation of rat non-barrel somatosensory cortex. Part I: Modeling anatomybioRxiv. 2022. DOI : 10.1101/2022.08.11.503144.
A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays2022. DOI : 10.1101/2022.08.03.502468.
Reconstruction of the HippocampusComputational Modelling of the Brain: Modelling Approaches to Cells, Circuits and Networks; Springer, 2022. p. 361.
Neuromodulatory organization in the developing rat somatosensory cortex2022. DOI : 10.1101/2022.11.11.516108.
A universal workflow for creation, validation and generalization of detailed neuronal models2022. DOI : 10.1101/2022.12.13.520234.
Data-driven whole mouse brain modeling for multi-scale simulationsLausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8715.
Neuromodulation of neocortical microcircuitry: a multi-scale framework to model the effects of cholinergic releaseLausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8866.
Extracellular stimulation and Local Field Potential recording in a L5 PC model with full axonal arbor2021-12-01. p. S123-S124.
Building somatosensory cortex neuron models using a workflow for the creation, validation and generalization of biophysically detailed cell models2021-12-01. p. S122-S123.
Computational modelling of a mouse layer 5 pyramidal neuron using genetic ion channels2021-12-01. p. S123-S123.
Digital Reconstruction of the Neuro-Glia-Vascular ArchitectureCerebral Cortex. 2021-12-01. DOI : 10.1093/cercor/bhab254.
A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and SimulationFrontiers In Molecular Neuroscience. 2021-11-10. DOI : 10.3389/fnmol.2021.604559.
Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamusJournal Of Physiology. 2021-10-23. DOI : 10.1113/JP281711.
The Role of Hub Neurons in Modulating Cortical DynamicsFrontiers In Neural Circuits. 2021-09-24. DOI : 10.3389/fncir.2021.718270.
Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain AtlasFrontiers In Neuroinformatics. 2021-07-28. DOI : 10.3389/fninf.2021.691918.
A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19Frontiers In Public Health. 2021-07-28. DOI : 10.3389/fpubh.2021.695139.
Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulationsBioinformatics. 2021-07-01. DOI : 10.1093/bioinformatics/btab280.
In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populationsNature Communications. 2021-06-15. DOI : 10.1038/s41467-021-23901-7.
ARC: An Open Web-Platform for Request/Supply Matching for a Prioritized and Controlled COVID-19 ResponseFrontiers In Public Health. 2021-02-16. DOI : 10.3389/fpubh.2021.607677.
Synthesis of branching morphologiesUS2022414436 ; EP4032103 ; CN114787939 ; KR20220084391 ; WO2021078588 . 2021.
Structural architecture of the Neuronal-Glial-Vascular systemLausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-7480.
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. DOI : 10.1016/j.bpj.2019.11.739.
Neuron Geometry Underlies Universal Network Features in Cortical MicrocircuitsbioRxiv. 2020. DOI : 10.1101/656058.
Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise ResponsebioRxiv. 2020. DOI : 10.1101/2020.05.31.126466.
Data driven building of realistic neuron model using IBEA and CMA evolution strategies2020. GECCO '20: Genetic and Evolutionary Computation Conference, Cancún, Mexico, July 8 - 12, 2020. p. 35-36. DOI : 10.1145/3377929.3398161.
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.
Microcircuitry of the neocortexHandbook of Brain Microcircuits; Oxford University Press, 2018. p. 35-46.
Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactatePLOS Computational Biology. 2018. DOI : 10.1371/journal.pcbi.1006392.
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Generating and identifying functional subnetworks within structural networksUS2023297808 ; US11615285 ; US2018197069 . 2018.
Simplification of neural network modelsUS2022230052 ; US11301750 ; US2018285716 . 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.
A Physically Plausible Model for Rendering Highly Scattering Fluorescent Participating Media2017
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.
Automated point-neuron simplification of data-driven microcircuit models2016
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.
Teaching & PhD
Life Sciences Engineering