Henry Markram
EPFL SV BMI LNMC
AAB 1 10 (Bâtiment AAB)
Station 15
1015 Lausanne
Web site: Web site: https://markram-lab.epfl.ch
+41 21 693 95 36
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Web site: Web site: https://sv.epfl.ch/education
Web site: Web site: https://www.epfl.ch/research/domains/bluebrain/
Biography
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.Publications
Infoscience publications
Infoscience
Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala
Nature Neuroscience. 2024-06-13. DOI : 10.1038/s41593-024-01676-6.Controlling morpho-electrophysiological variability of neurons with detailed models
Iscience. 2023-10-30. DOI : 10.1016/j.isci.2023.108222.Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization
Frontiers In Neuroanatomy. 2023-03-02. DOI : 10.3389/fnana.2023.1118170.A theory of memory consolidation and synaptic pruning in cortical circuits
Lausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-10094.Principles of Network Plasticity in Neocortical Microcircuits
Lausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-10137.Controlling morpho-electrophysiological variability of neurons with detailed biophysical models
bioRxiv. 2023. DOI : 10.1101/2023.04.06.535923.Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation
bioRxiv. 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 metabolism
bioRxiv. 2023. DOI : 10.1101/2023.08.30.555341.Of mice and men: Increased dendritic complexity gives rise to unique human networks
2023Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system
Lausanne, 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 sleep
Cell Reports. 2023. DOI : 10.1016/j.celrep.2023.112200.Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science
Semantic 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 cord
Lausanne, EPFL, 2023. DOI : 10.5075/epfl-thesis-9198.Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons
Plos 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 neuroscience
Briefings 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 disease
Glia. 2022-12-20. DOI : 10.1002/glia.24317.A method to estimate the cellular composition of the mouse brain from heterogeneous datasets
Plos Computational Biology. 2022-12-01. DOI : 10.1371/journal.pcbi.1010739.Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning
Biological Cybernetics. 2022-08-11. DOI : 10.1007/s00422-022-00940-x.Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex
Cerebral Cortex. 2022-07-08. DOI : 10.1093/cercor/bhac246.A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex
Nature Communications. 2022-06-01. DOI : 10.1038/s41467-022-30214-w.Representing stimulus information in an energy metabolism pathway
Journal Of Theoretical Biology. 2022-05-07. DOI : 10.1016/j.jtbi.2022.111090.Computational synthesis of cortical dendritic morphologies
Cell Reports. 2022-04-05. DOI : 10.1016/j.celrep.2022.110586.Modeling and simulation of rat non-barrel somatosensory cortex. Part I: Modeling anatomy
bioRxiv. 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 arrays
2022. DOI : 10.1101/2022.08.03.502468.Reconstruction of the Hippocampus
Computational Modelling of the Brain: Modelling Approaches to Cells, Circuits and Networks; Springer, 2022. p. 361.Neuromodulatory organization in the developing rat somatosensory cortex
2022. DOI : 10.1101/2022.11.11.516108.A universal workflow for creation, validation and generalization of detailed neuronal models
2022. DOI : 10.1101/2022.12.13.520234.Data-driven whole mouse brain modeling for multi-scale simulations
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8715.Neuromodulation of neocortical microcircuitry: a multi-scale framework to model the effects of cholinergic release
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8866.Extracellular stimulation and Local Field Potential recording in a L5 PC model with full axonal arbor
2021-12-01. p. S123-S124.Building somatosensory cortex neuron models using a workflow for the creation, validation and generalization of biophysically detailed cell models
2021-12-01. p. S122-S123.Computational modelling of a mouse layer 5 pyramidal neuron using genetic ion channels
2021-12-01. p. S123-S123.Digital Reconstruction of the Neuro-Glia-Vascular Architecture
Cerebral Cortex. 2021-12-01. DOI : 10.1093/cercor/bhab254.A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation
Frontiers In Molecular Neuroscience. 2021-11-10. DOI : 10.3389/fnmol.2021.604559.Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus
Journal Of Physiology. 2021-10-23. DOI : 10.1113/JP281711.The Role of Hub Neurons in Modulating Cortical Dynamics
Frontiers 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 Atlas
Frontiers 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-19
Frontiers 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 simulations
Bioinformatics. 2021-07-01. DOI : 10.1093/bioinformatics/btab280.In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations
Nature 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 Response
Frontiers In Public Health. 2021-02-16. DOI : 10.3389/fpubh.2021.607677.Synthesis of branching morphologies
US2022414436 ; EP4032103 ; CN114787939 ; KR20220084391 ; WO2021078588 . 2021.Structural architecture of the Neuronal-Glial-Vascular system
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-7480.Thalamic microcircuitry: neurons, synapses, and circuit motifs in receptive field structure and sensory processing
Lausanne, EPFL, 2021. DOI : 10.5075/epfl-thesis-7614.Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis
Bioinformatics. 2020-07-01. DOI : 10.1093/bioinformatics/btaa461.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.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. DOI : 10.1016/j.bpj.2019.11.739.Neuron Geometry Underlies Universal Network Features in Cortical Microcircuits
bioRxiv. 2020. DOI : 10.1101/656058.Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response
bioRxiv. 2020. DOI : 10.1101/2020.05.31.126466.Data driven building of realistic neuron model using IBEA and CMA evolution strategies
2020. 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 activity
Network Neuroscience. 2020-01-01. DOI : 10.1162/netn_a_00124.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.A null model of the mouse whole-neocortex micro-connectome
Nature Communications. 2019-08-29. DOI : 10.1038/s41467-019-11630-x.Caries Detection with Near-Infrared Transillumination Using Deep Learning
Journal of Dental Research. 2019-08-26. DOI : 10.1177/0022034519871884.Cortical reliability amid noise and chaos
Nature Communications. 2019-08-22. DOI : 10.1038/s41467-019-11633-8.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.A Derived Positional Mapping of Inhibitory Subtypes in the Somatosensory Cortex
Frontiers In Neuroanatomy. 2019-08-06. DOI : 10.3389/fnana.2019.00078.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.A Brief History of Simulation Neuroscience
Frontiers In Neuroinformatics. 2019-05-07. DOI : 10.3389/fninf.2019.00032.Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex
Frontiers In Neural Circuits. 2019-04-12. DOI : 10.3389/fncir.2019.00024.Objective Morphological Classification of Neocortical Pyramidal Cells
Cerebral Cortex. 2019-04-01. DOI : 10.1093/cercor/bhy339.Individual differences in sensory sensitivity: Further lessons from an Autism model
Cognitive Neuroscience. 2019-03-30. DOI : 10.1080/17588928.2019.1592143.Corrigendum: A Cell Atlas for the Mouse Brain
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 Activity
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9848.Reverse engineering the motor control system
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9599.Untangling emergent cortical dynamics: neurons from networks, noise from chaos
Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9616.A Cell Atlas for the Mouse Brain
Frontiers In Neuroinformatics. 2018-11-28. DOI : 10.3389/fninf.2018.00084.Cell Densities in the Mouse Brain: A Systematic Review
Frontiers 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 Activity
Frontiers 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 Ensemble
Frontiers 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 workflow
Plos Computational Biology. 2018-09-01. DOI : 10.1371/journal.pcbi.1006423.Microcircuitry of the neocortex
Handbook of Brain Microcircuits; Oxford University Press, 2018. p. 35-46.Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate
PLOS Computational Biology. 2018. DOI : 10.1371/journal.pcbi.1006392.Reconstruction and simulation of neocortical microcircuitry
US11817220 ; US2018101660 . 2018.Generating and identifying functional subnetworks within structural networks
US2023297808 ; US11615285 ; US2018197069 . 2018.Simplification of neural network models
US11983620 ; US2022230052 ; US11301750 ; US2018285716 . 2018.Data-driven reconstruction of a point neuron mouse brain
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8962.NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks
BIOINFORMATICS. 2018. DOI : 10.1093/bioinformatics/bty231.A Topological Representation of Branching Neuronal Morphologies
NEUROINFORMATICS. 2018. DOI : 10.1007/s12021-017-9341-1.Neuronal morphologies: the shapes of thoughts
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8255.Towards a unified understanding of synaptic plasticity
Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8186.A Physically Plausible Model for Rendering Highly Scattering Fluorescent Participating Media
2017Timed 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.In Silico Brain Imaging Physically-plausible Methods for Visualizing Neocortical Microcircuitry
Lausanne, 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 studies
Bmc Bioinformatics. 2017. DOI : 10.1186/s12859-017-1788-4.Using the Green's function to simplify and understand dendrites
Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7869.Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity
Cerebral Cortex. 2017. DOI : 10.1093/cercor/bhx150.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.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.Rich cell-type-specific network topology in neocortical microcircuitry
Nature Neuroscience. 2017. DOI : 10.1038/nn.4576.Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation
Bmc 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 Cells
Cerebral Cortex. 2016-06-09. DOI : 10.1093/cercor/bhw166.Automated point-neuron simplification of data-driven microcircuit models
2016Tight 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.BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience
Frontiers In Neuroinformatics. 2016. DOI : 10.3389/fninf.2016.00017.Agile in-litero experiments
Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6809.Structural to functional synaptic conversion
US9165244 ; US2014108315 . 2015.Physically-based in silico light sheet microscopy for visualizing fluorescent brain models
BMC bioinformatics. 2015. DOI : 10.1186/1471-2105-16-S11-S8.The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex
Frontiers In Neural Circuits. 2015. DOI : 10.3389/fncir.2015.00044.Teaching & PhD
Teaching
Life Sciences Engineering