Henry Markram

henry.markram@epfl.ch +41 21 693 95 36

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EPFL SV BMI LNMC
AAB 1 10 (Bâtiment AAB)
Station 19
CH-1015 Lausanne

+41 21 693 95 36
Office: AAB 1 10
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Website: https://markram-lab.epfl.ch

+41 21 693 95 36
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Website: https://sv.epfl.ch/education

Biography Awards Publications Teaching & PhD

Henry Markram started a dual scientific and medical career at the University of Cape Town, in South Africa. His scientific work in the 80’s 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. Markram’s 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 IBM’s most advanced supercomputers to reconstruct a detailed computer model of the neocortical column composed of 10’000 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, Markram’s 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.

Awards

Bell Labs Shannon Visionary Award

2016

Infoscience

A Detailed Model for Understanding the Human Neocortex

N. B. Zulaica; L. Kanari; V. Sood; P. Rai; A. Arnaudon et al.

2026

Henry Markram

Awards

Infoscience

A Detailed Model for Understanding the Human Neocortex

An interactive 3D atlas of gene expression in the mouse brain

Modeling and simulation of neocortical micro- and mesocircuitry (Part I, anatomy)

Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation)

Higher-order interactions in neuronal function: From genes to ionic currents in biophysical models

N-glycosylation modulates the inactivation kinetics of the Kv3.4 ion channel

Of mice and men: Dendritic architecture differentiates human from mouse neuronal networks

A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers

Seizure and redox rescue in a model of glucose transport deficiency

Breakdown and repair of metabolism in the aging brain

Computational Generation of Long-range Axonal Morphologies

Principles of inter-areal interactions in cortical sensory processing

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy

Modeling of blood flow dynamics in the rat somatosensory cortex

Sex dimorphism in rodent brain responses to ketogenic diets: A comparative study

An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Simplification of spiking neural network models

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus.

Brain Metabolism in Health and Neurodegeneration: The Interplay Among Neurons and Astrocytes

Generating brain-wide connectome using synthetic axonal morphologies

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

BlueCelluLab: Biologically Detailed Neural Network Experimentation API

Structural and molecular characterization of astrocyte and vasculature connectivity in the mouse hippocampus and cortex

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

Of mice and men: Increased dendritic complexity gives rise to unique human networks

Coupled Electromagnetic-Electrophysiological Modeling of Neural Circuits

A universal workflow for creation, validation and generalization of detailed neuronal models

Controlling morpho-electrophysiological variability of neurons with detailed models

Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization

A theory of memory consolidation and synaptic pruning in cortical circuits

Breakdown and rejuvenation of aging brain energy metabolism

Digital reconstruction of the mammalian spinal cord: from anatomical reference volume to cell type atlas of the mouse spinal cord

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons

Principles of Network Plasticity in Neocortical Microcircuits

Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience

Simulating Temporal Interference Stimulation

Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system

Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer's disease

A method to estimate the cellular composition of the mouse brain from heterogeneous datasets

Neuromodulatory organization in the developing rat somatosensory cortex

Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning

Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Representing stimulus information in an energy metabolism pathway

Computational synthesis of cortical dendritic morphologies

Reconstruction of the Hippocampus

Data-driven whole mouse brain modeling for multi-scale simulations

Neuromodulation of neocortical microcircuitry: a multi-scale framework to model the effects of cholinergic release

Digital Reconstruction of the Neuro-Glia-Vascular Architecture

Computational modelling of a mouse layer 5 pyramidal neuron using genetic ion channels

Building somatosensory cortex neuron models using a workflow for the creation, validation and generalization of biophysically detailed cell models

Extracellular stimulation and Local Field Potential recording in a L5 PC model with full axonal arbor

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus

The Role of Hub Neurons in Modulating Cortical Dynamics

Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19

Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations

In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations

ARC: An Open Web-Platform for Request/Supply Matching for a Prioritized and Controlled COVID-19 Response

Structural architecture of the Neuronal-Glial-Vascular system

Synthesis of branching morphologies

Thalamic microcircuitry: neurons, synapses, and circuit motifs in receptive field structure and sensory processing

Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis

Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling

A Kinetic Map of the Homomeric Voltage-gated Potassium Channel (Kv) Family

Impact of higher order network structure on emergent cortical activity

Data driven building of realistic neuron model using IBEA and CMA evolution strategies

Neuron Geometry Underlies Universal Network Features in Cortical Microcircuits

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex

A null model of the mouse whole-neocortex micro-connectome

Caries Detection with Near-Infrared Transillumination Using Deep Learning