Henry Markram

Columbia University, New York City, New York, United States

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Publications (162)898.19 Total impact

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    ABSTRACT: Despite cash-strapped times for research, several ambitious collaborative neuroscience projects have attracted large amounts of funding and media attention. In Europe, the Human Brain Project aims to develop a large-scale computer simulation of the brain, whereas in the United States, the Brain Activity Map is working towards establishing a functional connectome of the entire brain, and the Allen Institute for Brain Science has embarked upon a 10-year project to understand the mouse visual cortex (the MindScope project). US President Barack Obama's announcement of the BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies Initiative) in April 2013 highlights the political commitment to neuroscience and is expected to further foster interdisciplinary collaborations, accelerate the development of new technologies and thus fuel much needed medical advances. In this Viewpoint article, five prominent neuroscientists explain the aims of the projects and how they are addressing some of the questions (and criticisms) that have arisen.
    Nature Reviews Neuroscience 08/2013; 14(9):659-64. · 31.38 Impact Factor
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    ABSTRACT: Brain activity generates extracellular voltage fluctuations recorded as local field potentials (LFPs). It is known that the relevant microvariables, the ionic currents across membranes, jointly generate the macrovariables, the extracellular voltage, but neither the detailed biophysical knowledge nor the required computational power have been available to model these processes. We simulated the LFP in a model of the rodent neocortical column composed of >12,000 reconstructed, multicompartmental, and spiking cortical layer 4 and 5 pyramidal neurons and basket cells, including five million dendritic and somatic compartments with voltage- and ion-dependent currents, realistic connectivity, and probabilistic AMPA, NMDA, and GABA synapses. We found that, depending on a number of factors, the LFP reflects local and cross-layer processing. Active currents dominate the generation of LFPs, not synaptic ones. Spike-related currents impact the LFP not only at higher frequencies but below 50 Hz. This work calls for re-evaluating the genesis of LFPs.
    Neuron 07/2013; 79(2):375-90. · 15.77 Impact Factor
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    ABSTRACT: [This corrects the article on p. e1002133 in vol. 7.].
    PLoS Computational Biology 06/2013; 9(6). · 4.87 Impact Factor
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    ABSTRACT: Modifications of synaptic efficacies are considered essential for learning and memory. However, it is not known how the underlying functional components of synaptic transmission change over long time scales. To address this question, we studied cortical synapses from young Wistar rats before and after 12 h intervals of spontaneous or glutamate-induced spiking activity. We found that, under these conditions, synaptic efficacies can increase or decrease by up to 10-fold. Statistical analyses reveal that these changes reflect modifications in the number of presynaptic release sites, together with postsynaptic changes that maintain the quantal size per release site. The quantitative relation between the presynaptic and postsynaptic transmission components was not affected when synaptic plasticity was enhanced or reduced using a broad range of pharmacological agents. These findings suggest that ongoing synaptic plasticity results in matched presynaptic and postsynaptic modifications, in which elementary modules that span the synaptic cleft are added or removed as a function of experience.
    Journal of Neuroscience 04/2013; 33(15):6257-66. · 6.91 Impact Factor
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    ABSTRACT: Throughout the nervous system, cells belonging to a certain electrical class (e-class) - sharing high similarity in firing response properties - may nevertheless have widely variable dendritic morphologies. To quantify the effect of this morphological variability on the firing of layer 5 thick-tufted pyramidal cells (TTCs), a detailed conductance-based model (CBM) was constructed for a 3D reconstructed exemplar TTC. The model exhibited spike initiation in the axon and reproduced the characteristic features of individual spikes as well as of the firing properties at the soma as recorded in a population of TTCs in young Wistar rats. When using these model parameters over the population of 28 3D reconstructed TTCs, both axonal and somatic ion channel densities had to be scaled linearly with the conductance load imposed on each of these compartments (ρaxon and ρ, respectively). Otherwise, the firing of model cells deviated, sometimes very significantly, from the experimental variability of the TTC e-class. The study provides experimentally testable predictions regarding the co-regulation of axo-somatic membrane ion channels density for cells with different dendritic conductance load, together with a simple and systematic method for generating reliable CBMs for the whole population of modeled neurons belonging to a particular e-class with variable morphology as found experimentally.
    Journal of Neurophysiology 03/2013; · 3.30 Impact Factor
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    ABSTRACT: A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.
    Nature Reviews Neuroscience 02/2013; · 31.38 Impact Factor
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    ABSTRACT: Autism is a neurodevelopmental condition diagnosed by impaired social interaction, abnormal communication and, stereotyped behaviors. While post-mortem and imaging studies have provided good insights into the neurobiological symptomology of autism, animal models can be used to study the neuroanatomical, neurophysiological and molecular mediators in more detail and in a more controlled environment. The valproic acid (VPA) rat model is an environmentally triggered model with strong construct and clinical validity. It is based on VPA teratogenicity in humans, where mothers who are medicated with VPA during early pregnancy show an increased risk for giving birth to an autistic child. In rats, early embryonic exposure, around the time of neural tube closure, leads to autism-like anatomical and behavioral abnormalities in the offspring. Considering the increasing use of the VPA rat model, we present our observations of the general health of Wistar dams treated with a single intraperitoneal injection of 500 or, 600 mg/kg VPA on embryonic day E12.5, as well as their male and female offspring, in comparison to saline-exposed controls. We report increased rates of complete fetal reabsorption after both VPA doses. VPA 500 mg/kg showed no effect on dam body weight during pregnancy or, on litter size. Offspring exposed to VPA 500 mg/kg showed smaller brain mass on postnatal days 1 (P1) and 14 (P14), in addition to abnormal nest seeking behavior at P10 in the olfactory discrimination test, relative to controls. We also report increased rates of physical malformations in the offspring, rare occurrences of chromodacryorrhea and, developmentally similar body mass gain. Further documentation of developmental health may guide sub-grouping of individuals in a way to better predict core symptom severity.
    Frontiers in Behavioral Neuroscience 01/2013; 7:88. · 4.76 Impact Factor
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    Rodrigo Perin, Martin Telefont, Henry Markram
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    ABSTRACT: The organization of connectivity in neuronal networks is fundamental to understanding the activity and function of neural networks and information processing in the brain. Recent studies show that the neocortex is not only organized in columns and layers but also, within these, into synaptically connected clusters of neurons (Ko et al., 2011; Perin et al., 2011). The recently discovered common neighbor rule, according to which the probability of any two neurons being synaptically connected grows with the number of their common neighbors, is an organizing principle for this local clustering. Here we investigated the theoretical constraints for how the spatial extent of neuronal axonal and dendritic arborization, heretofore described by morphological reach, the density of neurons and the size of the network determine cluster size and numbers within neural networks constructed according to the common neighbor rule. In the formulation we developed, morphological reach, cell density, and network size are sufficient to estimate how many neurons, on average, occur in a cluster and how many clusters exist in a given network. We find that cluster sizes do not grow indefinitely as network parameters increase, but tend to characteristic limiting values.
    Frontiers in Neuroanatomy 01/2013; 7:1. · 4.06 Impact Factor
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    ABSTRACT: Neuroligins (Nlgns) are postsynaptic cell adhesion molecules that form transynaptic complexes with presynaptic neurexins and regulate synapse maturation and plasticity. We studied the impact of the loss of Nlgn4 on the excitatory and inhibitory circuits in somatosensory cortical slices of juvenile mice by electrically stimulating these circuits using a multi-electrode array and recording the synaptic input to single neurons using the patch-clamp technique. We detected a decreased network response to stimulation in both excitatory and inhibitory circuits of Nlgn4 knock-out animals as compared to wild-type controls, and a decreased excitation-inhibition ratio. These data indicate that Nlgn4 is involved in the regulation of excitatory and inhibitory circuits and contributes to a balanced circuit response to stimulation.
    Scientific Reports 01/2013; 3:2897. · 5.08 Impact Factor
  • Shruti Muralidhar, Yun Wang, Henry Markram
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    ABSTRACT: Layer 1 of the neocortex is sparsely populated with neurons and heavily innervated by fibers from lower layers and proximal and distal brain regions. Understanding the potential functions of this layer requires a comprehensive understanding of its cellular and synaptic organization. We therefore performed a quantitative study of the microcircuitry of neocortical layer 1 (L1) in the somatosensory cortex in juvenile rats (P13-P16) using multi-neuron patch-clamp and 3D morphology reconstructions. Expert-based subjective classification of the morphologies of the recorded L1 neurons suggest 6 morphological classes: (1) the Neurogliaform cells with dense axonal arborizations (NGC-DA) and with sparse arborizations (NGC-SA), (2) the Horizontal Axon Cell (HAC), (3) those with descending axonal collaterals (DAC), (4) the large axon cell (LAC), and (5) the small axon cell (SAC). Objective, supervised and unsupervised cluster analyses confirmed DAC, HAC, LAC and NGC as distinct morphological classes. The neurons were also classified into 5 electrophysiological types based on the Petilla convention; classical non-adapting (cNAC), burst non-adapting (bNAC), classical adapting (cAC), classical stuttering (cSTUT), and classical irregular spiking (cIR). The most common electrophysiological type of neuron was the cNAC type (40%) and the most common morpho-electrical type was the NGC-DA-cNAC. Paired patch-clamp recordings revealed that the neurons were connected via GABAergic inhibitory synaptic connections with a 7.9% connection probability and via gap junctions with a 5.2% connection probability. Most synaptic connections were mediated by both GABAA and GABAB receptors (62.6%). A smaller fraction of synaptic connections were mediated exclusively by GABAA (15.4%) or GABAB (21.8%) receptors. Morphological 3D reconstruction of synaptic connected pairs of L1 neurons revealed multi-synapse connections with an average of 9 putative synapses per connection. These putative synapses were widely distributed with 39% on somata and 61% on dendrites. We also discuss the functional implications of this L1 cellular and synaptic organization in neocortical information processing.
    Frontiers in Neuroanatomy 01/2013; 7:52. · 4.06 Impact Factor
  • Rodrigo Perin, Henry Markram
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    ABSTRACT: The patch-clamp technique is today the most well-established method for recording electrical activity from individual neurons or their subcellular compartments. Nevertheless, achieving stable recordings, even from individual cells, remains a time-consuming procedure of considerable complexity. Automation of many steps in conjunction with efficient information display can greatly assist experimentalists in performing a larger number of recordings with greater reliability and in less time. In order to achieve large-scale recordings we concluded the most efficient approach is not to fully automatize the process but to simplify the experimental steps and reduce the chances of human error while efficiently incorporating the experimenter's experience and visual feedback. With these goals in mind we developed a computer-assisted system which centralizes all the controls necessary for a multi-electrode patch-clamp experiment in a single interface, a commercially available wireless gamepad, while displaying experiment related information and guidance cues on the computer screen. Here we describe the different components of the system which allowed us to reduce the time required for achieving the recording configuration and substantially increase the chances of successfully recording large numbers of neurons simultaneously.
    Journal of Visualized Experiments 01/2013;
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    ABSTRACT: It is well-established that synapse formation involves highly selective chemospecific mechanisms, but how neuron arbors are positioned before synapse formation remains unclear. Using 3D reconstructions of 298 neocortical cells of different types (including nest basket, small basket, large basket, bitufted, pyramidal, and Martinotti cells), we constructed a structural model of a cortical microcircuit, in which cells of different types were independently and randomly placed. We compared the positions of physical appositions resulting from the incidental overlap of axonal and dendritic arbors in the model (statistical structural connectivity) with the positions of putative functional synapses (functional synaptic connectivity) in 90 synaptic connections reconstructed from cortical slice preparations. Overall, we found that statistical connectivity predicted an average of 74 ± 2.7% (mean ± SEM) synapse location distributions for nine types of cortical connections. This finding suggests that chemospecific attractive and repulsive mechanisms generally do not result in pairwise-specific connectivity. In some cases, however, the predicted distributions do not match precisely, indicating that chemospecific steering and aligning of the arbors may occur for some types of connections. This finding suggests that random alignment of axonal and dendritic arbors provides a sufficient foundation for specific functional connectivity to emerge in local neural microcircuits.
    Proceedings of the National Academy of Sciences 09/2012; 109(42):E2885-94. · 9.81 Impact Factor
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    ABSTRACT: Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.
    Cerebral Cortex 09/2012; · 8.31 Impact Factor
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    ABSTRACT: We present a biologically inspired recurrent network of spiking neurons and a learning rule that enables the network to balance a ball on a flat circular arena and to steer it towards a target field, by controlling the inclination angles of the arena. The neural controller is a recurrent network of adaptive exponential integrate and fire neurons configured and connected to match properties of cortical layer IV. The network is used as a liquid state machine with four action cells as readout neurons. The solution of the task requires the controller to take its own reaction time into account by anticipating the future state of the controlled system. We demonstrate that the cortical network can robustly learn this task using a supervised learning rule that penalizes the error on the force applied to the arena.
    Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I; 09/2012
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    ABSTRACT: Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simulations is a major task. Yet, they lack the tools to efficiently work with data of this size. One problem many scientists share is the analysis of the massive spatial models they build. For several types of analysis they need to interactively follow the structures in the spatial model, e.g., the arterial tree, neuron fibers, etc., and issue range queries along the way. Each query takes long to execute, and the total time for executing a sequence of queries significantly delays data analysis. Prefetching the spatial data reduces the response time considerably, but known approaches do not prefetch with high accuracy. We develop SCOUT, a structure-aware method for prefetching data along interactive spatial query sequences. SCOUT uses an approximate graph model of the structures involved in past queries and attempts to identify what particular structure the user follows. Our experiments with neuroscience data show that SCOUT prefetches with an accuracy from 71% to 92%, which translates to a speedup of 4x-15x. SCOUT also improves the prefetching accuracy on datasets from other scientific domains, such as medicine and biology.
    08/2012;
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    ABSTRACT: The electrical diversity of neurons arises from the expression of different combinations of ion channels. The gene expression rules governing these combinations are not known. We examined the expression of twenty-six ion channel genes in a broad range of single neocortical neuron cell types. Using expression data from a subset of twenty-six ion channel genes in ten different neocortical neuronal types, classified according to their electrophysiological properties, morphologies and anatomical positions, we first developed an incremental Support Vector Machine (iSVM) model that prioritizes the predictive value of single and combinations of genes for the rest of the expression pattern. With this approach we could predict the expression patterns for the ten neuronal types with an average 10-fold cross validation accuracy of 87% and for a further fourteen neuronal types not used in building the model, with an average accuracy of 75%. The expression of the genes for HCN4, Kv2.2, Kv3.2 and Caβ3 were found to be particularly strong predictors of ion channel gene combinations, while expression of the Kv1.4 and Kv3.3 genes has no predictive value. Using a logic gate analysis, we then extracted a spectrum of observed combinatorial gene expression rules of twenty ion channels in different neocortical neurons. We also show that when applied to a completely random and independent data, the model could not extract any rules and that it is only possible to extract them if the data has consistent expression patterns. This novel strategy can be used for predictive reverse engineering combinatorial expression rules from single-cell data and could help identify candidate transcription regulatory processes.
    PLoS ONE 01/2012; 7(4):e34786. · 3.73 Impact Factor
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    ABSTRACT: Fast synaptic inhibition in the brain is mediated by the pre-synaptic release of the neurotransmitter γ-Aminobutyric acid (GABA)and the post-synaptic activation of GABA-sensitive ionotropic receptors. As with excitatory synapses, it is being increasinly appreciated that a variety of plastic processes occur at inhibitory synapses, which operate over a range of timescales. Here we examine a form of activity-dependent plasticity that is somewhat unique to GABAergic transmission. This involves short-lasting changes to the ionic driving force for the post-synaptic receptors, a process referred to as short-term ionic plasticity. These changes are directly related to the history of activity at inhibitory synapses and are influenced by a variety of factors including the location of the synapse and the post-synaptic cell's ion regulation mechanisms. We explore the processes underlying this form of plasticity, when and where it can occur, and how it is likely to impact network activity.
    Frontiers in Synaptic Neuroscience 01/2012; 4:5.
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    Frontiers in Synaptic Neuroscience 01/2012; 4:2.
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    ABSTRACT: Neuroscientists increasingly use computational tools in building and simulating models of the brain. The amounts of data involved in these simulations are immense and efficiently managing this data is key. One particular problem in analyzing this data is the scalable execution of range queries on spatial models of the brain. Known indexing approaches do not perform well even on today's small models which represent a small fraction of the brain, containing only few millions of densely packed spatial elements. The problem of current approaches is that with the increasing level of detail in the models, also the overlap in the tree structure increases, ultimately slowing down query execution. The neuroscientists' need to work with bigger and more detailed (denser) models thus motivates us to develop a new indexing approach. To this end we develop FLAT, a scalable indexing approach for dense data sets. We base the development of FLAT on the key observation that current approaches suffer from overlap in case of dense data sets. We hence design FLAT as an approach with two phases, each independent of density. In the first phase it uses a traditional spatial index to retrieve an initial object efficiently. In the second phase it traverses the initial object's neighborhood to retrieve the remaining query result. Our experimental results show that FLAT not only outperforms R-Tree variants from a factor of two up to eight but that it also achieves independence from data set size and density.
    01/2012;
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    Frontiers in Neuroanatomy 01/2012; 6:22. · 4.06 Impact Factor

Publication Stats

12k Citations
898.19 Total Impact Points

Institutions

  • 2013
    • Columbia University
      New York City, New York, United States
  • 2007–2013
    • Hebrew University of Jerusalem
      • • Department of Neurobiology
      • • Interdisciplinary Center for Neural Computation
      Yerushalayim, Jerusalem District, Israel
  • 2002–2013
    • École Polytechnique Fédérale de Lausanne
      • • Neural Microcircuitry Laboratory
      • • Blue Brain Project (BBP)
      Lausanne, VD, Switzerland
  • 2011
    • California Institute of Technology
      • Division of Biology
      Pasadena, CA, United States
  • 2008
    • Holon Institute of Technology
      Cholon, Tel Aviv, Israel
  • 2002–2008
    • Yale University
      • • Department of Computer Science
      • • Section of Nephrology
      New Haven, CT, United States
  • 2006
    • University of California, San Francisco
      San Francisco, California, United States
  • 1989–2005
    • Weizmann Institute of Science
      • Department of Neurobiology
      Israel
  • 2004
    • Universität Heidelberg
      • Clinical Neurology
      Heidelberg, Baden-Wuerttemberg, Germany
    • Tufts University
      Georgia, United States
  • 2002–2004
    • Graz University of Technology
      • Institute for Theoretical Computer Science
      Graz, Styria, Austria
  • 2001
    • Universität Bern
      Berna, Bern, Switzerland
  • 1997
    • Brown University
      Providence, Rhode Island, United States
  • 1994–1997
    • Max Planck Institute for Medical Research
      • Department of Molecular Neurobiology
      Heidelburg, Baden-Württemberg, Germany