Reconstruction and Simulation of Neocortical Microcircuitry

Markram, Henry; Müller, Eilif; Ramaswamy, Srikanth; Reimann, Michael; Abdellah, Marwan; Sanchez, Carlos Aguado; Ailamaki, Anastasia; Alonso‐Nanclares, Lidia; Antille, Nicolas; Arsever, Selim; Kahou, Guy Antoine Atenekeng; Berger, Thomas; Bilgili, Ahmet; Buncic, Nenad; Chalimourda, Athanassia; Chindemi, Giuseppe; Courcol, Jean Denis; Delalondre, Fabien; Delattre, Vincent; Druckmann, Shaul; Dumusc, Raphael; Dynes, J.A.; Eilemann, Stefan; Gal, Eyal; Gevaert, Michael; Ghobril, Jean Pierre; Gidon, Albert; Graham, Joe W.; Gupta, A. P.; Haenel, Valentin; Hay, Etay; Heinis, Thomas; Hernando, Juan; Hines, Michael L.; Kanari, Lida; Keller, Daniel; Kenyon, James L.; Khazen, Georges; Kim, Yihwa; King, James G.; Kisvárday, Zoltán F.; Kumbhar, Pramod; Lasserre, Sébastien; Bé, Jean Vincent Le; Magalhães, Bruno R. C.; Merchán-Pérez, Ángel; Meystre, Julie; Morrice, Benjamin Roy; Muller, Jeffrey; Muñoz, Alberto; Muralidhar, Shruti; Muthurasa, Keerthan; Nachbaur, Daniel; Newton, Taylor H.; Nolte, Max; Ovcharenko, A. G.; Palacios, Juan C.; Pastor, Luis; Perin, Rodrigo; Ranjan, Rajnish; Riachi, Imad; Rodríguez, José Rodrigo; Riquelme, Juan Luis; Rössert, Christian; Sfyrakis, Konstantinos; Shi, Ying; Shillcock, Julian C.; Silberberg, Gilad; Silva, Ricardo; Tauheed, Farhan; Telefont, Martin; Toledo-Rodriguez, Maria; Tränkler, Thomas; Geit, Werner Van; Díaz, Jafet Villafranca; Walker, Richard; Wang, Yun; Zaninetta, Stefano M.; DeFelipe, Javier; Hill, Sean; Segev, Idan; Schürmann, Felix

doi:10.1016/j.cell.2015.09.029

Cited by 1,279 publications

(2,034 citation statements)

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“…We have shown previously that under these conditions of strong connectivity, firing of a small fraction of the neurons, presumably those neurons that are more sensitive to inputs and lead the activity, suffices to ignite the rest of the network (28)(29)(30). An additional effect of increased network synchronization with elevated [Ca 2+ ] o was recently reported in cortical slices (31), but through differential sensitivity of excitatory and inhibitory synapses to calcium.…”

Section: Discussionmentioning

confidence: 96%

Network synchronization in hippocampal neurons

Penn

Segal

Moses

2016

Proc. Natl. Acad. Sci. U.S.A.

120

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Oscillatory activity is widespread in dynamic neuronal networks. The main paradigm for the origin of periodicity consists of specialized pacemaking elements that synchronize and drive the rest of the network; however, other models exist. Here, we studied the spontaneous emergence of synchronized periodic bursting in a network of cultured dissociated neurons from rat hippocampus and cortex. Surprisingly, about 60% of all active neurons were self-sustained oscillators when disconnected, each with its own natural frequency. The individual neuron's tendency to oscillate and the corresponding oscillation frequency are controlled by its excitability. The single neuron intrinsic oscillations were blocked by riluzole, and are thus dependent on persistent sodium leak currents. Upon a gradual retrieval of connectivity, the synchrony evolves: Loose synchrony appears already at weak connectivity, with the oscillators converging to one common oscillation frequency, yet shifted in phase across the population. Further strengthening of the connectivity causes a reduction in the mean phase shifts until zerolag is achieved, manifested by synchronous periodic network bursts. Interestingly, the frequency of network bursting matches the average of the intrinsic frequencies. Overall, the network behaves like other universal systems, where order emerges spontaneously by entrainment of independent rhythmic units. Although simplified with respect to circuitry in the brain, our results attribute a basic functional role for intrinsic single neuron excitability mechanisms in driving the network's activity and dynamics, contributing to our understanding of developing neural circuits.neuron | network | oscillator | synchrony | persistent Na current P eriodicity emerges as a key physiological characteristic at all levels of neuronal activity, from the dynamics of neurons at subthreshold potentials (1, 2), through rhythmic neuronal ensembles within local networks, and all of the way up to global oscillations measured by electroencephalography (EEG) (3). The range of observed frequencies is surprisingly wide, from the millisecond range typical for interspike intervals all of the way to several seconds in the case of slow EEG. Over the years, accumulating evidence and theory have attributed different mechanisms for the origin of each measured periodic activity. Within local networks, the role of known collective mechanisms for periodicity, such as the balance between excitatory and inhibitory neurons and recurrent network architecture (4-6), is often contrasted with single neuron contributions, for example, the role of pacemaker neurons in oscillatory network dynamics (7). Although the physiological properties of single neurons are diverse and well documented (2, 8), their role in emergent network oscillations was predicted theoretically (9), but has not been observed experimentally. This contribution may involve a subtle interplay between intrinsic excitability and network connectivity (10, 11).The connectivity and excitability together determi...

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Section: Discussionmentioning

confidence: 96%

Network synchronization in hippocampal neurons

Penn

Segal

Moses

2016

Proc. Natl. Acad. Sci. U.S.A.

120

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“…The project proposes to infer the structure and function of recurrent connectivity as well as feedback from higher areas and estimate the computational role that each area plays in the visual circuit. The modeling part of MindScope shares some of the same goals as the Blue Brain Project/Human Brain Project at the EPFL in Switzerland (15). Their focus is on reproducing the firing behavior of rat somatosensory cortex in both slice and the animal using detailed biophysical models.…”

Section: Discussionmentioning

confidence: 99%

Inferring cortical function in the mouse visual system through large-scale systems neuroscience

Hawrylycz

Anastassiou

Arkhipov

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.T he neocortical sheet is a layered structure with a thickness that varies by a factor of two to three, whereas its surface area varies by 50,000 between the small smoky shrew and the massive blue whale. A unique hallmark of mammals, neocortex is a highly versatile, scalable, ∼2D computational tissue that excels at real time sensory processing across modalities and making and storing associations as well as planning and producing complex motor patterns. Neocortex consists of smaller modular units (columnar circuits that reach across the depth of cortex) broadly repeated iteratively across the cortical sheet. These modules vary considerably in their connectivity and properties between regions, with some controversy whether there is, indeed, a single canonical function performed by any and all neocortical columns.A deep understanding of cortex necessitates measuring relevant biophysical variables, such as recording action and membrane potentials, and relating them to genetically identified cell types. Mapping, observing, and intervening in widespread but highly specific cellular activity are more readily accomplished in the mouse, Mus musculus, than in the human brain. The brain of the laboratory mouse is more than three orders of magnitude smaller than the human brain in weight (0.4 vs. 1,350 g) and contains 71 million vs. 86 billion nerve cells for the entire brain and 14 million vs. 16 billion nerve cells for neocortex (1).The Allen Institute for Brain Science is engaged in a 10-y, highthroughput, milestone-driven effort to characterize all cortical cell types for the mouse cortex to build a small number of distinct experimental and computational platforms, called brain observatories, for studying behaving mice and to construct abstract and biophysically realisti...

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“…The massive data produced by CERN required the construction of new software and hardware systems (Armstrong et al, 1994). International attempts to model and simulate the brain based on massive curated experimental data revealed new challenges in the link between measures and simulation (Markram, 2006;Markram et al, 2015). The Big Science narrative insists on tackling the challenge of organizing collaborations on an international level despite academic competition, designing information pipelines to harness the massiveness of the data produced and on the relevant use of algorithmic simulation to test the coherence of the data and extrapolate for making new predictions.…”

Section: Data Bigness In Datafication Processesmentioning

confidence: 99%

Big Data of the Past

Kaplan

Lenardo

2017

Front. Digit. Humanit.

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Big Data is not a new phenomenon. History is punctuated by regimes of data acceleration, characterized by feelings of information overload accompanied by periods of social transformation and the invention of new technologies. During these moments, private organizations, administrative powers, and sometimes isolated individuals have produced important datasets, organized following a logic that is often subsequently superseded but was at the time, nevertheless, coherent. To be translated into relevant sources of information about our past, these document series need to be redocumented using contemporary paradigms. The intellectual, methodological, and technological challenges linked to this translation process are the central subject of this article.

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Reconstruction and Simulation of Neocortical Microcircuitry

Abstract: VIDEO ABSTRACT.

Cited by 1,279 publications

References 215 publications

Network synchronization in hippocampal neurons

Network synchronization in hippocampal neurons

Inferring cortical function in the mouse visual system through large-scale systems neuroscience

Big Data of the Past

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