Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

Hay, Etay; Hill, Sean; Schürmann, Felix; Markram, Henry; Segev, Idan

doi:10.1371/journal.pcbi.1002107

Cited by 317 publications

(849 citation statements)

References 78 publications

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“…Other free parameters are the densities of a leak conductance in each type of cellular compartment: somatic, axonal, basal dendrite, and apical dendrite (when present). In total, 15-16 free parameters are tuned to optimize these models using a genetic algorithm procedure (11,13). Several random seeds are used, and the best model across all runs is kept as the representative model for a given cell.…”

Section: Models Of Individual Neuronsmentioning

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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Section: Models Of Individual Neuronsmentioning

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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“…After creating a detailed multi-compartmental model of a L5 pyramidal neuron based on a combination of previous modeling work (Hay et al 2011) and dual soma and dendrite patch clamp recordings in V1, imposed barrages of dendritic and somatic excitatory synapses onto a single cell. The results of this simulation showed that the coincident input of perisomatic and apical input elicited Psyche, Signals and Systemsa burst of high-frequency action potentials at the soma, whereas only perisomatic or apical input in isolation would not.…”

Section: Active Membrane Currentsmentioning

confidence: 99%

“…Such models carry the potential of being able to link subcellular and cellular biophysics with locally measured signals such as cortical spiking, LFPs, Ca-imaging, etc. For example, morphologically detailed and biophysically realistic single-neuron (Gold et al 2006;Druckmann et al 2007;Hay et al 2011) and population models (Pettersen and Einevoll 2008;Lindén et al 2011;Schomburg et al 2012;Reimann et al 2013;Taxidis et al 2015) have offered considerable insights into extracellular spiking and LFP signals. Even more recently, large-scale simulation programs combining unprecedented level of detail have been initialized promising to unravel novel insights into a plethora of brain signals (e.g., Markram et al 2015).…”

Section: Local Monitoringmentioning

confidence: 99%

Psyche, Signals and Systems

Anastassiou

Shai

2016

Research and Perspectives in Neurosciences

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For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying understanding bridging between the micro-, meso-, and macro-scales of the brain and psychology. This chapter outlines an integrated view of the neurophysiological systems, psychophysical signals, and theoretical considerations related to consciousness. First, we review the signals that correlate to consciousness during psychophysics experiments. We then review the underlying neural mechanisms giving rise to these signals. Finally, we discuss the computational and theoretical functions of such neural mechanisms, and begin to outline means in which these are related to ongoing theoretical research.

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“…This is a fundamental limitation of all inverse solution approaches [5]. For computing, and especially large-scale modeling [36,39], sorting topologies is computationally intractable: it is the clique discovery problem, one of the bestknown NP-complete problems [48]. even simplifying concepts such as the common neighbor rule [83] do not significantly ease the topology discovery problem.…”

Section: Network Enumerationmentioning

confidence: 99%

High-Resolution Synaptic Connectomics

Marc

Jones

Sigulinsky

et al. 2015

Biological and Medical Physics, Biomedical Engineering

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high-speed, high-resolution connectomics enables unambiguous mapping of synapses, gap junctions, adherens junctions, and other forms of adjacency among neurons in complex neural systems such as brain and retina. This chapter reviews the motivations for generating complete network architectures; the technologies available for large-scale network acquisition, visualization, and analysis; the fusion of molecular markers with a high-resolution ultrastructure; new networks and organelles discovered by ultrastructural connectomics; and new technological advances needed to expand the applications of connectomics. Motivations for Ultrastructural Connectomicsa connectome is the complete set of cellular partners and connections for a neural region. it can be executed on the mesoscale (spatial resolution of magnetic resonance imaging or even conventional optical imaging) to map fiber networks or on the nanoscale (spatial resolution of electron imaging) to map synaptic networks. This review addresses our experience with high-resolution synaptic connectomics based on automated transmission electron microscope (aTeM) imaging.The notion of using computational methods to accelerate ultrastructural analysis is at least three decades old [92]. even so, computational imaging for electron microscopy did not become a mainstream strategy until recently. There were three reasons for this: slow acquisition speed, expensive storage, and weak analytical scale. Film-based imaging followed by high-performance digitization [40] or even digital camera acquisition followed by analysis was so slow that it had no competitive advantage. Second, data storage at the resolution required for synaptic identification and quantification was prohibitively expensive, especially for Nih-funded investigators. The third reason is less obvious: no formal rationale existed to motivate large-scale acquisitions.

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Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

Cited by 317 publications

References 78 publications

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

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

Psyche, Signals and Systems

High-Resolution Synaptic Connectomics

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