Whose cortical column would that be?

Costa, Nuno Maçarico da

doi:10.3389/fnana.2010.00016

Cited by 77 publications

(72 citation statements)

References 75 publications

(136 reference statements)

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“…2D), which would also indicate a cylindrical outline of a cortical column in supragranular layers. This finding can also be taken as supporting the assumption that cortical columns are relevant functional units in neocortex beyond the cortical input layer 4 (50)(51)(52) [a different view on cortical columns is presented elsewhere (53,54)]. …”

Section: Molecular Markers Of Ins In a Corticalsupporting

confidence: 58%

Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A

Meyer

Schwarz

Wimmer

et al. 2011

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

213

170

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Although physiological data on microcircuits involving a few inhibitory neurons in the mammalian cerebral cortex are available, data on the quantitative relation between inhibition and excitation in cortical circuits involving thousands of neurons are largely missing. Because the distribution of neurons is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than to rely on sparse sampling methods. Here, we report the comprehensive mapping of interneurons (INs) in cortical columns of rat somatosensory cortex, immunolabeled for neuron-specific nuclear protein and glutamate decarboxylase. We found that a column contains ∼2,200 INs (11.5% of ∼19,000 neurons), almost a factor of 2 less than previously estimated. The density of GABAergic neurons was inhomogeneous between layers, with peaks in the upper third of L2/3 and in L5A. IN density therefore defines a distinct layer 2 in the sensory neocortex. In addition, immunohistochemical markers of IN subtypes were layer-specific. The "hot zones" of inhibition in L2 and L5A match the reported low stimulus-evoked spiking rates of excitatory neurons in these layers, suggesting that these inhibitory hot zones substantially suppress activity in the neocortex.F or a quantitative understanding of the interaction between excitatory and inhibitory synaptic transmission in the neocortex, it is crucial to obtain data on the absolute numbers and the relative distribution of excitatory and inhibitory (mostly GABAergic) neurons [interneurons (INs)] in a cortical column. Only on the basis of such prevalence numbers is it possible to interpret data on single-cell physiology (1-8) and synaptic connections of pairs of neurons (9-12) at the circuit level. The distribution of cortical neurons and INs has therefore been the objective of several studies over the past decades (13-15). Statistical sampling methods were used because mapping tens of thousands of neurons was not feasible. Although the nominal error bounds of such methods are in the range of 10-20%, the reported absolute numbers differed strongly among studies, especially for sparse neuron populations, such as INs and their subtypes. The ratio of INs reported for the somatosensory cortex varied between 15% and 25%, thus by almost a factor of 2 (13,16,17). Data on possible differences in IN density between layers in a cortical column were contradictory (13,15,16,18,19).To resolve these substantial discrepancies, we undertook the complete mapping of INs in entire cortical columns using the cytoarchitectonic barrels in L4 as a reference frame for the thalamocortical innervation volume (20) in postnatal day (P) 25-36 rat vibrissal cortex. Our data show that the overall prevalence of inhibitory neurons was previously overestimated by almost a factor of 2. We find a unique substructure of the distribution of INs in a cortical column that can explain the layer-specific differences in the excitability in neocortex. The distribution of INs defines layer 2 as a distinct neocortical layer.

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Section: Molecular Markers Of Ins In a Corticalsupporting

confidence: 58%

Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A

Meyer

Schwarz

Wimmer

et al. 2011

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

213

170

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“…The network imposes an interpretation on the noisy or incomplete input by displaying one of a number of stereotypical responses. The connectivity patterns of these WTA networks are consistent with both intracellular recordings in cat visual cortex (Douglas, Martin, & Whitteridge, 1989;Douglas & Martin, 1991) and anatomical connectivity data measured in cat visual cortex and rat auditory cortex (da Costa & Martin, 2010;Martin, 2011), which point to a high level of recurrency with many excitatory, inhibitory, and excitatory-inhibitory loops (Binzegger, Douglas, & Martin, 2004;Thomson, West, Wang, & Bannister, 2002). These excitatory-inhibitory recurrent loops can produce oscillatory patterns of activity if the time constant of the inhibitory population in the WTA network is made sufficiently long compared to the time constant of the excitatory populations (Ermentrout, 1992).…”

Section: Introductionsupporting

confidence: 52%

Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits

2014

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Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits. The network architecture is consistent with mammalian cortical connectivity data and uses realistic neuronal and synaptic dynamics that give rise to noise-robust patterns of sequential activity. The novel aspect of the network we propose lies in its ability to produce robust patterns of sequential activity that can be halted, resumed, and readily modulated by external input, and in its ability to make use of realistic plastic synapses to learn and reproduce the arbitrary input-imposed sequential patterns. Sequential activity takes the form of a single activity bump that stably propagates through multiple WTA circuits along one of a number of possible paths. Because the network can be configured to either generate spontaneous sequences or wait for external inputs to trigger a transition in the sequence, it provides the basis for creating state-dependent perception-action loops. We first analyze a rate-based approximation of the proposed spiking network to highlight the relevant features of the network dynamics and then show numerical simulation results with spiking neurons, realistic conductance-based synapses, and spike-timing dependent plasticity (STDP) rules to validate the rate-based model. Giacomo Indiveri giacomo@ini.uzh.ch Institute for Neuroinformatics University of Zurich and ETH Zurich Zurich 8057, SwitzerlandUnderstanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits. The network architecture is consistent with mammalian cortical connectivity data and uses realistic neuronal and synaptic dynamics that give rise to noise-robust patterns of sequential activity. The novel aspect of the network we propose lies in its ability to produce robust patterns of sequential activity that can be halted, resumed, and readily modulated by external input, and in its ability to make use of realistic plastic synapses to learn and reproduce the arbitrary input-imposed sequential patterns. Sequential activity takes the form of a single activity bump that sta...

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“…Orientation selectivity is the most widely studied feature and a continuous map with a quasi-periodic layout for preferred orientation is present across the entire primary visual cortex 10,11 . Integrating the synaptic, cellular and network contributions that lead to stimulus selective responses in these functional maps requires the hybridization of imaging techniques that span sub-micron to millimeter spatial scales.…”

mentioning

confidence: 99%

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

O’Herron

Shen²,

Lu³

et al. 2012

JoVE

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In the primary visual cortex of non-rodent mammals, neurons are clustered according to their preference for stimulus features such as orientation [1][2][3][4] , direction [5][6][7] , ocular dominance 8,9 and binocular disparity 9 . Orientation selectivity is the most widely studied feature and a continuous map with a quasi-periodic layout for preferred orientation is present across the entire primary visual cortex 10,11 . Integrating the synaptic, cellular and network contributions that lead to stimulus selective responses in these functional maps requires the hybridization of imaging techniques that span sub-micron to millimeter spatial scales. With conventional intrinsic signal optical imaging, the overall layout of functional maps across the entire surface of the visual cortex can be determined 12 . The development of in vivo two-photon microscopy using calcium sensitive dyes enables one to determine the synaptic input arriving at individual dendritic spines 13 or record activity simultaneously from hundreds of individual neuronal cell bodies 6,14 . Consequently, combining intrinsic signal imaging with the sub-micron spatial resolution of two-photon microscopy offers the possibility of determining exactly which dendritic segments and cells contribute to the micro-domain of any functional map in the neocortex. Here we demonstrate a high-yield method for rapidly obtaining a cortical orientation map and targeting a specific micro-domain in this functional map for labeling neurons with fluorescent dyes in a non-rodent mammal. With the same microscope used for two-photon imaging, we first generate an orientation map using intrinsic signal optical imaging. Then we show how to target a micro-domain of interest using a micropipette loaded with dye to either label a population of neuronal cell bodies or label a single neuron such that dendrites, spines and axons are visible in vivo. Our refinements over previous methods facilitate an examination of neuronal structure-function relationships with sub-cellular resolution in the framework of neocortical functional architectures. Video LinkThe video component of this article can be found at http://www.jove.com/video/50025/ Protocol 1. Surgical Preparation 1. Induce anesthesia and continuously monitor heart rate, end tidal CO 2 , EEG, and temperature. All procedures were approved by the Institutional Animal Care and Use Committee of the Medical University of South Carolina and were based on those we previously published 9,15 . 2. Expose the dorsal surface of the skull by cutting the skin with a scalpel blade. Dissect the connective tissues overlying the bone using a Brudon curette. Clean the bone using cotton tipped applicators and cotton gauze. Apply bone wax (as needed) to the skull, to stop occasional bleeding through small emissary veins. 3. Attach a titanium or stainless steel head plate to the skull (over the region of interest where the craniotomy will be performed) by using dental cement mixed with black paint (see Materials). A rectangular opening in the ce...

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Whose cortical column would that be?

Cited by 77 publications

References 75 publications

Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A

Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A

Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

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