Dendritic encoding of sensory stimuli controlled by deep cortical interneurons

Murayama, Miyuki; Pérez-Garci, Enrique; Nevian, Thomas; Bock, Tobias; Senn, Walter; Larkum, Matthew E.

doi:10.1038/nature07663

Cited by 347 publications

(350 citation statements)

References 26 publications

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“…7c). Recent experimental evidence from awake rats suggests that these different types of inputs may also play a functional role in vivo (Murayama et al 2008).…”

Section: Discussionmentioning

confidence: 99%

“…Whether the extended geometry of pyramidal neurons offers real computational advantages, or whether it only solves the 'packing problem' of collecting a large amount of synapses for a single integration process, remains an open issue and it will not be discussed here. Instead, we will focus on some of the phenomena that depend critically on such extended geometry and cannot be captured with point-neuron approximations, like the dendritically induced gain modulation of the somatic response and its control by inhibition (Larkum et al 2004;Murayama et al 2008). This will allow us to characterize the response of layer 5 pyramidal neurons to noisy input currents which are simultaneously injected in the soma and in the apical dendrite.…”

Section: Response To Dendritic Inputs and Soma-dendritic Interactionsmentioning

confidence: 99%

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The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

et al. 2008

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The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane's inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite-soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons.

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“…7c). Recent experimental evidence from awake rats suggests that these different types of inputs may also play a functional role in vivo (Murayama et al 2008).…”

Section: Discussionmentioning

confidence: 99%

Section: Response To Dendritic Inputs and Soma-dendritic Interactionsmentioning

confidence: 99%

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

et al. 2008

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“…In vivo recordings of dendritic calcium activity has been restricted to anesthetized animals. In these studies, there is evidence for sensory stimulusevoked dendritic Ca 2ϩ spikes in the apical dendrites (11,12), which is, however, suppressed by powerful dendritic inhibitory mechanisms (13) that are more active during anesthesia (14). These studies also predict that dendritic activity should increase in the awake preparation; however, until now, dendritic recordings have not been feasible in awake animals.…”

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confidence: 99%

Enhanced dendritic activity in awake rats

Murayama

Larkum

2009

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

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Almost nothing is known about dendritic activity in awake animals and even less about its relationship to behavior. The tuft dendrites of layer 5 (L5) pyramidal neurons lie in layer 1, where long-range axons from secondary thalamic nuclei and higher cortical areas arrive. This class of input is very dependent on active thalamocortical loops and activity in higher brain areas and so is likely to be heavily influenced by the conscious state of the animal. If, as has been suggested, the dendrites of pyramidal neurons actively participate in this process, dendritic activity should greatly increase in the awake state. Here, we measured calcium activity in L5 pyramidal neuron dendrites using the ''periscope'' fiberoptic system. Recordings were made in the sensorimotor cortex of awake and anesthetized rats following sensory stimulation of the hindlimb. Bi-phasic dendritic responses evoked by hindlimb stimulation were extremely dependent on brain state. In the awake state, there was a prominent slow, delayed response whose integral was on average 14-fold larger than in the anesthetized state. Moreover, the dramatic increases in dendritic activity closely correlated to the strength of subsequent hindlimb movement. These changes were confined to L5 pyramidal dendrites and were not reflected in the response of layer 2/3 (L2/3) neurons to air-puff stimuli in general (which actually decreased in the awake state). The results demonstrate that the activity of L5 pyramidal dendrites is a neural correlate of awake behavior.calcium imaging ͉ calcium spike ͉ fiberoptics ͉ layer 5 pyramidal neuron ͉ neocortex T he changes that occur at the neuronal level from the anesthetized to the awake brain states is one of the most fundamental questions remaining in neuroscience (1-3). One leading hypothesis is that during awake sensory processing, thalamo-cortical interactions involving deep-layer pyramidal neurons result in non-specific thalamic nuclei projecting feedback input to the tuft dendrites of L5 pyramidal neurons (4). It is also hypothesized that higher cortical areas (more active during conscious processing) project to the same tuft dendrites in L1 in primary sensory areas (5, 6). Both these hypotheses predict specific input to pyramidal cell dendrites during the awake state. Given the intrinsic excitability of pyramidal tuft dendrites with their ability to sustain regenerative Ca 2ϩ activity (7,8), these hypotheses also predict that dendritic activity in these neurons should increase in the awake state.So far, it has only been possible to test the effectiveness of feedback input to L1 in vitro (9, 10). In vivo recordings of dendritic calcium activity has been restricted to anesthetized animals. In these studies, there is evidence for sensory stimulusevoked dendritic Ca 2ϩ spikes in the apical dendrites (11, 12), which is, however, suppressed by powerful dendritic inhibitory mechanisms (13) that are more active during anesthesia (14). These studies also predict that dendritic activity should increase in the awake preparation; howe...

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“…Only on the basis of such prevalence numbers is it possible to interpret data on single-cell physiology (1)(2)(3)(4)(5)(6)(7)(8) and synaptic connections of pairs of neurons (9)(10)(11)(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)(14)(15).…”

mentioning

confidence: 99%

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.

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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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Dendritic encoding of sensory stimuli controlled by deep cortical interneurons

Cited by 347 publications

References 26 publications

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs

Enhanced dendritic activity in awake rats

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

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