Recovery kinetics of short-term depression of GABAergic and glutamatergic synapses at layer 2/3 pyramidal cells in the mouse barrel cortex

Introduction

Short-term synaptic plasticity (STP) is a widespread mechanism underlying activity-dependent modifications of cortical networks.

Methods

To investigate how STP influences excitatory and inhibitory synapses in layer 2/3 of mouse barrel cortex, we combined whole-cell patch-clamp recordings from visually identified pyramidal neurons (PyrN) and parvalbumin-positive interneurons (PV-IN) of cortical layer 2/3 in acute slices with electrical stimulation of afferent fibers in layer 4 and optogenetic activation of PV-IN.

Results

These experiments revealed that electrical burst stimulation (10 pulses at 10 Hz) of layer 4 afferents to layer 2/3 neurons induced comparable short-term depression (STD) of glutamatergic postsynaptic currents (PSCs) in PyrN and in PV-IN, while disynaptic GABAergic PSCs in PyrN showed a stronger depression. Burst-induced depression of glutamatergic PSCs decayed within <4 s, while the decay of GABAergic PSCs required >11 s. Optogenetically-induced GABAergic PSCs in PyrN also demonstrated STD after burst stimulation, with a decay of >11 s. Excitatory postsynaptic potentials (EPSPs) in PyrN were unaffected after electrical burst stimulation, while a selective optogenetic STD of GABAergic synapses caused a transient increase of electrically evoked EPSPs in PyrN.

Discussion

In summary, these results demonstrate substantial short-term plasticity at all synapses investigated and suggest that the prominent STD observed in GABAergic synapses can moderate the functional efficacy of glutamatergic STD after repetitive synaptic stimulations. This mechanism may contribute to a reliable information flow toward the integrative layer 2/3 for complex time-varying sensory stimuli.

Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons

GABAergic interneurons tend to diversify into similar classes across telencephalic regions. However, it remains unclear whether the electrophysiological and molecular properties commonly used to define these classes are discriminant in the hilus of the dentate gyrus. Here, using patch-clamp combined with single cell RT-PCR, we compare the relevance of commonly used electrophysiological and molecular features for the clustering of GABAergic interneurons sampled from the mouse hilus and primary sensory cortex. While unsupervised clustering groups cortical interneurons into well-established classes, it fails to provide a convincing partition of hilar interneurons. Statistical analysis based on resampling indicates that hilar and cortical GABAergic interneurons share limited homology. While our results do not invalidate the use of classical molecular marker in the hilus, they indicate that classes of hilar interneurons defined by the expression of molecular markers do not exhibit strongly discriminating electrophysiological properties.

The impact of neuron morphology on cortical network architecture

The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons, depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density, and the cellular diversity of the neuropil. The higher these three factors are, the more recurrent is the topology of the network. Conversely, the lower these factors are, the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular, and network scales from the specific morphological properties of its neuronal constituents.

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Neuronal network architectures reflect the morphologies of their constituents

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Morphology predicts nonrandom connectivity from subcellular to network scales

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Morphology predicts connectivity patterns consistent with those observed empirically

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Neuron morphology is a major source for wiring specificity in the cerebral cortex

Cross-Whisker Adaptation of Neurons in Layer 2/3 of the Rat Barrel Cortex

Neurons in the barrel cortex respond preferentially to stimulation of one principal whisker and weakly to several adjacent whiskers. Such integration exists already in layer 4, the pivotal recipient layer of thalamic inputs. Previous studies show that cortical neurons gradually adapt to repeated whisker stimulations and that layer 4 neurons exhibit whisker specific adaptation and no apparent interactions with other whiskers. This study aimed to study the specificity of adaptation of layer 2/3 cortical cells. Towards this aim, we compared the synaptic response of neurons to either repetitive stimulation of one of two responsive whiskers or when repetitive stimulation of the two whiskers was interleaved. We found that in most layer 2/3 cells adaptation is whisker-specific. These findings indicate that despite the multi-whisker receptive fields in the cortex, the adaptation process for each whisker-pathway is mostly independent of other whiskers. A mechanism allowing high responsiveness in complex environments.

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

Auditory Long-Range Parvalbumin Cortico-Striatal Neurons

Previous studies have shown that cortico-striatal pathways link auditory signals to action-selection and reward-learning behavior through excitatory projections. Only recently it has been demonstrated that long-range GABAergic cortico-striatal somatostatin-expressing neurons in the auditory cortex project to the dorsal striatum, and functionally inhibit the main projecting neuronal population, the spiny projecting neuron. Here we tested the hypothesis that parvalbumin-expressing neurons of the auditory cortex can also send long-range projections to the auditory striatum. To address this fundamental question, we took advantage of viral and non-viral anatomical tracing approaches to identify cortico-striatal parvalbumin neurons (CS-Parv inhibitory projections → auditory striatum). Here, we describe their anatomical distribution in the auditory cortex and determine the anatomical and electrophysiological properties of layer 5 CS-Parv neurons. We also analyzed their characteristic voltage-dependent membrane potential gamma oscillation, showing that intrinsic membrane mechanisms generate them. The inherent membrane mechanisms can also trigger intermittent and irregular bursts (stuttering) of the action potential in response to steps of depolarizing current pulses.

Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers

Pyramidal tract neurons (PTs) represent the major output cell type of the mammalian neocortex. Here, we report the origins of the PTs’ ability to respond to a broad range of stimuli with onset latencies that rival or even precede those of their intracortical input neurons. We find that neurons with extensive horizontally projecting axons cluster around the deep-layer terminal fields of primary thalamocortical axons. The strategic location of these corticocortical neurons results in high convergence of thalamocortical inputs, which drive reliable sensory-evoked responses that precede those in other excitatory cell types. The resultant fast and horizontal stream of excitation provides PTs throughout the cortical area with input that acts to amplify additional inputs from thalamocortical and other intracortical populations. The fast onsets and broadly tuned characteristics of PT responses hence reflect a gating mechanism in the deep layers, which assures that sensory-evoked input can be reliably transformed into cortical output.

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Simulations predict in vivo responses for major output cell type of the neocortex

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Simulations reveal strategy how to test the origins of cortical output empirically

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Manipulations confirm that deep-layer corticocortical neurons gate cortical output

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Gating of cortical output originates from deep-layer thalamocortical input stratum

Anatomical Correlates of Local, Translaminar, and Transcolumnar Inhibition by Layer 6 GABAergic Interneurons in Somatosensory Cortex

In the vibrissal area of rodent somatosensory cortex, information on whisker stimulation is processed by neuronal networks in a corresponding cortical column. To understand how sensory stimuli are represented in a column, it is essential to identify cell types constituting these networks. Layer 6 (L6) comprises 25% of all neurons in a column. In rats, 430 of these are inhibitory interneurons (INs). Little is known about the axon projection of L6 INs with reference to columnar and laminar organization. We quantified axonal projections of L6 INs (n = 68) with reference to columns and layers in somatosensory cortex of rats. We found distinct projection types differentially targeting layers of a cortical column. The majority of L6 INs did not show a column-specific innervation, densely projecting to neighboring columns as well as the home column. However, a small fraction targeted granular and supragranular layers, where axon projections were confined to the home column. We also quantified putative innervation of pyramidal cells as a functional correlate of axonal distribution. Electrophysiological properties were not correlated to axon projection. The quantitative data on axonal projections and electrophysiological properties of L6 INs can guide future studies investigating cortical processing of sensory information at the single cell level.

Morphological and Functional Characterization of Non-fast-Spiking GABAergic Interneurons in Layer 4 Microcircuitry of Rat Barrel Cortex

GABAergic interneurons are notorious for their heterogeneity, despite constituting a small fraction of the neuronal population in the neocortex. Classification of interneurons is crucial for understanding their widespread cortical functions as they provide a complex and dynamic network, balancing excitation and inhibition. Here, we investigated different types of non-fast-spiking (nFS) interneurons in Layer 4 (L4) of rat barrel cortex using whole-cell patch-clamp recordings with biocytin-filling. Based on a quantitative analysis on a combination of morphological and electrophysiological parameters, we identified 5 distinct types of L4 nFS interneurons: 1) trans-columnar projecting interneurons, 2) locally projecting non-Martinotti-like interneurons, 3) supra-granular projecting Martinotti-like interneurons, 4) intra-columnar projecting VIP-like interneurons, and 5) locally projecting neurogliaform-like interneurons. Trans-columnar projecting interneurons are one of the most striking interneuron types, which have not been described so far in Layer 4. They feature extensive axonal collateralization not only in their home barrel but also in adjacent barrels. Furthermore, we identified that most of the L4 nFS interneurons express somatostatin, while few are positive for the transcription factor Prox1. The morphological and electrophysiological characterization of different L4 nFS interneuron types presented here provides insights into their synaptic connectivity and functional role in cortical information processing.

Cortical Circuits of Callosal GABAergic Neurons

Anatomical studies have shown that the majority of callosal axons are glutamatergic. However, a small proportion of callosal axons are also immunoreactive for glutamic acid decarboxylase, an enzyme required for gamma-aminobutyric acid (GABA) synthesis and a specific marker for GABAergic neurons. Here, we test the hypothesis that corticocortical parvalbumin-expressing (CC-Parv) neurons connect the two hemispheres of multiple cortical areas, project through the corpus callosum, and are a functional part of the local cortical circuit. Our investigation of this hypothesis takes advantage of viral tracing and optogenetics to determine the anatomical and electrophysiological properties of CC-Parv neurons of the mouse auditory, visual, and motor cortices. We found a direct inhibitory pathway made up of parvalbumin-expressing (Parv) neurons which connects corresponding cortical areas (CC-Parv neurons → contralateral cortex). Like other Parv cortical neurons, these neurons provide local inhibition onto nearby pyramidal neurons and receive thalamocortical input. These results demonstrate a previously unknown long-range inhibitory circuit arising from a genetically defined type of GABAergic neuron that is engaged in interhemispheric communication.

Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits

Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems' emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired cortical microcircuits tend to assume (often for the sake of analytical tractability) a great degree of homogeneity in both neuronal and synaptic/connectivity parameters. While simplification and reductionism are necessary to understand the brain's functional principles, disregarding the existence of the multiple heterogeneities in the cortical composition, which may be at the core of its computational proficiency, will inevitably fail to account for important phenomena and limit the scope and generalizability of cortical models. We address these issues by studying the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data. This approach was made possible by the emergence of large-scale, well curated databases, as well as the substantial improvements in experimental methodologies achieved over the last few years. Our results show that variability in single neuron parameters is the dominant source of functional specialization, leading to highly proficient microcircuits with much higher computational power than their homogeneous counterparts. We further show that fully heterogeneous circuits, which are closest to the biophysical reality, owe their response properties to the differential contribution of different sources of heterogeneity.

Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to in vivo Whole-Cell Patch-Clamp Data

A lot of efforts have been made to understand the structure and function of neocortical circuits. In fact, a promising way to understand the functions of cortical circuits is the classification of the neural types, based on their different properties. Recent studies focused on applying modern computational methods to classify neurons based on molecular, morphological, physiological, or mixed of these criteria. Although there are studies in the literature on in vitro/vivo extracellular or in vitro intracellular recordings, a study on the classification of neuronal types using in vivo whole-cell patch-clamp recordings is still lacking. We thus proposed a novel semi-supervised classification method based on waveform shape of neurons' spikes using in vivo whole-cell patch-clamp recordings. We, first, detected spike candidates. Then discriminative features were extracted from the time samples of the spikes using discrete cosine transform. We then extracted the center of clusters using fuzzy c-mean clustering and finally, the neurons were classified using the minimum distance classifier. We distinguished three types of neurons: excitatory pyramidal cells (Pyr) and two types of inhibitory neurons: GABAergic- parvalbumin positive (PV), and somatostatin positive (SST) non-pyramidal cells in layer II/III of the mice primary visual cortex. We used 10-fold cross validation in our study. The classification accuracy for PV, Pyr, and SST was 91.59 ± 1.69, 97.47 ± 0.67, and 89.06 ± 1.99, respectively. Overall, the algorithm correctly classified 92.67 ± 0.54% of the cells, confirming the relative robustness of the discriminant functions. The performance of the method was further assessed on in vitro recordings by using a pool of 50 neurons from Allen institute Cell Types Database (5 major subtypes of neurons: Pyr, PV, SST, 5HT3a, and vasoactive intestinal peptide (VIP) cells). Its overall accuracy was 84.13 ± 0.81% on this data set using cross validation framework. The proposed algorithm is thus a promising new tool in recognizing cell's type with high accuracy in laboratories using in vivo/vitro whole-cell patch-clamp recording technique. The developed programs and the entire dataset are available online to interested readers.

Quantitative Association of Anatomical and Functional Classes of Olfactory Bulb Neurons

Juxtaglomerular cells (JGCs) of the olfactory bulb (OB) glomerular layer (GL) play a fundamental role in olfactory information processing. Their variability in morphology, physiology, and connectivity suggests distinct functions. The quantitative understanding of population-wise morphological and physiological properties and a comprehensive classification based on quantitative parameters, however, is still lacking, impeding the analysis of microcircuits. Here, we provide multivariate clustering of 95 in vitro sampled cells from the GL of the mouse (male or female C57BL/6) OB and perform detailed morphological and physiological characterization for the seven computed JGC types. Using a classifier based on a subselection of parameters, we identified the neuron types in paired recordings to characterize their functional connectivity. We found that 4 of the 7 clusters comply with prevailing concepts of GL cell types, whereas the other 3 represent own distinct entities. We have labeled these entities horizontal superficial tufted cell (hSTC), vertical superficial tufted cell, and microglomerular cell (MGC): The hSTC is a tufted cell with a lateral dendrite that much like mitral cells and tufted cells receives excitatory inputs from the external tufted cell but likewise serves as an excitatory element for glomerular interneurons. The vertical superficial tufted cell, on the other hand, represents a tufted cell type with vertically projecting basal dendrites. We further define the MGC, characterized by a small dendritic tree and plateau action potentials. In addition to olfactory nerve-driven and external tufted cell driven interneurons, these MGCs represent a third functionally distinct type, the hSTC-driven interneurons. The presented correlative analysis helps to bridge the gap between branching patterns and cellular functional properties, permitting the integration of results from in vivo recordings, advanced morphological tools, and connectomics.

SIGNIFICANCE STATEMENT The variance of neuron properties is a feature across mammalian cerebral circuits, contributing to signal processing and adding computational robustness to the networks. It is particularly noticeable in the glomerular layer of the olfactory bulb, the first site of olfactory information processing. We provide the first unbiased population-wise multivariate analysis to correlate morphological and physiological parameters of juxtaglomerular cells. We identify seven cell types, including four previously described neuron types, and identify further three distinct classes. The presented correlative analysis of morphological and physiological parameters gives an opportunity to predict morphological classes from physiological measurements or the functional properties of neurons from morphology and opens the way to integrate results from in vivo recordings, advanced morphological tools, and connectomics.

Familiarization: A theory of repetition suppression predicts interference between overlapping cortical representations

Repetition suppression refers to a reduction in the cortical response to a novel stimulus that results from repeated presentation of the stimulus. We demonstrate repetition suppression in a well established computational model of cortical plasticity, according to which the relative strengths of lateral inhibitory interactions are modified by Hebbian learning. We present the model as an extension to the traditional account of repetition suppression offered by sharpening theory, which emphasises the contribution of afferent plasticity, by instead attributing the effect primarily to plasticity of intra-cortical circuitry. In support, repetition suppression is shown to emerge in simulations with plasticity enabled only in intra-cortical connections. We show in simulation how an extended 'inhibitory sharpening theory' can explain the disruption of repetition suppression reported in studies that include an intermediate phase of exposure to additional novel stimuli composed of features similar to those of the original stimulus. The model suggests a re-interpretation of repetition suppression as a manifestation of the process by which an initially distributed representation of a novel object becomes a more localist representation. Thus, inhibitory sharpening may constitute a more general process by which representation emerges from cortical re-organisation.

Inhibitory interneurons and their circuit motifs in the many layers of the barrel cortex

Recent years have seen substantial progress in studying the structural and functional properties of GABAergic interneurons and their roles in the neuronal networks of barrel cortex. Although GABAergic interneurons represent only about 12% of the total number of neocortical neurons, they are extremely diverse with respect to their structural and functional properties. It has become clear that barrel cortex interneurons not only serve the maintenance of an appropriate excitation/inhibition balance but also are directly involved in sensory processing. In this review we present different interneuron types and their axonal projection pattern framework in the context of the laminar and columnar organization of the barrel cortex. The main focus is here on the most prominent interneuron types, i.e. basket cells, chandelier cells, Martinotti cells, bipolar/bitufted cells and neurogliaform cells, but interneurons with more unusual axonal domains will also be mentioned. We describe their developmental origin, their classification with respect to molecular, morphological and intrinsic membrane and synaptic properties. Most importantly, we will highlight the most prominent circuit motifs these interneurons are involved in and in which way they serve feed-forward inhibition, feedback inhibition and disinhibition. Finally, this will be put into context to their functional roles in sensory signal perception and processing in the whisker system and beyond.

NCAM Regulates Inhibition and Excitability in Layer 2/3 Pyramidal Cells of Anterior Cingulate Cortex

The neural cell adhesion molecule (NCAM), has been shown to be an obligate regulator of synaptic stability and pruning during critical periods of cortical maturation. However, the functional consequences of NCAM deletion on the organization of inhibitory circuits in cortex are not known. In vesicular gamma-amino butyric acid (GABA) transporter (VGAT)-channelrhodopsin2 (ChR2)-enhanced yellow fluorescent protein (EYFP) transgenic mice, NCAM is expressed postnatally at perisomatic synaptic puncta of EYFP-labeled parvalbumin, somatostatin and calretinin-positive interneurons, and in the neuropil in the anterior cingulate cortex (ACC). To investigate how NCAM deletion affects the spatial organization of inhibitory inputs to pyramidal cells, we used laser scanning photostimulation in brain slices of VGAT-ChR2-EYFP transgenic mice crossed to either NCAM-null or wild type (WT) mice. Laser scanning photostimulation revealed that NCAM deletion increased the strength of close-in inhibitory connections to layer 2/3 pyramidal cells of the ACC. In addition, in NCAM-null mice, the intrinsic excitability of pyramidal cells increased, whereas the intrinsic excitability of GABAergic interneurons did not change. The increase in inhibitory tone onto pyramidal cells, and the increased pyramidal cell excitability in NCAM-null mice will alter the delicate coordination of excitation and inhibition (E/I coordination) in the ACC, and may be a factor contributing to circuit dysfunction in diseases such as schizophrenia and bipolar disorder, in which NCAM has been implicated.

Morphological Neuron Classification Using Machine Learning

Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction is challenging since it is still unclear how to delineate a neuronal cell class and which are the best features to define them by. The morphological neuron characterization represents a primary source to address anatomical comparisons, morphometric analysis of cells, or brain modeling. The objectives of this paper are (i) to develop and integrate a pipeline that goes from morphological feature extraction to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, linear discriminant analysis provided better classification results in comparison with others. For unsupervised algorithms, the affinity propagation and the Ward algorithms provided slightly better results.

Inhibitory Circuits in Cortical Layer 5

Inhibitory neurons play a fundamental role in cortical computation and behavior. Recent technological advances, such as two photon imaging, targeted in vivo recording, and molecular profiling, have improved our understanding of the function and diversity of cortical interneurons, but for technical reasons most work has been directed towards inhibitory neurons in the superficial cortical layers. Here we review current knowledge specifically on layer 5 (L5) inhibitory microcircuits, which play a critical role in controlling cortical output. We focus on recent work from the well-studied rodent barrel cortex, but also draw on evidence from studies in primary visual cortex and other cortical areas. The diversity of both deep inhibitory neurons and their pyramidal cell targets make this a challenging but essential area of study in cortical computation and sensory processing.

Visible rodent brain-wide networks at single-neuron resolution

There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development.

Synaptic Conductance Estimates of the Connection Between Local Inhibitor Interneurons and Pyramidal Neurons in Layer 2/3 of a Cortical Column

Stimulation of a principal whisker yields sparse action potential (AP) spiking in layer 2/3 (L2/3) pyramidal neurons in a cortical column of rat barrel cortex. The low AP rates in pyramidal neurons could be explained by activation of interneurons in L2/3 providing inhibition onto L2/3 pyramidal neurons. L2/3 interneurons classified as local inhibitors based on their axonal projection in the same column were reported to receive strong excitatory input from spiny neurons in L4, which are also the main source of the excitatory input to L2/3 pyramidal neurons. Here, we investigated the remaining synaptic connection in this intracolumnar microcircuit. We found strong and reliable inhibitory synaptic transmission between intracolumnar L2/3 local-inhibitor-to-L2/3 pyramidal neuron pairs [inhibitory postsynaptic potential (IPSP) amplitude −0.88 ± 0.67 mV]. On average, 6.2 ± 2 synaptic contacts were made by L2/3 local inhibitors onto L2/3 pyramidal neurons at 107 ± 64 µm path distance from the pyramidal neuron soma, thus overlapping with the distribution of synaptic contacts from L4 spiny neurons onto L2/3 pyramidal neurons (67 ± 34 µm). Finally, using compartmental simulations, we determined the synaptic conductance per synaptic contact to be 0.77 ± 0.4 nS. We conclude that the synaptic circuit from L4 to L2/3 can provide efficient shunting inhibition that is temporally and spatially aligned with the excitatory input from L4 to L2/3.

A barrel-related interneuron in layer 4 of rat somatosensory cortex with a high intrabarrel connectivity

Synaptic connections between identified fast-spiking (FS), parvalbumin (PV)-positive interneurons, and excitatory spiny neurons in layer 4 (L4) of the barrel cortex were investigated using patch-clamp recordings and simultaneous biocytin fillings. Three distinct clusters of FS L4 interneurons were identified based on their axonal morphology relative to the barrel column suggesting that these neurons do not constitute a homogeneous interneuron population. One L4 FS interneuron type had an axonal domain strictly confined to a L4 barrel and was therefore named "barrel-confined inhibitory interneuron" (BIn). BIns established reliable inhibitory synaptic connections with L4 spiny neurons at a high connectivity rate of 67%, of which 69% were reciprocal. Unitary IPSPs at these connections had a mean amplitude of 0.9 ± 0.8 mV with little amplitude variation and weak short-term synaptic depression. We found on average 3.7 ± 1.3 putative inhibitory synaptic contacts that were not restricted to perisomatic areas. In conclusion, we characterized a novel type of barrel cortex interneuron in the major thalamo-recipient layer 4 forming dense synaptic networks with L4 spiny neurons. These networks constitute an efficient and powerful inhibitory feedback system, which may serve to rapidly reset the barrel microcircuitry following sensory activation.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Generation of dense statistical connectomes from sparse morphological data

Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and subcellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results.

Flow of cortical activity underlying a tactile decision in mice

Perceptual decisions involve distributed cortical activity. Does information flow sequentially from one cortical area to another, or do networks of interconnected areas contribute at the same time? Here we delineate when and how activity in specific areas drives a whisker-based decision in mice. A short-term memory component temporally separated tactile "sensation" and "action" (licking). Using optogenetic inhibition (spatial resolution, 2 mm; temporal resolution, 100 ms), we surveyed the neocortex for regions driving behavior during specific behavioral epochs. Barrel cortex was critical for sensation. During the short-term memory, unilateral inhibition of anterior lateral motor cortex biased responses to the ipsilateral side. Consistently, barrel cortex showed stimulus-specific activity during sensation, whereas motor cortex showed choice-specific preparatory activity and movement-related activity, consistent with roles in motor planning and movement. These results suggest serial information flow from sensory to motor areas during perceptual decision making.

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Response selectivity is correlated to dendritic structure in parvalbumin-expressing inhibitory neurons in visual cortex

Inhibitory neurons have been shown to perform a variety of functions within brain circuits, including shaping response functions in target cells. Still, how the properties of specific inhibitory neuron classes relate to their local circuits remains unclear. To better understand the distribution and origins of orientation selectivity in inhibitory neurons expressing the calcium binding protein parvalbumin (PV) in the mouse primary visual cortex, we labeled PV(+) neurons with red fluorescent protein (RFP) and targeted them for cell-attached electrophysiological recordings. PV(+) neurons could be broadly tuned or sharply tuned for orientation but tended to be more broadly tuned than unlabeled neurons on average. The dendritic morphology of PV(+) cells, revealed by intracellular labeling, was strongly correlated with tuning: highly tuned PV(+) neurons had shorter dendrites that branched nearer to the soma and had smaller dendritic fields overall, whereas broadly tuned PV(+) neurons had longer dendrites that branched farther from the soma, producing larger dendritic fields. High-speed two-photon calcium imaging of visual responses showed that the orientation preferences of highly tuned PV(+) neurons resembled the preferred orientations of neighboring cells. These results suggest that the diversity of the local neighborhood and the nature of dendritic sampling may both contribute to the response selectivity of PV(+) neurons.

Motor cortex broadly engages excitatory and inhibitory neurons in somatosensory barrel cortex

Anatomical studies have shown that primary somatosensory (S1) and primary motor (M1) cortices are reciprocally connected. The M1 to S1 projection is thought to represent a modulatory signal that conveys motor-related information to S1. Here, we investigated M1 synaptic inputs to S1 by injecting an AAV virus containing channelrhodopsin-2 and a fluorescent tag into M1. Consistent with previous results, we found labeling of M1 axons within S1 that was most robust in the deep layers and in L1. Labeling was sparse in L4 and was concentrated in the interbarrel septa, largely avoiding barrel centers. In S1, we recorded in vitro from regular-spiking excitatory neurons and fast-spiking and somatostatin-expressing inhibitory interneurons. All 3 cell types had a high probability of receiving direct excitatory M1 input. Both excitatory and inhibitory cells within L4 were the least likely to receive such input from M1. Disynaptic inhibition was observed frequently, indicating that M1 recruits substantial inhibition within S1. Additionally, a subpopulation of L6 regular-spiking excitatory neurons received exceptionally strong M1 input. Overall, our results suggest that activation of M1 evokes within S1 a bombardment of excitatory and inhibitory synaptic activity that could contribute in a layer-specific manner to state-dependent changes in S1.

A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis

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.

Morphology and physiology of excitatory neurons in layer 6b of the somatosensory rat barrel cortex

Neocortical lamina 6B (L6B) is a largely unexplored layer with a very heterogeneous cellular composition. To date, only little is known about L6B neurons on a systematic and quantitative basis. We investigated the morphological and electrophysiological properties of excitatory L6B neurons in the rat somatosensory barrel cortex using whole-cell patch-clamp recordings and simultaneous biocytin fillings. Subsequent histological processing and computer-assisted 3D reconstructions provided the basis for a classification of excitatory L6B neurons according to their structural and functional characteristics. Three distinct clusters of excitatory L6B neurons were identified: (C1) pyramidal neurons with an apical dendrite pointing towards the pial surface, (C2) neurons with a prominent, “apical”-like dendrite not oriented towards the pia, and (C3) multipolar spiny neurons without any preferential dendritic orientation. The second group could be further subdivided into three categories termed inverted, “tangentially” oriented and “horizontally” oriented neurons. Furthermore, based on the axonal domain two subcategories of L6B pyramidal cells were identified that had either a more barrel-column confined or an extended axonal field. The classification of excitatory L6B neurons provided here may serve as a basis for future studies on the structure, function, and synaptic connectivity of L6B neurons.

Diversity of GABAergic interneurons in layer VIa and VIb of mouse barrel cortex

Neocortical layer VI modulates the thalamocortical transfer of information and has a significant impact on sensory processing. This function implicates local γ-aminobutyric acidergic (GABAergic) interneurons that have only been partly described at the present time. Here, we characterized 85 layer VI GABAergic interneurons in acute slices of mouse somatosensory barrel cortex, using whole-cell current-clamp recordings, single-cell reverse transcription-polymerase chain reaction, and biocytin labeling followed by Neurolucida reconstructions. Unsupervised clustering based on electrophysiological molecular and morphological properties disclosed 4 types of interneurons. The 2 major classes were fast-spiking cells transcribing parvalbumin (PV) (51%) and adapting interneurons transcribing somatostatin (SOM) (26%). The third population (18%) transcribed neuropeptide Y (NPY) and appeared very similar to neurogliaform cells. The last class (5%) was constituted by well-segregated GABAergic interneurons transcribing vasoactive intestinal peptide (VIP). Using transgenic mice expressing GFP under the control of the glutamic acid decarboxylase 67k (GAD67) promoter, we investigated the densities of GABAergic cells immunolabeled against PV, SOM, VIP, and NPY through the depth of layer VI. This analysis revealed that PV and NPY translating interneurons concentrate in the upper and lower parts of layer VI, respectively. This study provides an extensive characterization of the properties of layer VI interneurons.

Control of interneuron dendritic growth through NRG1/erbB4-mediated kalirin-7 disinhibition

Neuregulin 1 (NRG1) is a secreted trophic factor that activates the postsynaptic erbB4 receptor tyrosine kinase. Both NRG1 and erbB4 have been repeatedly associated with schizophrenia, but their downstream targets are not well characterized. ErbB4 is highly abundant in interneurons, and NRG1-mediated erbB4 activation has been shown to modulate interneuron function, but the role for NRG1-erbB4 signaling in regulating interneuron dendritic growth is not well understood. Here we show that NRG1/erbB4 promote the growth of dendrites in mature interneurons through kalirin, a major dendritic Rac1-GEF. Recent studies have shown associations of the KALRN gene with schizophrenia. Our data point to an essential role of phosphorylation in kalirin-7's C terminus as the critical site for these effects. As reduced interneuron dendrite length occurs in schizophrenia, understanding how NRG1-erbB4 signaling modulates interneuron dendritic morphogenesis might shed light on disease-related alterations in cortical circuits.

Comparison between supervised and unsupervised classifications of neuronal cell types: a case study

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011

Pharmacology of currents underlying the different firing patterns of spinal sensory neurons and interneurons identified in vivo using multivariate analysis

The operation of neuronal networks depends on the firing patterns of the network's neurons. When sustained current is injected, some neurons in the central nervous system fire a single action potential and others fire repetitively. For example, in Xenopus laevis tadpoles, primary-sensory Rohon-Beard (RB) neurons fired a single action potential in response to 300-ms rheobase current injections, whereas dorsolateral (DL) interneurons fired repetitively at 10-20 Hz. To investigate the basis for these differences in vivo, we examined drug-induced changes in the firing patterns of Xenopus spinal neurons using whole cell current-clamp recordings. Neuron types were initially separated through cluster analysis, and we compared results produced using different clustering algorithms. We used these results to develop a predictive function to classify subsequently recorded neurons. The potassium channel blocker tetraethylammonium (TEA) converted single-firing RB neurons to low-frequency repetitive firing but reduced the firing frequency of repetitive-firing DL interneurons. Firing frequency in DL interneurons was also reduced by the potassium channel blockers 4-aminopyridine (4-AP), catechol, and margatoxin; 4-AP had the greatest effect. The calcium channel blockers amiloride and nimodipine had few effects on firing in either neuron type but reduced action potential duration in DL interneurons. Muscarine, which blocks M-currents, did not affect RB neurons but reduced firing frequency in DL interneurons. These results suggest that potassium currents may control neuron firing patterns: a TEA-sensitive current prevents repetitive firing in RB neurons, whereas a 4-AP-sensitive current underlies repetitive firing in DL interneurons. The cluster and discriminant analysis described could help to classify neurons in other systems.

Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions

Recent theoretical and experimental work indicates that neurons tune themselves to maintain target levels of excitation by modulating ion channel expression and synaptic strengths. As a result, functionally equivalent circuits can produce similar activity despite disparate underlying network and cellular properties. To experimentally test the extent to which synaptic and intrinsic conductances can produce target activity in the presence of variability in neuronal intrinsic properties, we used the dynamic clamp to create hybrid two-cell circuits built from four types of stomatogastric neurons coupled to the same model Morris-Lecar neuron by reciprocal inhibition. We measured six intrinsic properties (input resistance, minimum membrane potential, firing rate in response to +1 nA of injected current, slope of the frequency-current curve, spike height, and spike voltage threshold) of dorsal gastric, gastric mill, lateral pyloric, and pyloric dilator neurons from male crabs of the species Cancer borealis. The intrinsic properties varied twofold to sevenfold in each cell type. We coupled each biological neuron to the Morris-Lecar model with seven different values of inhibitory synaptic conductance and also used the dynamic clamp to add seven different values of an artificial h-conductance, thus creating 49 different circuits for each biological neuron. Despite the variability in intrinsic excitability, networks formed from each neuron produced similar circuit performance at some values of synaptic and h-conductances. This work experimentally confirms results from previous modeling studies; tuning synaptic and intrinsic conductances can yield similar circuit outputs from neurons with variable intrinsic excitability.

Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts.

Modeling the emergence of whisker direction maps in rat barrel cortex

Based on measuring responses to rat whiskers as they are mechanically stimulated, one recent study suggests that barrel-related areas in layer 2/3 rat primary somatosensory cortex (S1) contain a pinwheel map of whisker motion directions. Because this map is reminiscent of topographic organization for visual direction in primary visual cortex (V1) of higher mammals, we asked whether the S1 pinwheels could be explained by an input-driven developmental process as is often suggested for V1. We developed a computational model to capture how whisker stimuli are conveyed to supragranular S1, and simulate lateral cortical interactions using an established self-organizing algorithm. Inputs to the model each represent the deflection of a subset of 25 whiskers as they are contacted by a moving stimulus object. The subset of deflected whiskers corresponds with the shape of the stimulus, and the deflection direction corresponds with the movement direction of the stimulus. If these two features of the inputs are correlated during the training of the model, a somatotopically aligned map of direction emerges for each whisker in S1. Predictions of the model that are immediately testable include (1) that somatotopic pinwheel maps of whisker direction exist in adult layer 2/3 barrel cortex for every large whisker on the rat's face, even peripheral whiskers; and (2) in the adult, neurons with similar directional tuning are interconnected by a network of horizontal connections, spanning distances of many whisker representations. We also propose specific experiments for testing the predictions of the model by manipulating patterns of whisker inputs experienced during early development. The results suggest that similar intracortical mechanisms guide the development of primate V1 and rat S1.

Quantitative morphometry of electrophysiologically identified CA3b interneurons reveals robust local geometry and distinct cell classes

The morphological and electrophysiological diversity of inhibitory cells in hippocampal area CA3 may underlie specific computational roles and is not yet fully elucidated. In particular, interneurons with somata in strata radiatum (R) and lacunosum-moleculare (L-M) receive converging stimulation from the dentate gyrus and entorhinal cortex as well as within CA3. Although these cells express different forms of synaptic plasticity, their axonal trees and connectivity are still largely unknown. We investigated the branching and spatial patterns, plus the membrane and synaptic properties, of rat CA3b R and L-M interneurons digitally reconstructed after intracellular labeling. We found considerable variability within but no difference between the two layers, and no correlation between morphological and biophysical properties. Nevertheless, two cell types were identified based on the number of dendritic bifurcations, with significantly different anatomical and electrophysiological features. Axons generally branched an order of magnitude more than dendrites. However, interneurons on both sides of the R/L-M boundary revealed surprisingly modular axodendritic arborizations with consistently uniform local branch geometry. Both axons and dendrites followed a lamellar organization, and axons displayed a spatial preference toward the fissure. Moreover, only a small fraction of the axonal arbor extended to the outer portion of the invaded volume, and tended to return toward the proximal region. In contrast, dendritic trees demonstrated more limited but isotropic volume occupancy. These results suggest a role of predominantly local feedforward and lateral inhibitory control for both R and L-M interneurons. Such a role may be essential to balance the extensive recurrent excitation of area CA3 underlying hippocampal autoassociative memory function.

Distinct firing patterns of identified basket and dendrite-targeting interneurons in the prefrontal cortex during hippocampal theta and local spindle oscillations

The medial prefrontal cortex is involved in working memory and executive control. However, the collective spatiotemporal organization of the cellular network has not been possible to explain during different brain states. We show that pyramidal cells in the prelimbic cortex fire synchronized to hippocampal theta and local spindle oscillations in anesthetized rats. To identify which types of interneurons contribute to the synchronized activity, we recorded and juxtacellularly labeled parvalbumin- and calbindin-expressing (PV+/CB+) basket cells and CB-expressing, PV-negative (CB+/PV-) dendrite-targeting interneurons during both network oscillations. All CB+/PV- dendrite-targeting cells strongly decreased their firing rate during hippocampal theta oscillations. Most PV+/CB+ basket cells fired at the peak of dorsal CA1 theta cycles, similar to prefrontal pyramidal cells. We show that pyramidal cells in the ventral hippocampus also fire around the peak of dorsal CA1 theta cycles, in contrast to previously reported dorsal hippocampal pyramidal cells. Therefore, prefrontal neurons might be driven by monosynaptic connections from the ventral hippocampus during theta oscillations. During prefrontal spindle oscillations, the majority of pyramidal cells and PV+/CB+ basket cells fired preferentially at the trough and early ascending phase, but CB+/PV- dendrite-targeting cells fired uniformly at all phases. We conclude that PV+/CB+ basket cells contribute to rhythmic responses of prefrontal pyramidal cells in relation to hippocampal and thalamic inputs and CB+/PV- dendrite-targeting cells modulate the excitability of dendrites and spines regardless of these field rhythms. Distinct classes of GABAergic interneuron in the prefrontal cortex contribute differentially to the synchronization of pyramidal cells during network oscillations.

Efficient recruitment of layer 2/3 interneurons by layer 4 input in single columns of rat somatosensory cortex

Interneurons in layers 2/3 are excited by pyramidal cells within the same layer (Reyes et al., 1998; Gupta et al., 2000), but little is known about translaminar innervation of these interneurons by spiny neurons in the main cortical input layer 4 (L4). Here, we investigated (1) how efficiently L4 spiny neurons excite L2/3 interneurons via monosynaptic connections, (2) whether glutamate release from axon terminals of L4 spiny neurons depends on the identity of the postsynaptic interneuron, and (3) how L4-to-L2/3 interneuron connections compare with L4-to-L2/3 pyramidal neuron connections. We recorded from pairs of L4 spiny neurons and L2/3 interneurons in acute slices of rat barrel cortex of postnatal day 20 (P20) to P29 rats. The L4-to-L2/3 interneuron connections had an average unitary EPSP of 1.2 +/- 1.1 mV. We found an average of 2.3 +/- 0.8 contacts per connection, and the L4-to-L2/3 interneuron innervation domains were mostly column restricted. Unitary EPSP amplitudes and paired-pulse ratios in the L4-to-L2/3 interneuron connections depended on the "group" of the postsynaptic interneuron. Averaged over all L4-to-L2/3 interneuron connections, unitary EPSP amplitudes were 1.8-fold higher than in the translaminar L4-to-L2/3 pyramidal cell connections. Our results suggest that L4 spiny neurons may more efficiently recruit L2/3 interneurons than L2/3 pyramidal neurons, and that glutamate release from translaminar boutons of L4 spiny neuron axons is target cell specific.