High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons

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.

Dynamics of neocortical networks: connectivity beyond the canonical microcircuit

The neocortical network consists of two types of excitatory neurons and a variety of GABAergic inhibitory interneurons, which are organized in distinct microcircuits providing feedforward, feedback, lateral inhibition, and disinhibition. This network is activated by layer- and cell-type specific inputs from first and higher order thalamic nuclei, other subcortical regions, and by cortico-cortical projections. Parallel and serial information processing occurs simultaneously in different intracortical subnetworks and is influenced by neuromodulatory inputs arising from the basal forebrain (cholinergic), raphe nuclei (serotonergic), locus coeruleus (noradrenergic), and ventral tegmentum (dopaminergic). Neocortical neurons differ in their intrinsic firing pattern, in their local and global synaptic connectivity, and in the dynamics of their synaptic interactions. During repetitive stimulation, synaptic connections between distinct neuronal cell types show short-term facilitation or depression, thereby activating or inactivating intracortical microcircuits. Specific networks are capable to generate local and global activity patterns (e.g., synchronized oscillations), which contribute to higher cognitive function and behavior. This review article aims to give a brief overview on our current understanding of the structure and function of the neocortical network.

Layer- and cell-type-specific differences in neural activity in mouse barrel cortex during a whisker detection task

To address the question which neocortical layers and cell types are important for the perception of a sensory stimulus, we performed multielectrode recordings in the barrel cortex of head-fixed mice performing a single-whisker go/no-go detection task with vibrotactile stimuli of differing intensities. We found that behavioral detection probability decreased gradually over the course of each session, which was well explained by a signal detection theory-based model that posits stable psychometric sensitivity and a variable decision criterion updated after each reinforcement, reflecting decreasing motivation. Analysis of multiunit activity demonstrated highest neurometric sensitivity in layer 4, which was achieved within only 30 ms after stimulus onset. At the level of single neurons, we observed substantial heterogeneity of neurometric sensitivity within and across layers, ranging from nonresponsiveness to approaching or even exceeding psychometric sensitivity. In all cortical layers, putative inhibitory interneurons on average proffered higher neurometric sensitivity than putative excitatory neurons. In infragranular layers, neurons increasing firing rate in response to stimulation featured higher sensitivities than neurons decreasing firing rate. Offline machine-learning-based analysis of videos of behavioral sessions showed that mice performed better when not moving, which at the neuronal level, was reflected by increased stimulus-evoked firing rates.

Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information

Neurons can carry information with both the synchrony and rate of their spikes. However, it is unknown whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, or vice versa. Here, we address this question using patterned optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. We used optical stimulation in layer 2/3 to encode a 1-bit signal using either the synchrony or rate of activity. We then examined the mutual information between this signal and the interneuron responses. We found that for a synchrony encoding, FS interneurons carried more information in the first five milliseconds, while both interneuron subtypes carried more information than excitatory neurons in later responses. For a rate encoding, we found that RS interneurons carried more information after several milliseconds. These data demonstrate that distinct interneuron subtypes in the neocortex have distinct sensitivities to synchrony versus rate codes.

In order to address whether distinct subtypes of neurons are more sensitive to information carried by synchrony versus rate, Prince et al. used optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. They demonstrated that FS and RS interneurons had differential sensitivity to changes in synchrony and rate, which advances our understanding of neural processing in the neocortex.

Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo

How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are “time-warped” (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.

Distinct mechanisms of face representation by enhancive and suppressive neurons of the inferior temporal cortex

Face-selective neurons in the inferior temporal (IT) cortex respond to faces by either increasing (ENH) or decreasing (SUP) their spiking activities compared with their baseline. Although nearly half of IT face neurons are selectively suppressed by face stimulation, their role in face representation is not clear. To address this issue, we recorded the spiking activities and local field potential (LFP) from IT cortex of three monkeys while they viewed a large set of visual stimuli. LFP high-gamma (HG-LFP) power indicated the presence of both ENH and SUP face-selective neural clusters in IT cortex. The magnitude of HG-LFP power of the recording sites was correlated with the magnitude of change in the evoked spiking activities of its constituent neurons for both ENH and SUP face clusters. Spatial distribution of the ENH and SUP face clusters suggests the presence of a complex and heterogeneous face hypercluster organization in IT cortex. Importantly, ENH neurons conveyed more face category and SUP neurons conveyed more face identity information at both the single-unit and neuronal population levels. Onset and peak of suppressive single-unit, neuronal population, and HG-LFP power activities lagged those of the ENH ones. These results demonstrate that IT neuronal code for face representation is optimized by increasing sparseness through selective suppression of a subset of face neurons. We suggest that IT cortex contains spatial clusters of both ENH and SUP face neurons with distinct specialized functional role in face representation.NEW & NOTEWORTHY Electrophysiological and imaging studies have suggested that face information is encoded by a network of clusters of enhancive face-selective neurons in the visual cortex of man and monkey. We show that nearly half of face-selective neurons are suppressed by face stimulation. The suppressive neurons form spatial clusters and convey more face identity information than the enhancive face neurons. Our results suggest the presence of two neuronal subsystems for coarse and fine face information processing.

Assessing the Impact of Single-Cell Stimulation on Local Networks in Rat Barrel Cortex-A Feasibility Study

In contrast to the long-standing notion that the role of individual neurons in population activity is vanishingly small, recent studies have shown that electrical activation of only a single cortical neuron can have measurable effects on global brain state, movement, and perception. Although highly important for understanding how neuronal activity in cortex is orchestrated, the cellular and network mechanisms underlying this phenomenon are unresolved. Here, we first briefly review the current state of knowledge regarding the phenomenon of single-cell induced network modulation and discuss possible underpinnings. Secondly, we show proof of principle for an experimental approach to elucidate the mechanisms of single-cell induced changes in cortical activity. The setup allows simultaneous recordings of the spiking activity of multiple neurons across all layers of the cortex using a multi-electrode array, while manipulating the activity of one individual neuron in close proximity to the array. We demonstrate that single cells can be recorded and stimulated reliably for hundreds of trials, conferring high statistical power even for expectedly small effects of single-neuron spiking on network activity. Preliminary results suggest that single-cell stimulation on average decreases the firing rate of local network units. We expect that characterization of the spatiotemporal spread of single-cell evoked activity across layers and columns will yield novel insights into intracortical processing.

Neural coding: A single neuron's perspective

What any sensory neuron knows about the world is one of the cardinal questions in Neuroscience. Information from the sensory periphery travels across synaptically coupled neurons as each neuron encodes information by varying the rate and timing of its action potentials (spikes). Spatiotemporally correlated changes in this spiking regimen across neuronal populations are the neural basis of sensory representations. In the somatosensory cortex, however, spiking of individual (or pairs of) cortical neurons is only minimally informative about the world. Recent studies showed that one solution neurons implement to counteract this information loss is adapting their rate of information transfer to the ongoing synaptic activity by changing the membrane potential at which spike is generated. Here we first introduce the principles of information flow from the sensory periphery to the primary sensory cortex in a model sensory (whisker) system, and subsequently discuss how the adaptive spike threshold gates the intracellular information transfer from the somatic post-synaptic potential to action potentials, controlling the information content of communication across somatosensory cortical neurons.

Optogenetic Modulation of a Minor Fraction of Parvalbumin-Positive Interneurons Specifically Affects Spatiotemporal Dynamics of Spontaneous and Sensory-Evoked Activity in Mouse Somatosensory Cortex in Vivo

Parvalbumin (PV) positive interneurons exert strong effects on the neocortical excitatory network, but it remains unclear how they impact the spatiotemporal dynamics of sensory processing in the somatosensory cortex. Here, we characterized the effects of optogenetic inhibition and activation of PV interneurons on spontaneous and sensory-evoked activity in mouse barrel cortex in vivo. Inhibiting PV interneurons led to a broad-spectrum power increase both in spontaneous and sensory-evoked activity. Whisker-evoked responses were significantly increased within 20 ms after stimulus onset during inhibition of PV interneurons, demonstrating high temporal precision of PV-shaped inhibition. Multiunit activity was strongly enhanced in neighboring cortical columns, but not at the site of transduction, supporting a central and highly specific role of PV interneurons in lateral inhibition. Inversely, activating PV interneurons drastically decreased spontaneous and whisker-evoked activity in the principal column and exerted strong lateral inhibition. Histological assessment of transduced cells combined with quantitative modeling of light distribution and spike sorting revealed that only a minor fraction (~10%) of the local PV population comprising no more than a few hundred neurons is optogenetically modulated, mediating the observed prominent and widespread effects on neocortical processing.

Single-Cell Stimulation in Barrel Cortex Influences Psychophysical Detection Performance

A single whisker stimulus elicits action potentials in a sparse subset of neurons in somatosensory cortex. The precise contribution of these neurons to the animal's perception of a whisker stimulus is unknown. Here we show that single-cell stimulation in rat barrel cortex of both sexes influences the psychophysical detection of a near-threshold whisker stimulus in a cell type-dependent manner, without affecting false alarm rate. Counterintuitively, stimulation of single fast-spiking putative inhibitory neurons increased detection performance. Single-cell stimulation of putative excitatory neurons failed to change detection performance, except for a small subset of deep-layer neurons that were highly sensitive to whisker stimulation and that had an unexpectedly strong impact on detection performance. These findings indicate that the perceptual impact of excitatory barrel cortical neurons relates to their firing response to whisker stimulation and that strong activity in a single highly sensitive neuron in barrel cortex can already enhance sensory detection. Our data suggest that sensory detection is based on a decoding mechanism that lends a disproportionally large weight to interneurons and to deep-layer neurons showing a strong response to sensory stimulation.SIGNIFICANCE STATEMENT Rat whisker somatosensory cortex contains a variety of neuronal cell types with distinct anatomical and physiological characteristics. How each of these different cell types contribute to the animal's perception of whisker stimuli is unknown. We explored this question by using a powerful electrophysiological stimulation technique that allowed us to target and stimulate single neurons with different sensory response types in whisker cortex. In awake, behaving animals, trained to detect whisker stimulation, only costimulation of single fast-spiking inhibitory neurons or single deep-layer excitatory neurons with strong responses to whisker stimulation enhanced detection performance. Our data demonstrate that single cortical neurons can have measurable impact on the detection of sensory stimuli and suggest a decoding mechanism based on select cell types.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

Sex differences in endogenous cortical network activity: spontaneously recurring Up/Down states

BACKGROUND

Several molecular and cellular processes in the vertebrate brain exhibit differences between males and females, leading to sexual dimorphism in the formation of neural circuits and brain organization. While studies on large-scale brain networks provide ample evidence for both structural and functional sex differences, smaller-scale local networks have remained largely unexplored. In the current study, we investigate sexual dimorphism in cortical dynamics by means of spontaneous Up/Down states, a type of network activity that is exhibited during slow-wave sleep, quiet wakefulness, and anesthesia and is thought to represent the default activity of the cortex.

METHODS

Up state activity was monitored by local field potential recordings in coronal brain slices of male and female mice across three ages with distinct secretion profiles of sex hormones: (i) pre-puberty (17-21 days old), (ii) 3-9 adult (months old), and (iii) old (19-24 months old).

RESULTS

Female mice of all ages exhibited longer and more frequent Up states compared to aged-matched male mice. Power spectrum analysis revealed sex differences in the relative power of Up state events, with female mice showing reduced power in the delta range (1-4 Hz) and increased power in the theta range (4-8 Hz) compared to male mice. No sex differences were found in the characteristics of Up state peak voltage and latency.

CONCLUSIONS

The present study revealed for the first time sex differences in intracortical network activity, using an ex vivo paradigm of spontaneously occurring Up/Down states. We report significant sex differences in Up state properties that are already present in pre-puberty animals and are maintained through adulthood and old age.

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.

Dynamics of Population Activity in Rat Sensory Cortex: Network Correlations Predict Anatomical Arrangement and Information Content

To study the spatiotemporal dynamics of neural activity in a cortical population, we implanted a 10 × 10 microelectrode array in the vibrissal cortex of urethane-anesthetized rats. We recorded spontaneous neuronal activity as well as activity evoked in response to sustained and brief sensory stimulation. To quantify the temporal dynamics of activity, we computed the probability distribution function (PDF) of spiking on one electrode given the observation of a spike on another. The spike-triggered PDFs quantified the strength, temporal delay, and temporal precision of correlated activity across electrodes. Nearby cells showed higher levels of correlation at short delays, whereas distant cells showed lower levels of correlation, which tended to occur at longer delays. We found that functional space built based on the strength of pairwise correlations predicted the anatomical arrangement of electrodes. Moreover, the correlation profile of electrode pairs during spontaneous activity predicted the "signal" and "noise" correlations during sensory stimulation. Finally, mutual information analyses revealed that neurons with stronger correlations to the network during spontaneous activity, conveyed higher information about the sensory stimuli in their evoked response. Given the 400-μm-distance between adjacent electrodes, our functional quantifications unravel the spatiotemporal dynamics of activity among nearby and distant cortical columns.

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.

Propagation of spontaneous slow-wave activity across columns and layers of the adult rat barrel cortex in vivo

During slow-wave sleep, neocortical networks exhibit self-organized activity switching between periods of concurrent spiking (up-states) and periods of network silence (down-states), a phenomenon also occurring under the effects of different anesthetics and in in vitro brain slice preparations. Although this type of ongoing activity has been implicated into important functions such as memory consolidation and learning, the manner in which it propagates across different cortical modules (i.e., columns and layers) has not been fully characterized. In the present study, we investigated this issue by measuring spontaneous activity at large scale in the adult rat barrel cortex under urethane anesthesia by means of voltage-sensitive dye imaging and 128-channel probe recordings. Up to 74 neurons located in all layers of up to four functionally identified barrel-related columns were recorded simultaneously. The spontaneous activity propagated isotropically across the cortical surface with a median speed of ~35 µm/ms. A concomitant radial spread of activation was present from deep to superficial cortical layers. Thus, spontaneous activity occurred rather globally in the barrel cortex, with ≥50 % of the up-states presenting spikes in ≥3 columns and layers. Temporally precise spike sequences, which occurred repeatedly (although sporadically) within the up-states, were typically led by putative excitatory neurons in the infragranular cortical layers. In summary, our data provide for the first time an overall view of the spontaneous slow-wave activity within the barrel cortex circuit, characterizing its propagation across columns and layers at high spatio-temporal resolution.