Spatial profile and differential recruitment of GABAB modulate oscillatory activity in auditory cortex

Pallidal GABA B receptors: involvement in cortex beta dynamics and thalamic reticular nucleus activity

The external globus pallidus (GP) firing rate synchronizes the basal ganglia-thalamus-cortex network controlling GABAergic output to different nuclei. In this context, two findings are significant: the activity and GABAergic transmission of the GP modulated by GABA B receptors and the presence of the GP-thalamic reticular nucleus (RTn) pathway, the functionality of which is unknown. The functional participation of GABA B receptors through this network in cortical dynamics is feasible because the RTn controls transmission between the thalamus and cortex. To analyze this hypothesis, we used single-unit recordings of RTn neurons and electroencephalograms of the motor cortex (MCx) before and after GP injection of the GABA B agonist baclofen and the antagonist saclofen in anesthetized rats. We found that GABA B agonists increase the spiking rate of the RTn and that this response decreases the spectral density of beta frequency bands in the MCx. Additionally, injections of GABA B antagonists decreased the firing activity of the RTn and reversed the effects in the power spectra of beta frequency bands in the MCx. Our results proved that the GP modulates cortical oscillation dynamics through the GP-RTn network via tonic modulation of RTn activity.

Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability

Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.

Self-organization of in vitro neuronal assemblies drives to complex network topology

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

Gamma Oscillations in the Basolateral Amygdala: Localization, Microcircuitry, and Behavioral Correlates

The lateral (LA) and basolateral (BL) nuclei of the amygdala regulate emotional behaviors. Despite their dissimilar extrinsic connectivity, they are often combined, perhaps because their cellular composition is similar to that of the cerebral cortex, including excitatory principal cells reciprocally connected with fast-spiking interneurons (FSIs). In the cortex, this microcircuitry produces gamma oscillations that support information processing and behavior. We tested whether this was similarly the case in the rat (males) LA and BL using extracellular recordings, biophysical modeling, and behavioral conditioning. During periods of environmental assessment, both nuclei exhibited gamma oscillations that stopped upon initiation of active behaviors. Yet, BL exhibited more robust spontaneous gamma oscillations than LA. The greater propensity of BL to generate gamma resulted from several microcircuit differences, especially the proportion of FSIs and their interconnections with principal cells. Furthermore, gamma in BL but not LA regulated the efficacy of excitatory synaptic transmission between connected neurons. Together, these results suggest fundamental differences in how LA and BL operate. Most likely, gamma in LA is externally driven, whereas in BL it can also arise spontaneously to support ruminative processing and the evaluation of complex situations.SIGNIFICANCE STATEMENT The basolateral amygdala (BLA) participates in the production and regulation of emotional behaviors. It is thought to perform this using feedforward circuits that enhance stimuli that gain emotional significance and directs them to valence-appropriate downstream effectors. This perspective overlooks the fact that its microcircuitry is recurrent and potentially capable of generating oscillations in the gamma band (50-80 Hz), which synchronize spiking activity and modulate communication between neurons. This study found that BLA gamma supports both of these processes, is associated with periods of action selection and environmental assessment regardless of valence, and differs between BLA subnuclei in a manner consistent with their heretofore unknown microcircuit differences. Thus, it provides new mechanisms for BLA to support emotional behaviors.

Network dynamics underlying OFF responses in the auditory cortex

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared them to linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organization of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.

Neocortical Layer 1: An Elegant Solution to Top-Down and Bottom-Up Integration

Many of our daily activities, such as riding a bike to work or reading a book in a noisy cafe, and highly skilled activities, such as a professional playing a tennis match or a violin concerto, depend upon the ability of the brain to quickly make moment-to-moment adjustments to our behavior in response to the results of our actions. Particularly, they depend upon the ability of the neocortex to integrate the information provided by the sensory organs (bottom-up information) with internally generated signals such as expectations or attentional signals (top-down information). This integration occurs in pyramidal cells (PCs) and their long apical dendrite, which branches extensively into a dendritic tuft in layer 1 (L1). The outermost layer of the neocortex, L1 is highly conserved across cortical areas and species. Importantly, L1 is the predominant input layer for top-down information, relayed by a rich, dense mesh of long-range projections that provide signals to the tuft branches of the PCs. Here, we discuss recent progress in our understanding of the composition of L1 and review evidence that L1 processing contributes to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning.

Functional Logic of Layer 2/3 Inhibitory Connectivity in the Ferret Visual Cortex

Understanding how cortical inhibition shapes circuit function requires identifying the connectivity rules relating the response properties of inhibitory interneurons and their postsynaptic targets. Here we explore the orientation tuning of layer 2/3 inhibitory inputs in the ferret visual cortex using a combination of in vivo axon imaging, functional input mapping, and physiology. Inhibitory boutons exhibit robust orientation-tuned responses with preferences that can differ significantly from the cortical column in which they reside. Inhibitory input fields measured with patterned optogenetic stimulation and intracellular recordings revealed that these inputs originate from a wide range of orientation domains, inconsistent with a model of co-tuned inhibition and excitation. Intracellular synaptic conductance measurements confirm that individual neurons can depart from a co-tuned regime. Our results argue against a simple rule for the arrangement of inhibitory inputs supplied by layer 2/3 circuits and suggest that heterogeneity in presynaptic inhibitory networks contributes to neural response properties.

Neuronal Avalanches in Input and Associative Layers of Auditory Cortex

The primary auditory cortex processes acoustic sequences for the perception of behaviorally meaningful sounds such as speech. Sound information arrives at its input layer four from where activity propagates to associative layer 2/3. It is currently not known whether there is a characteristic organization of neuronal population activity across layers and sound levels during sound processing. Here, we identify neuronal avalanches, which in theory and experiments have been shown to maximize dynamic range and optimize information transfer within and across networks, in primary auditory cortex. We used in vivo 2-photon imaging of pyramidal neurons in cortical layers L4 and L2/3 of mouse A1 to characterize the populations of neurons that were active spontaneously, i.e., in the absence of a sound stimulus, and those recruited by single-frequency tonal stimuli at different sound levels. Single-frequency sounds recruited neurons of widely ranging frequency selectivity in both layers. We defined neuronal ensembles as neurons being active within or during successive temporal windows at the temporal resolution of our imaging. For both layers, neuronal ensembles were highly variable in size during spontaneous activity as well as during sound presentation. Ensemble sizes distributed according to power laws, the hallmark of neuronal avalanches, and were similar across sound levels. Avalanches activated by sound were composed of neurons with diverse tuning preference, yet with selectivity independent of avalanche size. Our results suggest that optimization principles identified for avalanches guide population activity in L4 and L2/3 of auditory cortex during and in-between stimulus processing.

Preserving Inhibition during Developmental Hearing Loss Rescues Auditory Learning and Perception

Transient periods of childhood hearing loss can induce deficits in aural communication that persist long after auditory thresholds have returned to normal, reflecting long-lasting impairments to the auditory CNS. Here, we asked whether these behavioral deficits could be reversed by treating one of the central impairments: reduction of inhibitory strength. Male and female gerbils received bilateral earplugs to induce a mild, reversible hearing loss during the critical period of auditory cortex development. After earplug removal and the return of normal auditory thresholds, we trained and tested animals on an amplitude modulation detection task. Transient developmental hearing loss induced both learning and perceptual deficits, which were entirely corrected by treatment with a selective GABA reuptake inhibitor (SGRI). To explore the mechanistic basis for these behavioral findings, we recorded the amplitudes of GABA_A and GABA_B receptor-mediated IPSPs in auditory cortical and thalamic brain slices. In hearing loss-reared animals, cortical IPSP amplitudes were significantly reduced within a few days of hearing loss onset, and this reduction persisted into adulthood. SGRI treatment during the critical period prevented the hearing loss-induced reduction of IPSP amplitudes; but when administered after the critical period, it only restored GABA_B receptor-mediated IPSP amplitudes. These effects were driven, in part, by the ability of SGRI to upregulate α1 subunit-dependent GABA_A responses. Similarly, SGRI prevented the hearing loss-induced reduction of GABA_A and GABA_B IPSPs in the ventral nucleus of the medial geniculate body. Thus, by maintaining, or subsequently rescuing, GABAergic transmission in the central auditory thalamocortical pathway, some perceptual and cognitive deficits induced by developmental hearing loss can be prevented.SIGNIFICANCE STATEMENT Even a temporary period of childhood hearing loss can induce communication deficits that persist long after auditory thresholds return to normal. These deficits may arise from long-lasting central impairments, including the loss of synaptic inhibition. Here, we asked whether hearing loss-induced behavioral deficits could be reversed by reinstating normal inhibitory strength. Gerbils reared with transient hearing loss displayed both learning and perceptual deficits. However, when animals were treated with a selective GABA reuptake inhibitor during or after hearing loss, behavioral deficits were entirely corrected. This behavioral recovery was correlated with the return of normal thalamic and cortical inhibitory function. Thus, some perceptual and cognitive deficits induced by developmental hearing loss were prevented with a treatment that rescues a central synaptic property.

A Critical Role of Inhibition in Temporal Processing Maturation in the Primary Auditory Cortex

Faithful representation of sound envelopes in primary auditory cortex (A1) is vital for temporal processing and perception of natural sounds. However, the emergence of cortical temporal processing mechanisms during development remains poorly understood. Although cortical inhibition has been proposed to play an important role in this process, direct in-vivo evidence has been lacking. Using loose-patch recordings in rat A1 immediately after hearing onset, we found that stimulus-following ability in fast-spiking neurons was significantly better than in regular-spiking (RS) neurons. In-vivo whole-cell recordings of RS neurons revealed that inhibition in the developing A1 demonstrated much weaker adaptation to repetitive stimuli than in adult A1. Furthermore, inhibitory synaptic inputs were of longer duration than observed in vitro and in adults. Early in development, overlap of the prolonged inhibition evoked by 2 closely following stimuli disrupted the classical temporal sequence between excitation and inhibition, resulting in slower following capacity. During maturation, inhibitory duration gradually shortened accompanied by an improving temporal following ability of RS neurons. Both inhibitory duration and stimulus-following ability demonstrated exposure-based plasticity. These results demonstrate the role of inhibition in setting the pace for experience-dependent maturation of temporal processing in the auditory cortex.

Correlated states in balanced neuronal networks

Understanding the magnitude and structure of interneuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations, defining the "asynchronous state." Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent network produces much larger correlations. We provide an in-depth analysis of the resulting "correlated state" in balanced networks and show that, unlike the asynchronous state, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

Gamma Oscillations in the Basolateral Amygdala: Biophysical Mechanisms and Computational Consequences

The basolateral nucleus of the amygdala (BL) is thought to support numerous emotional behaviors through specific microcircuits. These are often thought to be comprised of feedforward networks of principal cells (PNs) and interneurons. Neither well-understood nor often considered are recurrent and feedback connections, which likely engender oscillatory dynamics within BL. Indeed, oscillations in the gamma frequency range (40 − 100 Hz) are known to occur in the BL, and yet their origin and effect on local circuits remains unknown. To address this, we constructed a biophysically and anatomically detailed model of the rat BL and its local field potential (LFP) based on the physiological and anatomical literature, along with in vivo and in vitro data we collected on the activities of neurons within the rat BL. Remarkably, the model produced intermittent gamma oscillations (∼50 − 70 Hz) whose properties matched those recorded in vivo, including their entrainment of spiking. BL gamma-band oscillations were generated by the intrinsic circuitry, depending upon reciprocal interactions between PNs and fast-spiking interneurons (FSIs), while connections within these cell types affected the rhythm’s frequency. The model allowed us to conduct experimentally impossible tests to characterize the synaptic and spatial properties of gamma. The entrainment of individual neurons to gamma depended on the number of afferent connections they received, and gamma bursts were spatially restricted in the BL. Importantly, the gamma rhythm synchronized PNs and mediated competition between ensembles. Together, these results indicate that the recurrent connectivity of BL expands its computational and communication repertoire.

Four Unique Interneuron Populations Reside in Neocortical Layer 1

Sensory perception depends on neocortical computations that contextually adjust sensory signals in different internal and environmental contexts. Neocortical layer 1 (L1) is the main target of cortical and subcortical inputs that provide "top-down" information for context-dependent sensory processing. Although L1 is devoid of excitatory cells, it contains the distal "tuft" dendrites of pyramidal cells (PCs) located in deeper layers. L1 also contains a poorly characterized population of GABAergic interneurons (INs), which regulate the impact that different top-down inputs have on PCs. A poor comprehension of L1 IN subtypes and how they affect PC activity has hampered our understanding of the mechanisms that underlie contextual modulation of sensory processing. We used novel genetic strategies in male and female mice combined with electrophysiological and morphological methods to help resolve differences that were unclear when using only electrophysiological and/or morphological approaches. We discovered that L1 contains four distinct populations of INs, each with a unique molecular profile, morphology, and electrophysiology, including a previously overlooked IN population (named here "canopy cells") representing 40% of L1 INs. In contrast to what is observed in other layers, most L1 neurons appear to be unique to the layer, highlighting the specialized character of the signal processing that takes place in L1. This new understanding of INs in L1, as well as the application of genetic methods based on the markers described here, will enable investigation of the cellular and circuit mechanisms of top-down processing in L1 with unprecedented detail.SIGNIFICANCE STATEMENT Neocortical layer 1 (L1) is the main target of corticocortical and subcortical projections that mediate top-down or context-dependent sensory perception. However, this unique layer is often referred to as "enigmatic" because its neuronal composition has been difficult to determine. Using a combination of genetic, electrophysiological, and morphological approaches that helped to resolve differences that were unclear when using a single approach, we were able to decipher the neuronal composition of L1. We identified markers that distinguish L1 neurons and found that the layer contains four populations of GABAergic interneurons, each with unique molecular profiles, morphologies, and electrophysiological properties. These findings provide a new framework for studying the circuit mechanisms underlying the processing of top-down inputs in neocortical L1.

Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons

Visual stimuli evoke activity in visual cortical neuronal populations. Neuronal activity can be selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined trial averaged tuning properties. However, given any single stimulus parameter, a large number of neurons in visual cortex remain unmodulated, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in neuronal populations consisting of both tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the resultant population correlation structure, or functional network, with graph theory. Using the graph neighbors of a neuron, we find that the local population, including both tuned and untuned neurons, are able to predict individual neuron activity on a moment to moment basis, while also recapitulating tuning properties of tuned neurons. Variance explained in total population activity scales with the number of neurons imaged, demonstrating larger sample sizes are required to fully capture local network interactions. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.

Rapid Rebalancing of Excitation and Inhibition by Cortical Circuitry

Excitation is balanced by inhibition to cortical neurons across a wide range of conditions. To understand how this relationship is maintained, we broadly suppressed the activity of parvalbumin-expressing (PV+) inhibitory neurons and asked how this affected the balance of excitation and inhibition throughout auditory cortex. Activating archaerhodopsin in PV+ neurons effectively suppressed them in layer 4. However, the resulting increase in excitation outweighed Arch suppression and produced a net increase in PV+ activity in downstream layers. Consequently, suppressing PV+ neurons did not reduce inhibition to principal neurons (PNs) but instead resulted in a tightly coordinated increase in both excitation and inhibition. The increase in inhibition constrained the magnitude of PN spiking responses to the increase in excitation and produced nonlinear changes in spike tuning. Excitatory-inhibitory rebalancing is mediated by strong PN-PV+ connectivity within and between layers and is likely engaged during normal cortical operation to ensure balance in downstream neurons.

40Hz auditory steady-state response in schizophrenia: Sensitivity to stimulation type (clicks versus flutter amplitude-modulated tones)

Auditory steady-state response (ASSR) at 40Hz has been proposed as a potential biomarker for schizophrenia. The ASSR studies in patients have used click stimulation or amplitude-modulated tones. However, the sensitivity of 40Hz ASSRs to different stimulation types in the same group of patients has not been previously evaluated. Two stimulation types for ASSRs were tested in this study: (1) 40Hz clicks and (2) flutter-amplitude modulated tones. The mean phase-locking index, evoked amplitude and event-related spectral perturbation values were compared between schizophrenia patients (n=26) and healthy controls (n=20). Both stimulation types resulted in the observation of impaired phase-locking and power measures of late (200-500ms) 40Hz ASSR in patients compared to healthy controls. The early-latency (0-100ms) 40Hz ASSR part was diminished in the schizophrenia group in response to clicks only. The late-latency 40Hz ASSR parameters obtained through different stimulation types correlated in healthy subjects but not in patients. We conclude that flutter amplitude-modulated tone stimulation, due to its potential to reveal late-latency entrainment deficits, is suitable for use in clinical populations. Careful consideration of experimental stimulation settings can contribute to the interpretation of ASSR deficits and utilization as a potential biomarker.

From the statistics of connectivity to the statistics of spike times in neuronal networks

An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.

GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits

Cortical networks are composed of glutamatergic excitatory projection neurons and local GABAergic inhibitory interneurons that gate signal flow and sculpt network dynamics. Although they represent a minority of the total neocortical neuronal population, GABAergic interneurons are highly heterogeneous, forming functional classes based on their morphological, electrophysiological, and molecular features, as well as connectivity and in vivo patterns of activity. Here we review our current understanding of neocortical interneuron diversity and the properties that distinguish cell types. We then discuss how the involvement of multiple cell types, each with a specific set of cellular properties, plays a crucial role in diversifying and increasing the computational power of a relatively small number of simple circuit motifs forming cortical networks. We illustrate how recent advances in the field have shed light onto the mechanisms by which GABAergic inhibition contributes to network operations.

Population activity structure of excitatory and inhibitory neurons

Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure.

When do correlations increase with firing rates in recurrent networks?

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix.

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. We quantify spiking patterns by using pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments is that correlations can increase systematically with firing rate. Recent studies of a type of output cell in mouse retina found this relationship; furthermore, they determined that the increase of correlation with firing rate helped the cells encode information, provided the correlations were stimulus-dependent. Several theoretical studies have explored this basic structure, and found that it is generally beneficial to modulate correlations in this way. However—aside from mouse retinal cells referenced here—we do not yet have many examples of real neural circuits that show this correlation-firing rate pattern, so we do not know what common features (or mechanisms) might occur between them.

In this study, we address this question via a computational model. We set up a computational model with features representative of a generic cortical network, to see whether correlations would increase with firing rate. To produce different firing patterns, we varied excitatory coupling. We found that with stronger excitatory coupling, there was a positive relationship between pairwise correlations and firing rates. We used a network linear response theory to show why correlations could increase with firing rates in some networks, but not in others; this could be explained by how cells responded to fluctuations in inhibitory conductances.

The spatial structure of correlated neuronal variability

Shared neural variability is ubiquitous in cortical populations. While this variability is presumed to arise from overlapping synaptic input, its precise relationship to local circuit architecture remains unclear. We combine computational models and in vivo recordings to study the relationship between the spatial structure of connectivity and correlated variability in neural circuits. Extending the theory of networks with balanced excitation and inhibition, we find that spatially localized lateral projections promote weakly correlated spiking, but broader lateral projections produce a distinctive spatial correlation structure: nearby neuron pairs are positively correlated, pairs at intermediate distances are negatively correlated and distant pairs are weakly correlated. This non-monotonic dependence of correlation on distance is revealed in a new analysis of recordings from superficial layers of macaque primary visual cortex. Our findings show that incorporating distance-dependent connectivity improves the extent to which balanced network theory can explain correlated neural variability.

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

We seek to understand how billions of neurons in the brain work together to give rise to everyday brain function. In most current experimental settings, we can only record from tens of neurons for a few hours at a time. A major question in systems neuroscience is whether our interpretation of how neurons interact would change if we monitor orders of magnitude more neurons and for substantially more time. In this study, we use realistic networks of model neurons, which allow us to analyze the activity from as many model neurons as we want for as long as we want. For these models, we found that we can identify the salient interactions among neurons and interpret their activity meaningfully within the range of neurons and recording time available in current experiments. Furthermore, we studied how the neural activity from the models reflects how the neurons are connected. These results help to guide the interpretation of analyses using populations of neurons in the context of the larger network to understand brain function.

Dynamic Signal Tracking in a Simple V1 Spiking Model

This work is part of an effort to understand the neural basis for our visual system's ability, or failure, to accurately track moving visual signals. We consider here a ring model of spiking neurons, intended as a simplified computational model of a single hypercolumn of the primary visual cortex of primates. Signals that consist of edges with time-varying orientations localized in space are considered. Our model is calibrated to produce spontaneous and driven firing rates roughly consistent with experiments, and our two main findings, for which we offer dynamical explanation on the level of neuronal interactions, are the following. First, we have documented consistent transient overshoots in signal perception following signal switches due to emergent interactions of the E- and I-populations. Second, for continuously moving signals, we have found that accuracy is considerably lower at reversals of orientation than when continuing in the same direction (as when the signal is a rotating bar). To measure performance, we use two metrics, called fidelity and reliability, to compare signals reconstructed by the system to the ones presented and assess trial-to-trial variability. We propose that the same population mechanisms responsible for orientation selectivity also impose constraints on dynamic signal tracking that manifest in perception failures consistent with psychophysical observations.

Sodium salicylate reduces the level of GABAB receptors in the rat's inferior colliculus

Previous studies have indicated that sodium salicylate (SS) can cause hearing abnormalities through affecting the central auditory system. In order to understand central effects of the drug, we examined how a single intraperitoneal injection of the drug changed the level of subunits of the type-B γ-aminobutyric acid receptor (GABAB receptor) in the rat's inferior colliculus (IC). Immunohistochemical and western blotting experiments were conducted three hours following a drug injection, as previous studies indicated that a tinnitus-like behavior could be reliably induced in rats within this time period. Results revealed that both subunits of the receptor, GABABR1 and GABABR2, reduced their level over the entire area of the IC. Such a reduction was observed in both cell body and neuropil regions. In contrast, no changes were observed in other brain structures such as the cerebellum. Thus, a coincidence existed between a structure-specific reduction in the level of GABAB receptor subunits in the IC and the presence of a tinnitus-like behavior. This coincidence likely suggests that a reduction in the level of GABAB receptor subunits was involved in the generation of a tinnitus-like behavior and/or used by the nervous system to restore normal hearing following application of SS.

Local Integration Accounts for Weak Selectivity of Mouse Neocortical Parvalbumin Interneurons

Dissecting the functional roles of excitatory and inhibitory neurons in cortical circuits is a fundamental goal in neuroscience. Of particular interest are their roles in emergent cortical computations such as binocular integration in primary visual cortex (V1). We measured the binocular response selectivity of genetically defined subpopulations of excitatory and inhibitory neurons. Parvalbumin (PV+) interneurons received strong inputs from both eyes but lacked selectivity for binocular disparity. Because broad selectivity could result from heterogeneous synaptic input from neighboring neurons, we examined how individual PV+ interneuron selectivity compared to that of the local neuronal network, which is primarily composed of excitatory neurons. PV+ neurons showed functional similarity to neighboring neuronal populations over spatial distances resembling measurements of synaptic connectivity. On the other hand, excitatory neurons expressing CaMKIIα displayed no such functional similarity with the neighboring population. Our findings suggest that broad selectivity of PV+ interneurons results from nonspecific integration within local networks. VIDEO ABSTRACT.

Neocortical somatostatin neurons reversibly silence excitatory transmission via GABAb receptors

BACKGROUND

Understanding the dynamic range for excitatory transmission is a critical component of building a functional circuit diagram for the mammalian brain. Excitatory synaptic transmission is typically studied under optimized conditions, when background activity in the network is low. The range of synaptic function in the presence of inhibitory and excitatory activity within the neocortical circuit is unknown.

RESULTS

Paired-cell recordings from pyramidal neurons in acute brain slices of mouse somatosensory cortex show that excitatory synaptic transmission is markedly suppressed during spontaneous network activity: EPSP amplitudes are 2-fold smaller and failure rates are greater than 50%. This suppression is mediated by tonic activation of presynaptic GABAb receptors gated by the spontaneous activity of somatostatin-expressing (Sst) interneurons. Optogenetic suppression of Sst neuron firing was sufficient to enhance EPSP amplitude and reduce failure rates, effects that were fully reversible and occluded by GABAb antagonists.

CONCLUSIONS

These data indicate that Sst interneurons can rapidly and reversibly silence excitatory synaptic connections through the regulation of presynaptic release. This is an unanticipated role for Sst interneurons, which have been assigned a role only in fast GABAa-mediated inhibition. Because Sst interneuron activity has been shown to be regulated by sensory and motor input, these results suggest a mechanism by which functional connectivity and synaptic plasticity could be gated in a state-dependent manner.

Strength and duration of perisomatic GABAergic inhibition depend on distance between synaptically connected cells

GABAergic perisoma-inhibiting fast-spiking interneurons (PIIs) effectively control the activity of large neuron populations by their wide axonal arborizations. It is generally assumed that the output of one PII to its target cells is strong and rapid. Here, we show that, unexpectedly, both strength and time course of PII-mediated perisomatic inhibition change with distance between synaptically connected partners in the rodent hippocampus. Synaptic signals become weaker due to lower contact numbers and decay more slowly with distance, very likely resulting from changes in GABAA receptor subunit composition. When distance-dependent synaptic inhibition is introduced to a rhythmically active neuronal network model, randomly driven principal cell assemblies are strongly synchronized by the PIIs, leading to higher precision in principal cell spike times than in a network with uniform synaptic inhibition.

Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model

Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. We show that (a) NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b) The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c) Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d) Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e) Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity) that may facilitate the short-memory function of the PFC.

Working memory, the ability to retain information for a short period of time, is a fundamental cognitive function that shapes behavior. The cellular correlate of working memory is the prolonged spiking (persistent) activity of neurons in the prefrontal cortex. Impairments of prefrontal cortex functionalities and working memory have been associated with a variety of cognitive disorders, such as schizophrenia, the attention deficit hyperactivity disorder, and drug addiction. Hence, understanding how neurons embedded in the local circuitry support and maintain persistent activity is of outmost importance. Our work uses a multi-level integrative approach spanning from the dendritic, to the neuronal and network levels to identify the key biophysical and anatomical mechanisms contributing to persistent activity, leading to a number of high impact findings: it predicts a tradeoff between dendritic regenerative events and the size of a network expressing persistent activity. It also proposes when and how the persistent state can be stabilized, opening new avenues for pharmacological interventions. Finally, it describes decoding mechanisms for upcoming ON/OFF state transitions, furthering our understanding of information processing in the PFC and shedding new light on the emergence of anticipatory behaviors.

Activity-dependent changes in excitability of perirhinal cortex networks in vitro

Rat brain slices comprising the perirhinal cortex (PC) and a portion of the lateral nucleus of the amygdala (LA), in standard medium, can generate synchronous oscillatory activity that is associated with action potential discharge and reflects the activation of glutamatergic and GABAergic receptors. We report here that similar synchronous oscillatory events are recorded in the PC in response to single-shock, electrical stimuli delivered in LA. In addition, we found that the latency of these responses progressively increased when the stimulus interval was varied from 10 to 1 s; for example, the response latency during stimuli delivered at 1 Hz was more than twofold longer than that seen during stimulation at 0.1 Hz. This prolongation in latency occurred after approximately 5 stimuli, attained a steady value after 24-35 stimuli, and recovered to control values 30 s after stimulation arrest. These frequency-dependent changes in latency continued to occur during NMDA receptor antagonism but weakened following application of GABAA and/or GABAB receptor blockers. Our findings identify a new type of short-term plasticity that is mediated by GABA receptor function and may play a role in decreasing neuronal network synchronization during repeated activation. We propose that this frequency-dependent adaptive mechanism influences the excitability of limbic networks, thus potentially controlling epileptiform synchronization.

Interplay of intrinsic and synaptic conductances in the generation of high-frequency oscillations in interneuronal networks with irregular spiking

High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks.

Neurons in the brain engage in collective oscillations at different frequencies. Gamma and high-gamma oscillations (30–100 Hz and higher) have been associated with cognitive functions, and are altered in psychiatric disorders such as schizophrenia and autism. Our understanding of how high-frequency oscillations are orchestrated in the brain is still limited, but it is necessary for the development of effective clinical approaches to the treatment of these disorders. Some neuron types exhibit dynamical properties that can favour synchronization. The theory of weakly coupled oscillators showed how the phase response of individual neurons can predict the patterns of phase relationships that are observed at the network level. However, neurons in vivo do not behave like regular oscillators, but fire irregularly in a regime dominated by fluctuations. Hence, which intrinsic dynamical properties matter for synchronization, and in which regime, is still an open question. Here, we show how single-cell damped subthreshold oscillations enhance synchrony in interneuronal networks by introducing a depolarizing component, mediated by post-inhibitory rebound, that is correlated among neurons due to common inhibitory input.

GABAergic synapses: their plasticity and role in sensory cortex

The mammalian neocortex is composed of a variety of cell types organized in a highly interconnected circuit. GABAergic neurons account for only about 20% of cortical neurons. However, they show widespread connectivity and a high degree of diversity in morphology, location, electrophysiological properties and gene expression. In addition, distinct populations of inhibitory neurons have different sensory response properties, capacities for plasticity and sensitivities to changes in sensory experience. In this review we summarize experimental evidence regarding the properties of GABAergic neurons in primary sensory cortex. We will discuss how distinct GABAergic neurons and different forms of GABAergic inhibitory plasticity may contribute to shaping sensory cortical circuit activity and function.

Spatial pattern of intra-laminar connectivity in supragranular mouse auditory cortex

Neuronal responses and topographic organization of feature selectivity in the cerebral cortex are shaped by ascending inputs and by intracortical connectivity. The mammalian primary auditory cortex has a tonotopic arrangement at large spatial scales (greater than 300 microns). This large-scale architecture breaks down in supragranular layers at smaller scales (around 300 microns), where nearby frequency and sound level tuning properties can be quite heterogeneous. Since layer 4 has a more homogeneous architecture, the heterogeneity in supragranular layers might be caused by heterogeneous ascending input or via heterogeneous intralaminar connections. Here we measure the functional 2-dimensional spatial connectivity pattern of the supragranular auditory cortex on micro-column scales. In general connection probability decreases with radial distance from each neuron, but the decrease is steeper in the isofrequency axis leading to an anisotropic distribution of connection probability with respect to the tonotopic axis. In addition to this radial decrease in connection probability we find a patchy organization of inhibitory and excitatory synaptic inputs that is also anisotropic with respect to the tonotopic axis. These periodicities are at spatial scales of ~100 and ~300 μm. While these spatial periodicities show anisotropy in auditory cortex, they are isotropic in visual cortex, indicating region specific differences in intralaminar connections. Together our results show that layer 2/3 neurons in auditory cortex show specific spatial intralaminar connectivity despite the overtly heterogeneous tuning properties.

Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex

Correlations in the spiking activity of neurons have been found in many regions of the cortex under multiple experimental conditions and are postulated to have important consequences for neural population coding. While there is a large body of extracellular data reporting correlations of various strengths, the subthreshold events underlying the origin and magnitude of signal-independent correlations (called noise or spike count correlations) are unknown. Here we investigate, using intracellular recordings, how synaptic input correlations from shared presynaptic neurons translate into membrane potential and spike-output correlations. Using a pharmacologically activated thalamocortical slice preparation, we perform simultaneous recordings from pairs of layer IV neurons in the auditory cortex of mice and measure synaptic potentials/currents, membrane potentials, and spiking outputs. We calculate cross-correlations between excitatory and inhibitory inputs to investigate correlations emerging from the network. We furthermore evaluate membrane potential correlations near resting potential to study how excitation and inhibition combine and affect spike-output correlations. We demonstrate directly that excitation is correlated with inhibition thereby partially canceling each other and resulting in weak membrane potential and spiking correlations between neurons. Our data suggest that cortical networks are set up to partially cancel correlations emerging from the connections between neurons. This active decorrelation is achieved because excitation and inhibition closely track each other. Our results suggest that the numerous shared presynaptic inputs do not automatically lead to increased spiking correlations.

In sync: gamma oscillations and emotional memory

Emotional experiences leave vivid memories that can last a lifetime. The emotional facilitation of memory has been attributed to the engagement of diffusely projecting neuromodulatory systems that enhance the consolidation of synaptic plasticity in regions activated by the experience. This process requires the propagation of signals between brain regions, and for those signals to induce long-lasting synaptic plasticity. Both of these demands are met by gamma oscillations, which reflect synchronous population activity on a fast timescale (35-120 Hz). Regions known to participate in the formation of emotional memories, such as the basolateral amygdala, also promote gamma-band activation throughout cortical and subcortical circuits. Recent studies have demonstrated that gamma oscillations are enhanced during emotional situations, coherent between regions engaged by salient stimuli, and predict subsequent memory for cues associated with aversive stimuli. Furthermore, neutral stimuli that come to predict emotional events develop enhanced gamma oscillations, reflecting altered processing in the brain, which may underpin how past emotional experiences color future learning and memory.

Functional properties and short-term dynamics of unidirectional and reciprocal synaptic connections between layer 2/3 pyramidal cells and fast-spiking interneurons in juvenile rat prefrontal cortex

The interactions between inhibitory fast-spiking (FS) interneurons and excitatory pyramidal neurons contribute to the fundamental properties of cortical networks. An important role for FS interneurons in mediating rapid inhibition in local sensory and motor cortex microcircuits and processing thalamic inputs to the cortex has been shown in multiple reports; however, studies in the prefrontal cortex, a key neocortical region supporting working memory, are less numerous. In the present work, connections between layer 2/3 pyramidal cells and FS interneurons were studied with paired whole-cell recordings in acute neocortical slices of the medial prefrontal cortex from juvenile rats. The connection rate between FS interneurons and pyramidal neurons was about 40% in each direction with 16% of pairs connected reciprocally. Excitatory and inhibitory connections had a high efficacy and a low neurotransmission failure rate. Sustained presynaptic activity decreased the amplitude of responses and increased the failure rate more in excitatory connections than in inhibitory connections. In the reciprocal connections between the FS and pyramidal neurons, inhibitory and excitatory neurotransmission was more efficient and had a lower failure rate than in the unidirectional connections; the differences increased during the train stimulation. These results suggest the presence of distinct preferential subnetworks between FS interneurons and pyramidal cells in the rat prefrontal cortex that might be specific for this cortical area.

Gap junctions facilitate propagation of synchronous firing in the cortical neural population: a numerical simulation study

This study investigates the effect of gap junctions on firing propagation in a feedforward neural network by a numerical simulation with biologically plausible parameters. Gap junctions are electrical couplings between two cells connected by a binding protein, connexin. Recent electrophysiological studies have reported that a large number of inhibitory neurons in the mammalian cortex are mutually connected by gap junctions, and synchronization of gap junctions, spread over several hundred microns, suggests that these have a strong effect on the dynamics of the cortical network. However, the effect of gap junctions on firing propagation in cortical circuits has not been examined systematically. In this study, we perform numerical simulations using biologically plausible parameters to clarify this effect on population firing in a feedforward neural network. The results suggest that gap junctions switch the temporally uniform firing in a layer to temporally clustered firing in subsequent layers, resulting in an enhancement in the propagation of population firing in the feedforward network. Because gap junctions are often modulated in physiological conditions, we speculate that gap junctions could be related to a gating function of population firing in the brain.

Cortical information flow in Parkinson's disease: a composite network/field model

The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power toward lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality between cortical layers in the beta and low gamma frequency bands, but this causality was largely absent in the PD model. In particular, the reduction in Granger causality from the main “input” layer of the cortex (layer 4) to the main “output” layer (layer 5) was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than pure white noise.

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.

Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons

Many features of synaptic connectivity are ubiquitous among cortical systems. Cortical networks are dominated by excitatory neurons and synapses, are sparsely connected, and function with stereotypically distributed connection weights. We show that these basic structural and functional features of synaptic connectivity arise readily from the requirement of efficient associative memory storage. Our theory makes two fundamental predictions. First, we predict that, despite a large number of neuron classes, functional connections between potentially connected cells must be realized with <50% probability if the presynaptic cell is excitatory and >50% probability if the presynaptic cell is inhibitory. Second, we establish a unique relation between probability of connection and coefficient of variation in connection weights. These predictions are consistent with a dataset of 74 published experiments reporting connection probabilities and distributions of postsynaptic potential amplitudes in various cortical systems. What is more, our theory explains the shapes of the distributions obtained in these experiments.

Generation of spike latency tuning by thalamocortical circuits in auditory cortex

In many sensory systems, the latency of spike responses of individual neurons is found to be tuned for stimulus features and proposed to be used as a coding strategy. Whether the spike latency tuning is simply relayed along sensory ascending pathways or generated by local circuits remains unclear. Here, in vivo whole-cell recordings from rat auditory cortical neurons in layer 4 revealed that the onset latency of their aggregate thalamic input exhibited nearly flat tuning for sound frequency, whereas their spike latency tuning was much sharper with a broadly expanded dynamic range. This suggests that the spike latency tuning is not simply inherited from the thalamus, but can be largely reconstructed by local circuits in the cortex. Dissecting of thalamocortical circuits and neural modeling further revealed that broadly tuned intracortical inhibition prolongs the integration time for spike generation preferentially at off-optimal frequencies, while sharply tuned intracortical excitation shortens it selectively at the optimal frequency. Such push and pull mechanisms mediated likely by feedforward excitatory and inhibitory inputs respectively greatly sharpen the spike latency tuning and expand its dynamic range. The modulation of integration time by thalamocortical-like circuits may represent an efficient strategy for converting information spatially coded in synaptic strength to temporal representation.

Slow dynamics and high variability in balanced cortical networks with clustered connections

Anatomical studies demonstrate that excitatory connections in cortex are not uniformly distributed across a network but instead exhibit clustering into groups of highly connected neurons. The implications of clustering for cortical activity are unclear. We studied the effect of clustered excitatory connections on the dynamics of neuronal networks that exhibited high spike time variability owing to a balance between excitation and inhibition. Even modest clustering substantially changed the behavior of these networks, introducing slow dynamics during which clusters of neurons transiently increased or decreased their firing rate. Consequently, neurons exhibited both fast spiking variability and slow firing rate fluctuations. A simplified model shows how stimuli bias networks toward particular activity states, thereby reducing firing rate variability as observed experimentally in many cortical areas. Our model thus relates cortical architecture to the reported variability in spontaneous and evoked spiking activity.

Spatial profile of excitatory and inhibitory synaptic connectivity in mouse primary auditory cortex

The role of local cortical activity in shaping neuronal responses is controversial. Among other questions, it is unknown how the diverse response patterns reported in vivo-lateral inhibition in some cases, approximately balanced excitation and inhibition (co-tuning) in others-compare to the local spread of synaptic connectivity. Excitatory and inhibitory activity might cancel each other out, or, whether one outweighs the other, receptive field properties might be substantially affected. As a step toward addressing this question, we used multiple intracellular recording in mouse primary auditory cortical slices to map synaptic connectivity among excitatory pyramidal cells and the two broad classes of inhibitory cells, fast-spiking (FS) and non-FS cells in the principal input layer. Connection probability was distance-dependent; the spread of connectivity, parameterized by Gaussian fits to the data, was comparable for all cell types, ranging from 85 to 114 μm. With brief stimulus trains, unitary synapses formed by FS interneurons were stronger than other classes of synapses; synapse strength did not correlate with distance between cells. The physiological data were qualitatively consistent with predictions derived from anatomical reconstruction. We also analyzed the truncation of neuronal processes due to slicing; overall connectivity was reduced but the spatial pattern was unaffected. The comparable spatial patterns of connectivity and relatively strong excitatory-inhibitory interconnectivity are consistent with a theoretical model where either lateral inhibition or co-tuning can predominate, depending on the structure of the input.

Laminarly orthogonal excitation of fast-spiking and low-threshold-spiking interneurons in mouse motor cortex

In motor cortex, long-range output to subcortical motor circuits depends on excitatory and inhibitory inputs converging on projection neurons in layers 5A/B. How interneurons interconnect with these projection neurons, and whether these microcircuits are interneuron and/or projection specific, is unclear. We found that fast-spiking interneurons received strong intralaminar (horizontal) excitation from pyramidal neurons in layers 5A/B including corticostriatal and corticospinal neurons, implicating them in mediating disynaptic recurrent, feedforward, and feedback inhibition within and across the two projection classes. Low-threshold-spiking (LTS) interneurons were instead strongly excited by descending interlaminar (vertical) input from layer 2/3 pyramidal neurons, implicating them in mediating disynaptic feedforward inhibition to both projection classes. Furthermore, in a novel pattern, lower layer 2/3 preferentially excited interneurons in one layer (5A/LTS) and excitatory neurons in another (5B/corticospinal). Thus, these inhibitory microcircuits in mouse motor cortex follow an orderly arrangement that is laminarly orthogonalized by interneuron-specific, projection-nonspecific connectivity.

Correlated neural variability in persistent state networks

Neural activity that persists long after stimulus presentation is a biological correlate of short-term memory. Variability in spiking activity causes persistent states to drift over time, ultimately degrading memory. Models of short-term memory often assume that the input fluctuations to neural populations are independent across cells, a feature that attenuates population-level variability and stabilizes persistent activity. However, this assumption is at odds with experimental recordings from pairs of cortical neurons showing that both the input currents and output spike trains are correlated. It remains unclear how correlated variability affects the stability of persistent activity and the performance of cognitive tasks that it supports. We consider the stochastic long-timescale attractor dynamics of pairs of mutually inhibitory populations of spiking neurons. In these networks, persistent activity was less variable when correlated variability was globally distributed across both populations compared with the case when correlations were locally distributed only within each population. Using a reduced firing rate model with a continuum of persistent states, we show that, when input fluctuations are correlated across both populations, they drive firing rate fluctuations orthogonal to the persistent state attractor, thereby causing minimal stochastic drift. Using these insights, we establish that distributing correlated fluctuations globally as opposed to locally improves network's performance on a two-interval, delayed response discrimination task. Our work shows that the correlation structure of input fluctuations to a network is an important factor when determining long-timescale, persistent population spiking activity.

Impact of network structure and cellular response on spike time correlations

Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neurons. As a result there is increasing interest in the structure of cooperative – or correlated – activity in neural populations, and in the possible impact of such correlations on the neural code. A fundamental theoretical challenge is to understand how the architecture of network connectivity along with the dynamical properties of single cells shape the magnitude and timescale of correlations. We provide a general approach to this problem by extending prior techniques based on linear response theory. We consider networks of general integrate-and-fire cells with arbitrary architecture, and provide explicit expressions for the approximate cross-correlation between constituent cells. These correlations depend strongly on the operating point (input mean and variance) of the neurons, even when connectivity is fixed. Moreover, the approximations admit an expansion in powers of the matrices that describe the network architecture. This expansion can be readily interpreted in terms of paths between different cells. We apply our results to large excitatory-inhibitory networks, and demonstrate first how precise balance – or lack thereof – between the strengths and timescales of excitatory and inhibitory synapses is reflected in the overall correlation structure of the network. We then derive explicit expressions for the average correlation structure in randomly connected networks. These expressions help to identify the important factors that shape coordinated neural activity in such networks.

Is neural activity more than the sum of its individual parts? What is the impact of cooperative, or correlated, spiking among multiple cells? We can start addressing these questions, as rapid advances in experimental techniques allow simultaneous recordings from ever-increasing populations. However, we still lack a general understanding of the origin and consequences of the joint activity that is revealed. The challenge is compounded by the fact that both the intrinsic dynamics of single cells and the correlations among then vary depending on the overall state of the network. Here, we develop a toolbox that addresses this issue. Specifically, we show how linear response theory allows for the expression of correlations explicitly in terms of the underlying network connectivity and known single-cell properties – and that the predictions of this theory accurately match simulations of a touchstone, nonlinear model in computational neuroscience, the general integrate-and-fire cell. Thus, our theory should help unlock the relationship between network architecture, single-cell dynamics, and correlated activity in diverse neural circuits.

Neural correlation is stimulus modulated by feedforward inhibitory circuitry

Correlated variability of neural spiking activity has important consequences for signal processing. How incoming sensory signals shape correlations of population responses remains unclear. Cross-correlations between spiking of different neurons may be particularly consequential in sparsely firing neural populations such as those found in layer 2/3 of sensory cortex. In rat whisker barrel cortex, we found that pairs of excitatory layer 2/3 neurons exhibit similarly low levels of spike count correlation during both spontaneous and sensory-evoked states. The spontaneous activity of excitatory-inhibitory neuron pairs is positively correlated, while sensory stimuli actively decorrelate joint responses. Computational modeling shows how threshold nonlinearities and local inhibition form the basis of a general decorrelating mechanism. We show that inhibitory population activity maintains low correlations in excitatory populations, especially during periods of sensory-evoked coactivation. The role of feedforward inhibition has been previously described in the context of trial-averaged phenomena. Our findings reveal a novel role for inhibition to shape correlations of neural variability and thereby prevent excessive correlations in the face of feedforward sensory-evoked activation.

Assessing cortical network properties using TMS-EEG

The past decade has seen significant developments in the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to directly assess cortical network properties such as excitability and connectivity in humans. New hardware solutions, improved EEG amplifier technology, and advanced data processing techniques have allowed substantial reduction of the TMS-induced artifact, which had previously rendered concurrent TMS-EEG impossible. Various physiological artifacts resulting from TMS have also been identified, and methods are being developed to either minimize or remove these sources of artifact. With these developments, TMS-EEG has unlocked regions of the cortex to researchers that were previously inaccessible to TMS. By recording the TMS-evoked response directly from the cortex, TMS-EEG provides information on the excitability, effective connectivity, and oscillatory tuning of a given cortical area, removing the need to infer such measurements from indirect measures. In the following review, we investigate the different online and offline methods for reducing artifacts in TMS-EEG recordings and the physiological information contained within the TMS-evoked cortical response. We then address the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity, before briefly reviewing studies that have utilized TMS-EEG to explore cortical network properties at rest and during different functional brain states.

Minimal size of cell assemblies coordinated by gamma oscillations

In networks of excitatory and inhibitory neurons with mutual synaptic coupling, specific drive to sub-ensembles of cells often leads to gamma-frequency (25–100 Hz) oscillations. When the number of driven cells is too small, however, the synaptic interactions may not be strong or homogeneous enough to support the mechanism underlying the rhythm. Using a combination of computational simulation and mathematical analysis, we study the breakdown of gamma rhythms as the driven ensembles become too small, or the synaptic interactions become too weak and heterogeneous. Heterogeneities in drives or synaptic strengths play an important role in the breakdown of the rhythms; nonetheless, we find that the analysis of homogeneous networks yields insight into the breakdown of rhythms in heterogeneous networks. In particular, if parameter values are such that in a homogeneous network, it takes several gamma cycles to converge to synchrony, then in a similar, but realistically heterogeneous network, synchrony breaks down altogether. This leads to the surprising conclusion that in a network with realistic heterogeneity, gamma rhythms based on the interaction of excitatory and inhibitory cell populations must arise either rapidly, or not at all. For given synaptic strengths and heterogeneities, there is a (soft) lower bound on the possible number of cells in an ensemble oscillating at gamma frequency, based simply on the requirement that synaptic interactions between the two cell populations be strong enough. This observation suggests explanations for recent experimental results concerning the modulation of gamma oscillations in macaque primary visual cortex by varying spatial stimulus size or attention level, and for our own experimental results, reported here, concerning the optogenetic modulation of gamma oscillations in kainate-activated hippocampal slices. We make specific predictions about the behavior of pyramidal cells and fast-spiking interneurons in these experiments.

Gamma-frequency (25–100 Hz) oscillations in the brain often arise as a result of an interaction between excitatory and inhibitory cell populations. For this mechanism to work, the interaction must be sufficiently strong, and connectivity and external drives to participating neurons must be sufficiently homogeneous. As the interactions become weaker, either because the neuronal ensembles become smaller or because synapses weaken, the rhythms deteriorate, and eventually break down. This fact, by itself, is not surprising, but details of how the breakdown occurs are subtle. In particular, our analysis leads to the conclusion that in realistically heterogeneous networks, gamma rhythms must arise quickly, within a small number of oscillation periods, if they arise at all. Our findings suggest explanations for recent experimental findings concerning the minimal spatial extent of stimuli eliciting gamma oscillations in the primary visual cortex, the modulation of gamma oscillations in the primary visual cortex by attention, as well as our own experimental results, reported here, concerning the minimal light intensity below which optogenetic drive to pyramidal cells in a kainate-activated hippocampal slice results in disruption of an ongoing gamma oscillation. Our analysis leads to experimentally testable predictions about the behavior of the excitatory and inhibitory cells in these experiments.