Laminar-dependent effects of cortical state on auditory cortical spontaneous activity

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Associations between in vitro , in vivo and in silico cell classes in mouse primary visual cortex

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

Mutual Interactions between Brain States and Alzheimer's Disease Pathology: A Focus on Gamma and Slow Oscillations

Brain state varies from moment to moment. While brain state can be defined by ongoing neuronal population activity, such as neuronal oscillations, this is tightly coupled with certain behavioural or vigilant states. In recent decades, abnormalities in brain state have been recognised as biomarkers of various brain diseases and disorders. Intriguingly, accumulating evidence also demonstrates mutual interactions between brain states and disease pathologies: while abnormalities in brain state arise during disease progression, manipulations of brain state can modify disease pathology, suggesting a therapeutic potential. In this review, by focusing on Alzheimer's disease (AD), the most common form of dementia, we provide an overview of how brain states change in AD patients and mouse models, and how controlling brain states can modify AD pathology. Specifically, we summarise the relationship between AD and changes in gamma and slow oscillations. As pathological changes in these oscillations correlate with AD pathology, manipulations of either gamma or slow oscillations can modify AD pathology in mouse models. We argue that neuromodulation approaches to target brain states are a promising non-pharmacological intervention for neurodegenerative diseases.

Deep Predictive Learning in Neocortex and Pulvinar

How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism for the widely embraced idea that learning is driven by the differences between predictions and actual outcomes (i.e., predictive error-driven learning). Specifically, numerous weak projections into the pulvinar nucleus of the thalamus generate top-down predictions, and sparse driver inputs from lower areas supply the actual outcome, originating in Layer 5 intrinsic bursting neurons. Thus, the outcome representation is only briefly activated, roughly every 100 msec (i.e., 10 Hz, alpha), resulting in a temporal difference error signal, which drives local synaptic changes throughout the neocortex. This results in a biologically plausible form of error backpropagation learning. We implemented these mechanisms in a large-scale model of the visual system and found that the simulated inferotemporal pathway learns to systematically categorize 3-D objects according to invariant shape properties, based solely on predictive learning from raw visual inputs. These categories match human judgments on the same stimuli and are consistent with neural representations in inferotemporal cortex in primates.

GSK3β inhibition restores cortical gamma oscillation and cognitive behavior in a mouse model of NMDA receptor hypofunction relevant to schizophrenia

Cortical gamma oscillations are believed to be involved in mental processes which are disturbed in schizophrenia. For example, the magnitudes of sensory-evoked oscillations, as measured by auditory steady-state responses (ASSRs) at 40 Hz, are robustly diminished, whereas the baseline gamma power is enhanced in schizophrenia. Such dual gamma oscillation abnormalities are also present in a mouse model of N-methyl-D-aspartate receptor hypofunction (Ppp1r2cre/Grin1 knockout mice). However, it is unclear whether the abnormal gamma oscillations are associated with dysfunction in schizophrenia. We found that glycogen synthase kinase-3 (GSK3) is overactivated in corticolimbic parvalbumin-positive GABAergic interneurons in Grin1 mutant mice. Here we addressed whether GSK3β inhibition reverses both abnormal gamma oscillations and behavioral deficits with high correlation by pharmacological and genetic approach. We demonstrated that the paralog selective-GSK3β inhibitor, but not GSK3α inhibitor, normalizes the diminished ASSRs, excessive baseline gamma power, and deficits in spatial working memory and prepulse inhibition (PPI) of acoustic startle in Grin1 mutant mice. Cell-type specific GSK3B knockdown, but not GSK3A knockdown, also reversed abnormal gamma oscillations and behavioral deficits. Moreover, GSK3B knockdown, but not GSK3A knockdown, reverses the mutants' in vivo spike synchrony deficits. Finally, ex vivo patch-clamp recording from pairs of neighboring cortical pyramidal neurons showed a reduction of synchronous spontaneous inhibitory-postsynaptic-current events in mutants, which was reversed by GSK3β inhibition genetically and pharmacologically. Together, GSK3β inhibition in corticolimbic interneurons ameliorates the deficits in spatial working memory and PPI, presumably by restoration of synchronous GABA release, synchronous spike firing, and evoked-gamma power increase with lowered baseline power.

Local and Volume-Conducted Contributions to Cortical Field Potentials

Brain field potentials (FPs) can reach far from their sources, making difficult to know which waves come from where. We show that modern algorithms efficiently segregate the local and remote contributions to cortical FPs by recovering the generator-specific spatial voltage profiles. We investigated experimentally and numerically the local and remote origin of FPs in different cortical areas in anesthetized rats. All cortices examined show significant state, layer, and region dependent contribution of remote activity, while the voltage profiles help identify their subcortical or remote cortical origin. Co-activation of different cortical modules can be discriminated by the distinctive spatial features of the corresponding profiles. All frequency bands contain remote activity, thus influencing the FP time course, in cases drastically. The reach of different FP patterns is boosted by spatial coherence and curved geometry of the sources. For instance, slow cortical oscillations reached the entire brain, while hippocampal theta reached only some portions of the cortex. In anterior cortices, most alpha oscillations have a remote origin, while in the visual cortex the remote theta and gamma even surpass the local contribution. The quantitative approach to local and distant FP contributions helps to refine functional connectivity among cortical regions, and their relation to behavior.

Early Disruption of Cortical Sleep-Related Oscillations in a Mouse Model of Dementia With Lewy Bodies (DLB) Expressing Human Mutant (A30P) Alpha-Synuclein

Changes in sleep behavior and sleep-related cortical activity have been reported in conditions associated with abnormal alpha-synuclein (α-syn) expression, in particular Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). Notably, changes can occur in patients years before the onset of cognitive decline. Sleep-related network oscillations play a key role in memory function, but how abnormal α-syn impacts the generation of such activity is currently unclear. To determine whether early changes in sleep-related network activity could also be observed, prior to any previously reported cognitive dysfunction, we used mice that over-express human mutant α-syn (A30P). Recordings in vivo were performed under urethane anesthesia in the medial prefrontal cortex (mPFC) and CA1 region of the hippocampus in young male (2.5 – 4 months old) A30P and age-matched wild type (WT) mice. We found that the slow oscillation (SO) < 1 Hz frequency was significantly faster in both the mPFC and hippocampus in A30P mice, and Up-state-associated fast oscillations at beta (20 – 30 Hz) and gamma (30 – 80 Hz) frequencies were delayed relative to the onset of the Up-state. Spindle (8 – 15 Hz) activity in the mPFC was also altered in A30P mice, as spindles were shorter in duration and had reduced density compared to WT. These changes demonstrate that dysregulation of sleep-related oscillations occurs in young A30P mice long before the onset of cognitive dysfunction. Our data suggest that, as seen in patients, changes in sleep-related oscillations are an early consequence of abnormal α-syn aggregation in A30P mice.

Effects of Cortical Cooling on Activity Across Layers of the Rat Barrel Cortex

Moderate cortical cooling is known to suppress slow oscillations and to evoke persistent cortical activity. However, the cooling-induced changes in electrical activity across cortical layers remain largely unknown. Here, we performed multi-channel local field potential (LFP) and multi-unit activity (MUA) recordings with linear silicone probes through the layers of single cortical barrel columns in urethane-anesthetized rats under normothermia (38°C) and during local cortical surface cooling (30°C). During cortically generated slow oscillations, moderate cortical cooling decreased delta wave amplitude, delta-wave occurrence, the duration of silent states, and delta wave-locked MUA synchronization. Moderate cortical cooling increased total time spent in the active state and decreased total time spent in the silent state. Cooling-evoked changes in the MUA firing rate in cortical layer 5 (L5) varied from increase to decrease across animals, and the polarity of changes in L5 MUA correlated with changes in total time spent in the active state. The decrease in temperature reduced MUA firing rates in all other cortical layers. Sensory-evoked MUA responses also decreased during cooling through all cortical layers. The cooling-dependent slowdown was detected at the fast time-scale with a decreased frequency of sensory-evoked high-frequency oscillations (HFO). Thus, moderate cortical cooling suppresses slow oscillations and desynchronizes neuronal activity through all cortical layers, and is associated with reduced firing across all cortical layers except L5, where cooling induces variable and non-consistent changes in neuronal firing, which are common features of the transition from slow-wave synchronization to desynchronized activity in the barrel cortex.

Electrical stimulation of the ventral tegmental area evokes sleep-like state transitions under urethane anaesthesia in the rat medial prefrontal cortex via dopamine D1 -like receptors

The role of dopamine in regulating sleep-state transitions during, both natural sleep and under anaesthesia, is still unclear. Recording in vivo in the rat mPFC under urethane anaesthesia, we observed predominantly slow wave activity (SWA) of <1 Hz in the local field potential interrupted by occasional spontaneous transitions to a low-amplitude-fast (LAF) pattern of activity. During periods of SWA, transitions to LAF activity could be rapidly and consistently evoked by electrical stimulation of the ventral tegmental area (VTA). Spontaneous LAF activity, and that evoked by stimulation of the VTA, consisted of fast oscillations similar to those seen in the rapid eye movement (REM)-like sleep state. Spontaneous and VTA stimulation-evoked LAF activity occurred simultaneously along the dorsoventral extent of all mPFC subregions. Evoked LAF activity depended on VTA stimulation current and could be elicited using either regular (25-50 Hz) or burst stimulation patterns and was reproducible upon repeated stimulation. Simultaneous extracellular single-unit recordings showed that during SWA, presumed pyramidal cells fired phasically and almost exclusively on the Up state, while during both spontaneous and VTA-evoked LAF activity, they fired tonically. The transition to LAF activity evoked by VTA stimulation depended on dopamine D1 -like receptor activation as it was almost completely blocked by systemic administration of the D1 -like receptor antagonist SCH23390. Overall, our data demonstrate that activation of dopamine D1 -like receptors in the mPFC is important for regulating sleep-like state transitions.

Three brain states in the hippocampus and cortex

Contemporary brain research seeks to understand how cognition is reducible to neural activity. Crucially, much of this effort is guided by a scientific paradigm that views neural activity as essentially driven by external stimuli. In contrast, recent perspectives argue that this paradigm is by itself inadequate and that understanding patterns of activity intrinsic to the brain is needed to explain cognition. Yet, despite this critique, the stimulus-driven paradigm still dominates-possibly because a convincing alternative has not been clear. Here, we review a series of findings suggesting such an alternative. These findings indicate that neural activity in the hippocampus occurs in one of three brain states that have radically different anatomical, physiological, representational, and behavioral correlates, together implying different functional roles in cognition. This three-state framework also indicates that neural representations in the hippocampus follow a surprising pattern of organization at the timescale of ∼1 s or longer. Lastly, beyond the hippocampus, recent breakthroughs indicate three parallel states in the cortex, suggesting shared principles and brain-wide organization of intrinsic neural activity.

Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque

Neural responses in the cerebral cortex change dramatically between the 'synchronized' state during sleep and 'desynchronized' state during wakefulness. Our understanding of cortical state emerges largely from experiments performed in sensory areas of head-fixed or tethered rodents due to technical limitations of recording from larger freely-moving animals for several hours. Here, we report a system integrating wireless electrophysiology, wireless eye tracking, and real-time video analysis to examine the dynamics of population activity in a high-level, executive area - dorsolateral prefrontal cortex (dlPFC) of unrestrained monkey. This technology allows us to identify cortical substates during quiet and active wakefulness, and transitions in population activity during rest. We further show that narrow-spiking neurons exhibit stronger synchronized fluctuations in population activity than broad-spiking neurons regardless of state. Our results show that cortical state is controlled by behavioral demands and arousal by asymmetrically modulating the slow response fluctuations of local excitatory and inhibitory cell populations.

Diverse tuning underlies sparse activity in layer 2/3 vibrissal cortex of awake mice

KEY POINTS

Sparse population activity is a common feature observed across cortical areas, yet the implications for sensory coding are not clear. We recorded single neuron activity in the vibrissal somatosensory cortex of awake head-fixed mice using the cell-attached technique. Unlike the anaesthetised condition, in awake mice a high-velocity, piezo-controlled whisker deflection excited only a small fraction of neurons. Manual probing of whiskers revealed that the majority of these silent neurons could be activated by specific forms of whisker-object contact. Our results suggest that sparse coding in vibrissal cortex may be due to high dimensionality of the stimulus space and narrow tuning of individual neurons.

ABSTRACT

It is widely reported that superficial layers of the somatosensory cortex exhibit sparse firing. This sparseness could reflect weak feedforward sensory inputs that are not sufficient to generate action potentials in these layers. Alternatively, sparseness might reflect tuning to unknown or higher-level complex features that are not fully explored in the stimulus space. Here, we examined these hypotheses by applying a range of vibrotactile and manual vibrissal stimuli in awake, head-fixed mice while performing loose-seal cell-attached recordings from the vibrissal primary somatosensory (vS1) cortex. A high-velocity stimulus delivered by a piezo-electric actuator evoked activity in a small fraction of regular spiking supragranular neurons (23%) in the awake condition. However, a majority of the supragranular regular spiking neurons (84%) were driven by manual stimulation of whiskers. Our results suggest that most neurons in the superficial layers of vS1 cortex contribute to coding in the awake condition when neurons may encounter their preferred feature(s) during whisker-object interactions.

Theta Oscillations Alternate With High Amplitude Neocortical Population Within Synchronized States

Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the neocortex and large- and small- irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis to parse which populations are involved in different kinds of neocortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects neocortical deep layers, and is associated with larger amplitude slower neocortical oscillations. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the neocortex, suggesting hippocampal origin, and are associated with smaller amplitude faster neocortical oscillations. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.

State-dependent geometry of population activity in rat auditory cortex

The accuracy of the neural code depends on the relative embedding of signal and noise in the activity of neural populations. Despite a wealth of theoretical work on population codes, there are few empirical characterizations of the high-dimensional signal and noise subspaces. We studied the geometry of population codes in the rat auditory cortex across brain states along the activation-inactivation continuum, using sounds varying in difference and mean level across the ears. As the cortex becomes more activated, single-hemisphere populations go from preferring contralateral loud sounds to a symmetric preference across lateralizations and intensities, gain-modulation effectively disappears, and the signal and noise subspaces become approximately orthogonal to each other and to the direction corresponding to global activity modulations. Level-invariant decoding of sound lateralization also becomes possible in the active state. Our results provide an empirical foundation for the geometry and state-dependence of cortical population codes.

Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse

The relationship between mesoscopic local field potentials (LFPs) and single-neuron firing in the multi-layered neocortex is poorly understood. Simultaneous recordings from all layers in the primary visual cortex (V1) of the behaving mouse revealed functionally defined layers in V1. The depth of maximum spike power and sink-source distributions of LFPs provided consistent laminar landmarks across animals. Coherence of gamma oscillations (30-100 Hz) and spike-LFP coupling identified six physiological layers and further sublayers. Firing rates, burstiness, and other electrophysiological features of neurons displayed unique layer and brain state dependence. Spike transmission strength from layer 2/3 cells to layer 5 pyramidal cells and interneurons was stronger during waking compared with non-REM sleep but stronger during non-REM sleep among deep-layer excitatory neurons. A subset of deep-layer neurons was active exclusively in the DOWN state of non-REM sleep. These results bridge mesoscopic LFPs and single-neuron interactions with laminar structure in V1.

Firing-rate models for neurons with a broad repertoire of spiking behaviors

Capturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate responses are generally too complex to be captured by first-order low-pass filters but require bandpass filters instead; (iii) that linear-nonlinear models capture the response of AMAT models better than of Izhikevich models; (iv) that the wide range of response types evoked by current-injection experiments collapses to few response types when neurons are driven by stationary or sinusoidally modulated Poisson input; and (v) that AMAT and Izhikevich models show different responses to spike input despite identical responses to current injections. Together, these findings suggest that rate-based models of network dynamics may capture a wider range of neuronal response properties by incorporating second-order bandpass filters fitted to responses of spiking model neurons. These models may contribute to bringing rate-based network modeling closer to the reality of biological neuronal networks.

The Locus Coeruleus Is a Complex and Differentiated Neuromodulatory System

Diffuse projections of locus coeruleus (LC) neurons and evidence of synchronous spiking have long been perceived as features of global neuromodulation. Recent studies demonstrated the possibility of targeted modulation by subsets of LC neurons. Non-global neuromodulation depends on target specificity and the differentiated spatiotemporal dynamics within LC. Here, we characterized interactions between 3,164 LC cell pairs in the rat LC under urethane anesthesia. Spike count correlations were near zero and only a small proportion of unit pairs had synchronized spontaneous (15%) or evoked (16%) discharge. We identified infra-slow (0.01-1 Hz) fluctuations of LC unit spike rate, which were also asynchronous across the population. Despite overall sparse population synchrony, we report the existence of LC ensembles and relate them to forebrain projection targets. We also show that spike waveform width was related to ensemble membership, propensity for synchronization, and interactions with cortex. Our findings suggest a partly differentiated and target-specific noradrenergic signal.

The Impact of Anesthetic State on Spike-Sorting Success in the Cortex: A Comparison of Ketamine and Urethane Anesthesia

Spike sorting is an essential first step in most analyses of extracellular in vivo electrophysiological recordings. Here we show that spike-sorting success depends critically on characteristics of coordinated population activity that can differ between anesthetic states. In tetrode recordings from mouse auditory cortex, spike sorting was significantly less successful under ketamine/medetomidine (ket/med) than urethane anesthesia. Surprisingly, this difficulty with sorting under ket/med anesthesia did not appear to result from either greater millisecond-scale burstiness of neural activity or increased coordination of activity among neighboring neurons. Rather, the key factor affecting sorting success appeared to be the amount of coordinated population activity at long time intervals and across large cortical distances. We propose that spike-sorting success is directly dependent on overall coordination of activity, and is most disrupted by large-scale fluctuations in cortical population activity. Reliability of single-unit recording may therefore differ not only between urethane-anesthetized and ket/med-anesthetized states as demonstrated here, but also between synchronized and desynchronized states, asleep and awake states, or inattentive and attentive states in unanesthetized animals.

Distinct Temporal Coordination of Spontaneous Population Activity between Basal Forebrain and Auditory Cortex

The basal forebrain (BF) has long been implicated in attention, learning and memory, and recent studies have established a causal relationship between artificial BF activation and arousal. However, neural ensemble dynamics in the BF still remains unclear. Here, recording neural population activity in the BF and comparing it with simultaneously recorded cortical population under both anesthetized and unanesthetized conditions, we investigate the difference in the structure of spontaneous population activity between the BF and the auditory cortex (AC) in mice. The AC neuronal population show a skewed spike rate distribution, a higher proportion of short (≤80 ms) inter-spike intervals (ISIs) and a rich repertoire of rhythmic firing across frequencies. Although the distribution of spontaneous firing rate in the BF is also skewed, a proportion of short ISIs can be explained by a Poisson model at short time scales (≤20 ms) and spike count correlations are lower compared to AC cells, with optogenetically identified cholinergic cell pairs showing exceptionally higher correlations. Furthermore, a smaller fraction of BF neurons shows spike-field entrainment across frequencies: a subset of BF neurons fire rhythmically at slow (≤6 Hz) frequencies, with varied phase preferences to ongoing field potentials, in contrast to a consistent phase preference of AC populations. Firing of these slow rhythmic BF cells is correlated to a greater degree than other rhythmic BF cell pairs. Overall, the fundamental difference in the structure of population activity between the AC and BF is their temporal coordination, in particular their operational timescales. These results suggest that BF neurons slowly modulate downstream populations whereas cortical circuits transmit signals on multiple timescales. Thus, the characterization of the neural ensemble dynamics in the BF provides further insight into the neural mechanisms, by which brain states are regulated.

Midbrain dopaminergic neuron activity across alternating brain states of urethane anaesthetized rat

Midbrain dopaminergic neurons are implicated in the control of motor functions and reward-driven behaviours. The function of this neuronal population is strongly connected with distinct patterns of firing - irregular or bursting, which either maintains basal levels of dopamine (DA) or leads to phasic release, respectively. Heterogeneity of dopaminergic neurons, observed on both structural and functional levels, is also reflected in different responses of DA neurons to changes in global brain states. Preparation of urethane anaesthetized animal is a broadly used model to study brain state dependent activity of neurons. Unfortunately activity of midbrain DA neurons across urethane induced cyclic, spontaneous brain state alternations is poorly described. To fulfil this gap in our knowledge we have performed simultaneous, extracellular recordings of the firing of single putative DA neurons combined with continuous brain state monitoring. We found that during slow wave activity, the firing rate of recorded putative DA neurons was significantly higher compared to firing rates during activated state, both in ventral tegmental area (VTA) and substantia nigra pars compacta (SNc). In the presence of cortical slow waves, putative dopaminergic neurons also intensified bursting activity, but the magnitude of this phenomena differed in respect to the examined region (VTA or SNc). Our results show that activity of DA neurons under urethane anaesthesia is brain-state dependent and emphasize the importance of brain state monitoring during electrophysiological experiments.

Inhibitory control of correlated intrinsic variability in cortical networks

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

DOI:http://dx.doi.org/10.7554/eLife.19695.001

Our brains contain billions of neurons, which are continually producing electrical signals to relay information around the brain. Yet most of our knowledge of how the brain works comes from studying the activity of one neuron at a time. Recently, studies of multiple neurons have shown that they tend to be active together in short bursts called “up” states, which are followed by periods in which they are less active called “down” states. When we are sleeping or under a general anesthetic, the neurons may be completely silent during down states, but when we are awake the difference in activity between the two states is usually less extreme. However, it is still not clear how the neurons generate these patterns of activity.

To address this question, Stringer et al. studied the activity of neurons in the brains of awake and anesthetized rats, mice and gerbils. The experiments recorded electrical activity from many neurons at the same time and found a wide range of different activity patterns. A computational model based on these data suggests that differences in the degree to which some neurons suppress the activity of other neurons may account for this variety. Increasing the strength of these inhibitory signals in the model decreased the fluctuations in electrical activity across entire areas of the brain. Further analysis of the experimental data supported the model’s predictions by showing that inhibitory neurons – which act to reduce electrical activity in other neurons – were more active when there were fewer fluctuations in activity across the brain.

The next step following on from this work would be to develop ways to build computer models that can mimic the activity of many more neurons at the same time. The models could then be used to interpret the electrical activity produced by many different kinds of neuron. This will enable researchers to test more sophisticated hypotheses about how the brain works.

DOI:http://dx.doi.org/10.7554/eLife.19695.002

Depth-specific optogenetic control in vivo with a scalable, high-density μLED neural probe

Controlling neural circuits is a powerful approach to uncover a causal link between neural activity and behaviour. Optogenetics has been widely adopted by the neuroscience community as it offers cell-type-specific perturbation with millisecond precision. However, these studies require light delivery in complex patterns with cellular-scale resolution, while covering a large volume of tissue at depth in vivo. Here we describe a novel high-density silicon-based microscale light-emitting diode (μLED) array, consisting of up to ninety-six 25 μm-diameter μLEDs emitting at a wavelength of 450 nm with a peak irradiance of 400 mW/mm². A width of 100 μm, tapering to a 1 μm point, and a 40 μm thickness help minimise tissue damage during insertion. Thermal properties permit a set of optogenetic operating regimes, with ~0.5 °C average temperature increase. We demonstrate depth-dependent activation of mouse neocortical neurons in vivo, offering an inexpensive novel tool for the precise manipulation of neural activity.

Translaminar Cortical Membrane Potential Synchrony in Behaving Mice

The synchronized activity of six layers of cortical neurons is critical for sensory perception and the control of voluntary behavior, but little is known about the synaptic mechanisms of cortical synchrony across layers in behaving animals. We made single and dual whole-cell recordings from the primary somatosensory forepaw cortex in awake mice and show that L2/3 and L5 excitatory neurons have layer-specific intrinsic properties and membrane potential dynamics that shape laminar-specific firing rates and subthreshold synchrony. First, while sensory and movement-evoked synaptic input was tightly correlated across layers, spontaneous action potentials and slow spontaneous subthreshold fluctuations had laminar-specific timing; second, longer duration forepaw movement was associated with a decorrelation of subthreshold activity; third, spontaneous and sensory-evoked forepaw movements were signaled more strongly by L5 than L2/3 neurons. Together, our data suggest that the degree of translaminar synchrony is dependent upon the origin (sensory, spontaneous, and movement) of the synaptic input.

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We made dual whole-cell recordings from L2/3 and L5 cortical neurons in behaving mice

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Layer-specific membrane properties determine higher mean firing rates of L5 neurons

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Synchrony of translaminar synaptic activity is determined by the origin of input

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L5 neurons signal spontaneous and sensory-triggered movements

Correlation between Cortical State and Locus Coeruleus Activity: Implications for Sensory Coding in Rat Barrel Cortex

Cortical state modulates the background activity of cortical neurons, and their evoked response to sensory stimulation. Multiple mechanisms are involved in switching between cortical states including various neuromodulatory systems. Locus Coeruleus (LC) is one of the major neuromodulatory nuclei in the brainstem with widespread projections throughout the brain and modulates the activity of cells and networks. Here, we quantified the link between the LC spontaneous activity, cortical state and sensory processing in the rat vibrissal somatosensory "barrel" cortex (BC). We simultaneously recorded unit activity from LC and BC along with prefrontal electroencephalogram (EEG) while presenting brief whisker deflections under urethane anesthesia. The ratio of low to high frequency components of EEG (referred to as the L/H ratio) was employed to identify cortical state. We found that the spontaneous activity of LC units exhibited a negative correlation with the L/H ratio. Cross-correlation analysis revealed that changes in LC firing preceded changes in the cortical state: the correlation of the LC firing profile with the L/H ratio was maximal at an average lag of -1.2 s. We further quantified BC neuronal responses to whisker stimulation during the synchronized and desynchronized states. In the desynchronized state, BC neurons showed lower stimulus detection threshold, higher response fidelity, and shorter response latency. The most prominent change was observed in the late phase of BC evoked activity (100-400 ms post stimulus onset): almost every BC unit exhibited a greater late response during the desynchronized state. Categorization of the BC evoked responses based on LC activity (into high and low LC discharge rates) resulted in highly similar response profiles compared to categorization based on the cortical state (low and high L/H ratios). These findings provide evidence for the involvement of the LC neuromodulatory system in desynchronization of cortical state and the consequent enhancement of sensory coding efficiency.

Membrane Potential Dynamics of Spontaneous and Visually Evoked Gamma Activity in V1 of Awake Mice

Cortical gamma activity (30–80 Hz) is believed to play important functions in neural computation and arises from the interplay of parvalbumin-expressing interneurons (PV) and pyramidal cells (PYRs). However, the subthreshold dynamics underlying its emergence in the cortex of awake animals remain unclear. Here, we characterized the intracellular dynamics of PVs and PYRs during spontaneous and visually evoked gamma activity in layers 2/3 of V1 of awake mice using targeted patch-clamp recordings and synchronous local field potentials (LFPs). Strong gamma activity patterned in short bouts (one to three cycles), occurred when PVs and PYRs were depolarizing and entrained their membrane potential dynamics regardless of the presence of visual stimulation. PV firing phase locked unconditionally to gamma activity. However, PYRs only phase locked to visually evoked gamma bouts. Taken together, our results indicate that gamma activity corresponds to short pulses of correlated background synaptic activity synchronizing the output of cortical neurons depending on external sensory drive.

Gamma activity, an important component of brain dynamics, is driven by synaptic background activity and synchronizes distinct cortical cell types differently depending on visual input.

The neocortex is the main substrate of cognitive activity of the mammalian brain. During active wakefulness, it exhibits an oscillatory activity in the gamma range (30–80Hz), which is believed to play an important functional role and is altered in schizophrenic patients. Experimental studies have shown that gamma activity arises from the interaction of excitatory pyramidal neurons, the main neuronal type of the cortex, and local inhibitory neurons expressing the protein parvalbumin (PV). However, how these neuronal types behave during gamma activity remains largely unknown. Here, we recorded the intracellular activity of pyramidal and PV-expressing neurons in the visual cortex of awake mice while acquiring Local Field Potentials (LFPs)—extracellular voltage fluctuations within a small volume of the cortex—to monitor gamma activity. We found that gamma activity arises when PV-expressing neurons synchronize their output in response to a correlated input, reflecting the general activation of the local cortical network. This happens even in the absence of visual input. On the other hand, the output of pyramidal neurons only becomes entrained to gamma activity when the mice are exposed to visual stimulation. Thus, our results suggest that gamma activity synchronizes pyramidal neurons specifically when the cortex is engaged in processing external inputs.

Synaptic refinement during development and its effect on slow-wave activity: a computational study

Recent evidence suggests that synaptic refinement, the reorganization of synapses and connections without significant change in their number or strength, is important for the development of the visual system of juvenile rodents. Other evidence in rodents and humans shows that there is a marked drop in sleep slow-wave activity (SWA) during adolescence. Slow waves reflect synchronous transitions of neuronal populations between active and inactive states, and the amount of SWA is influenced by the connection strength and organization of cortical neurons. In this study, we investigated whether synaptic refinement could account for the observed developmental drop in SWA. To this end, we employed a large-scale neural model of primary visual cortex and sections of the thalamus, capable of producing realistic slow waves. In this model, we reorganized intralaminar connections according to experimental data on synaptic refinement: during prerefinement, local connections between neurons were homogenous, whereas in postrefinement, neurons connected preferentially to neurons with similar receptive fields and preferred orientations. Synaptic refinement led to a drop in SWA and to changes in slow-wave morphology, consistent with experimental data. To test whether learning can induce synaptic refinement, intralaminar connections were equipped with spike timing-dependent plasticity. Oriented stimuli were presented during a learning period, followed by homeostatic synaptic renormalization. This led to activity-dependent refinement accompanied again by a decline in SWA. Together, these modeling results show that synaptic refinement can account for developmental changes in SWA. Thus sleep SWA may be used to track noninvasively the reorganization of cortical connections during development.

Local Slow Waves in Superficial Layers of Primary Cortical Areas during REM Sleep

Sleep is traditionally constituted of two global behavioral states, non-rapid eye movement (NREM) and rapid eye movement (REM), characterized by quiescence and reduced responsiveness to sensory stimuli [1]. NREM sleep is distinguished by slow waves and spindles throughout the cerebral cortex and REM sleep by an "activated," low-voltage fast electroencephalogram (EEG) paradoxically similar to that of wake, accompanied by rapid eye movements and muscle atonia. However, recent evidence has shown that cortical activity patterns during wake and NREM sleep are not as global as previously thought. Local slow waves can appear in various cortical regions in both awake humans [2] and rodents [3-5]. Intracranial recordings in humans [6] and rodents [4, 7] have shown that NREM sleep slow waves most often involve only a subset of brain regions that varies from wave to wave rather than occurring near synchronously across all cortical areas. Moreover, some cortical areas can transiently "wake up" [8] in an otherwise sleeping brain. Yet until now, cortical activity during REM sleep was thought to be homogenously wake-like. We show here, using local laminar recordings in freely moving mice, that slow waves occur regularly during REM sleep, but only in primary sensory and motor areas and mostly in layer 4, the main target of relay thalamic inputs, and layer 3. This finding may help explain why, during REM sleep, we remain disconnected from the environment even though the bulk of the cortex shows wake-like, paradoxical activation.

State-dependent and cell type-specific temporal processing in auditory thalamocortical circuit

Ongoing spontaneous activity in cortical circuits defines cortical states, but it still remains unclear how cortical states shape sensory processing across cortical laminae and what type of response properties emerge in the cortex. Recording neural activity from the auditory cortex (AC) and medial geniculate body (MGB) simultaneously with electrical stimulations of the basal forebrain (BF) in urethane-anesthetized rats, we investigated state-dependent spontaneous and auditory-evoked activities in the auditory thalamocortical circuit. BF stimulation induced a short-lasting desynchronized state, with sparser firing and increased power at gamma frequency in superficial layers. In this desynchronized state, the reduction in onset response variability in both AC and MGB was accompanied by cell type-specific firing, with decreased responses of cortical broad spiking cells, but increased responses of cortical narrow spiking cells. This onset response was followed by distinct temporal evolution in AC, with quicker rebound firing in infragranular layers. This temporal profile was associated with improved processing of temporally structured stimuli across AC layers to varying degrees, but not in MGB. Thus, the reduction in response variability during the desynchronized state can be seen subcortically whereas the improvement of temporal tuning emerges across AC layers, emphasizing the importance of state-dependent intracortical processing in hearing.

Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

Gamma oscillations are a robust component of sensory responses but are also part of the background spontaneous activity of the brain. To determine whether the properties of gamma oscillations in cortex are specific to their mechanism of generation, we compared in mouse visual cortex in vivo the laminar geometry and single-neuron rhythmicity of oscillations produced during sensory representation with those occurring spontaneously in the absence of stimulation. In mouse visual cortex under anesthesia (isoflurane and xylazine), visual stimulation triggered oscillations mainly between 20 and 50 Hz, which, because of their similar functional significance to gamma oscillations in higher mammals, we define here as gamma range. Sensory representation in visual cortex specifically increased gamma oscillation amplitude in the supragranular (L2/3) and granular (L4) layers and strongly entrained putative excitatory and inhibitory neurons in infragranular layers, while spontaneous gamma oscillations were distributed evenly through the cortical depth and primarily entrained putative inhibitory neurons in the infragranular (L5/6) cortical layers. The difference in laminar distribution of gamma oscillations during the two different conditions may result from differences in the source of excitatory input to the cortex. In addition, modulation of superficial gamma oscillation amplitude did not result in a corresponding change in deep-layer oscillations, suggesting that superficial and deep layers of cortex may utilize independent but related networks for gamma generation. These results demonstrate that stimulus-driven gamma oscillations engage cortical circuitry in a manner distinct from spontaneous oscillations and suggest multiple networks for the generation of gamma oscillations in cortex.

Temporal Structure of Neuronal Activity among Cortical Neuron Subtypes during Slow Oscillations in Anesthetized Rats

Slow-wave oscillations, the predominant brain rhythm during sleep, are composed of Up/Down cycles. Depolarizing Up-states involve activity in layer 5 (L5) of the neocortex, but it is unknown how diverse subtypes of neurons within L5 participate in generating and maintaining Up-states. Here we compare the in vivo firing patterns of corticopontine (CPn) pyramidal cells, crossed-corticostriatal (CCS) pyramidal cells, and fast-spiking (FS) GABAergic neurons in the rat frontal cortex, with those of thalamocortical neurons during Up/Down cycles in the anesthetized condition. During the transition from Down- to Up-states, increased activity in these neurons was highly temporally structured, with spiking occurring first in thalamocortical neurons, followed by cortical FS cells, CCS cells, and, finally, CPn cells. Activity in some FS, CCS, and CPn neurons occurred in phase with Up-nested gamma rhythms, with FS neurons showing phase delay relative to pyramidal neurons. These results suggest that thalamic and cortical pyramidal neurons are activated in a specific temporal sequence during Up/Down cycles, but cortical pyramidal cells are activated at a similar gamma phase. In addition to Up-state firing specificity, CCS and CPn cells exhibited differences in activity during cortical desynchronization, further indicating projection- and state-dependent information processing within L5.

SIGNIFICANCE STATEMENT

Patterned activity in neocortical electroencephalograms, including slow waves and gamma oscillations, is thought to reflect the organized activity of neocortical neurons that comprises many specialized neuron subtypes. We found that the timing of action potentials during slow waves in individual cortical neurons was correlated with their laminar positions and axonal targets. Within gamma cycles nested in the slow-wave depolarization, cortical pyramidal cells fired earlier than did interneurons. At the start of slow-wave depolarizations, activity in thalamic neurons receiving inhibition from the basal ganglia occurred earlier than activity in cortical neurons. Together, these findings reveal a temporally ordered pattern of output from diverse neuron subtypes in the frontal cortex and related thalamic nuclei during neocortical oscillations.

Formation and disruption of tonotopy in a large-scale model of the auditory cortex

There is ample experimental evidence describing changes of tonotopic organisation in the auditory cortex due to environmental factors. In order to uncover the underlying mechanisms, we designed a large-scale computational model of the auditory cortex. The model has up to 100 000 Izhikevich's spiking neurons of 17 different types, almost 21 million synapses, which are evolved according to Spike-Timing-Dependent Plasticity (STDP) and have an architecture akin to existing observations. Validation of the model revealed alternating synchronised/desynchronised states and different modes of oscillatory activity. We provide insight into these phenomena via analysing the activity of neuronal subtypes and testing different causal interventions into the simulation. Our model is able to produce experimental predictions on a cell type basis. To study the influence of environmental factors on the tonotopy, different types of auditory stimulations during the evolution of the network were modelled and compared. We found that strong white noise resulted in completely disrupted tonotopy, which is consistent with in vivo experimental observations. Stimulation with pure tones or spontaneous activity led to a similar degree of tonotopy as in the initial state of the network. Interestingly, weak white noise led to a substantial increase in tonotopy. As the STDP was the only mechanism of plasticity in our model, our results suggest that STDP is a sufficient condition for the emergence and disruption of tonotopy under various types of stimuli. The presented large-scale model of the auditory cortex and the core simulator, SUSNOIMAC, have been made publicly available.

Rhythmic auditory cortex activity at multiple timescales shapes stimulus-response gain and background firing

The phase of low-frequency network activity in the auditory cortex captures changes in neural excitability, entrains to the temporal structure of natural sounds, and correlates with the perceptual performance in acoustic tasks. Although these observations suggest a causal link between network rhythms and perception, it remains unknown how precisely they affect the processes by which neural populations encode sounds. We addressed this question by analyzing neural responses in the auditory cortex of anesthetized rats using stimulus-response models. These models included a parametric dependence on the phase of local field potential rhythms in both stimulus-unrelated background activity and the stimulus-response transfer function. We found that phase-dependent models better reproduced the observed responses than static models, during both stimulation with a series of natural sounds and epochs of silence. This was attributable to two factors: (1) phase-dependent variations in background firing (most prominent for delta; 1-4 Hz); and (2) modulations of response gain that rhythmically amplify and attenuate the responses at specific phases of the rhythm (prominent for frequencies between 2 and 12 Hz). These results provide a quantitative characterization of how slow auditory cortical rhythms shape sound encoding and suggest a differential contribution of network activity at different timescales. In addition, they highlight a putative mechanism that may implement the selective amplification of appropriately timed sound tokens relative to the phase of rhythmic auditory cortex activity.

High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons

The manner in which populations of inhibitory (INH) and excitatory (EXC) neocortical neurons collectively encode stimulus-related information is a fundamental, yet still unresolved question. Here we address this question by simultaneously recording with large-scale multi-electrode arrays (of up to 128 channels) the activity of cell ensembles (of up to 74 neurons) distributed along all layers of 3–4 neighboring cortical columns in the anesthetized adult rat somatosensory barrel cortex in vivo. Using two different whisker stimulus modalities (location and frequency) we show that individual INH neurons – classified as such according to their distinct extracellular spike waveforms – discriminate better between restricted sets of stimuli (≤6 stimulus classes) than EXC neurons in granular and infra-granular layers. We also demonstrate that ensembles of INH cells jointly provide as much information about such stimuli as comparable ensembles containing the ~20% most informative EXC neurons, however presenting less information redundancy – a result which was consistent when applying both theoretical information measurements and linear discriminant analysis classifiers. These results suggest that a consortium of INH neurons dominates the information conveyed to the neocortical network, thereby efficiently processing incoming sensory activity. This conclusion extends our view on the role of the inhibitory system to orchestrate cortical activity.

Perception of the environment relies on neuronal computation in the cerebral cortex. However, the exact algorithms by which cortical neuronal networks process relevant information from the inputs of sensory organs are only poorly understood. To address this problem we stimulated distinct whiskers and recorded the neuronal responses from identified cortical whisker representations of the rat using multi-site electrodes. For rodents the whisker system is one main sensory input channel, offering the unique property that for each whisker an identified cortical area ("barrel-related column") represents its main cortical input station. In the present study we were able to demonstrate that the action potential firing of single inhibitory neurons provides more information about behaviorally relevant qualities of whisker stimulation (identity of the stimulated whisker and frequency of stimulation) than excitatory neurons. In addition, information about stimulation qualities was encoded with less redundancy in inhibitory neurons. In summary, the results of our study suggest that inhibitory neurons carry substantial information about the sensory environment and can thereby adequately orchestrate neuronal activity in sensory cortices.

Optogenetic activation of neocortical neurons in vivo with a sapphire-based micro-scale LED probe

Optogenetics has proven to be a revolutionary technology in neuroscience and has advanced continuously over the past decade. However, optical stimulation technologies for in vivo need to be developed to match the advances in genetics and biochemistry that have driven this field. In particular, conventional approaches for in vivo optical illumination have a limitation on the achievable spatio-temporal resolution. Here we utilize a sapphire-based microscale gallium nitride light-emitting diode (μLED) probe to activate neocortical neurons in vivo. The probes were designed to contain independently controllable multiple μLEDs, emitting at 450 nm wavelength with an irradiance of up to 2 W/mm². Monte-Carlo stimulations predicted that optical stimulation using a μLED can modulate neural activity within a localized region. To validate this prediction, we tested this probe in the mouse neocortex that expressed channelrhodopsin-2 (ChR2) and compared the results with optical stimulation through a fiber at the cortical surface. We confirmed that both approaches reliably induced action potentials in cortical neurons and that the μLED probe evoked strong responses in deep neurons. Due to the possibility to integrate many optical stimulation sites onto a single shank, the μLED probe is thus a promising approach to control neurons locally in vivo.

Neural control of brain state

How the brain takes in information, makes a decision, and acts on this decision is strongly influenced by the ongoing and constant fluctuations of state. Understanding the nature of these brain states and how they are controlled is critical to making sense of how the nervous system operates, both normally and abnormally. While broadly projecting neuromodulatory systems acting through metabotropic pathways have long been appreciated to be critical for determining brain state, more recent investigations have revealed a prominent role for fast acting neurotransmitter pathways for temporally and spatially precise control of neural processing. Corticocortical and thalamocortical glutamatergic projections can rapidly and precisely control brain state by changing both the nature of ongoing activity and by controlling the gain and precision of neural responses.

Role of cortical neurodynamics for understanding the neural basis of motivated behavior - lessons from auditory category learning

Rhythmic activity appears in the auditory cortex in both microscopic and macroscopic observables and is modulated by both bottom-up and top-down processes. How this activity serves both types of processes is largely unknown. Here we review studies that have recently improved our understanding of potential functional roles of large-scale global dynamic activity patterns in auditory cortex. The experimental paradigm of auditory category learning allowed critical testing of the hypothesis that global auditory cortical activity states are associated with endogenous cognitive states mediating the meaning associated with an acoustic stimulus rather than with activity states that merely represent the stimulus for further processing.

Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce "packets" of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013). However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC) pathways sequentially activate cells in layers 4 (L4), L2/3, and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2-6, presumably via synapses onto dendritic processes located in L3 and L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a non-linear amplification process, are initiated by infragranular cells and tightly regulated by feed-forward inhibitory cells.

How local excitation-inhibition ratio impacts the whole brain dynamics

The spontaneous activity of the brain shows different features at different scales. On one hand, neuroimaging studies show that long-range correlations are highly structured in spatiotemporal patterns, known as resting-state networks, on the other hand, neurophysiological reports show that short-range correlations between neighboring neurons are low, despite a large amount of shared presynaptic inputs. Different dynamical mechanisms of local decorrelation have been proposed, among which is feedback inhibition. Here, we investigated the effect of locally regulating the feedback inhibition on the global dynamics of a large-scale brain model, in which the long-range connections are given by diffusion imaging data of human subjects. We used simulations and analytical methods to show that locally constraining the feedback inhibition to compensate for the excess of long-range excitatory connectivity, to preserve the asynchronous state, crucially changes the characteristics of the emergent resting and evoked activity. First, it significantly improves the model's prediction of the empirical human functional connectivity. Second, relaxing this constraint leads to an unrealistic network evoked activity, with systematic coactivation of cortical areas which are components of the default-mode network, whereas regulation of feedback inhibition prevents this. Finally, information theoretic analysis shows that regulation of the local feedback inhibition increases both the entropy and the Fisher information of the network evoked responses. Hence, it enhances the information capacity and the discrimination accuracy of the global network. In conclusion, the local excitation-inhibition ratio impacts the structure of the spontaneous activity and the information transmission at the large-scale brain level.

Local versus global scales of organization in auditory cortex

Topographic organization is a hallmark of sensory cortical organization. Topography is robust at spatial scales ranging from hundreds of microns to centimeters, but can dissolve at the level of neighboring neurons or subcellular compartments within a neuron. This dichotomous spatial organization is especially pronounced in the mouse auditory cortex, where an orderly tonotopic map can arise from heterogeneous frequency tuning between local neurons. Here, we address a debate surrounding the robustness of tonotopic organization in the auditory cortex that has persisted in some form for over 40 years. Drawing from various cortical areas, cortical layers, recording methodologies, and species, we describe how auditory cortical circuitry can simultaneously support a globally systematic, yet locally heterogeneous representation of this fundamental sound property.

Brain state-dependent abnormal LFP activity in the auditory cortex of a schizophrenia mouse model

In schizophrenia, evoked 40-Hz auditory steady-state responses (ASSRs) are impaired, which reflects the sensory deficits in this disorder, and baseline spontaneous oscillatory activity also appears to be abnormal. It has been debated whether the evoked ASSR impairments are due to the possible increase in baseline power. GABAergic interneuron-specific NMDA receptor (NMDAR) hypofunction mutant mice mimic some behavioral and pathophysiological aspects of schizophrenia. To determine the presence and extent of sensory deficits in these mutant mice, we recorded spontaneous local field potential (LFP) activity and its click-train evoked ASSRs from primary auditory cortex of awake, head-restrained mice. Baseline spontaneous LFP power in the pre-stimulus period before application of the first click trains was augmented at a wide range of frequencies. However, when repetitive ASSR stimuli were presented every 20 s, averaged spontaneous LFP power amplitudes during the inter-ASSR stimulus intervals in the mutant mice became indistinguishable from the levels of control mice. Nonetheless, the evoked 40-Hz ASSR power and their phase locking to click trains were robustly impaired in the mutants, although the evoked 20-Hz ASSRs were also somewhat diminished. These results suggested that NMDAR hypofunction in cortical GABAergic neurons confers two brain state-dependent LFP abnormalities in the auditory cortex; (1) a broadband increase in spontaneous LFP power in the absence of external inputs, and (2) a robust deficit in the evoked ASSR power and its phase-locking despite of normal baseline LFP power magnitude during the repetitive auditory stimuli. The “paradoxically” high spontaneous LFP activity of the primary auditory cortex in the absence of external stimuli may possibly contribute to the emergence of schizophrenia-related aberrant auditory perception.

Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex

Cortical sensory processing is modulated by behavioral and cognitive states. How the modulation is achieved through impacting synaptic circuits remains largely unknown. In awake mouse auditory cortex, we reported that sensory-evoked spike responses of layer 2/3 (L2/3) excitatory cells were scaled down with preserved sensory tuning when animals transitioned from quiescence to active behaviors, while L4 and thalamic responses were unchanged. Whole-cell voltage-clamp recordings further revealed that tone-evoked synaptic excitation and inhibition exhibited a robust functional balance. Changes of behavioral state caused scaling down of excitation and inhibition at an approximately equal level in L2/3 cells, but no synaptic changes in L4 cells. This laminar-specific gain control could be attributed to an enhancement of L1–mediated inhibitory tone, with L2/3 parvalbumin inhibitory neurons suppressed as well. Thus, L2/3 circuits can adjust the salience of output in accordance with momentary behavioral demands while maintaining the sensitivity and quality of sensory processing.

Laminar and Columnar Structure of Sensory-Evoked Multineuronal Spike Sequences in Adult Rat Barrel Cortex In Vivo

One of the most relevant questions regarding the function of the nervous system is how sensory information is represented in populations of cortical neurons. Despite its importance, the manner in which sensory-evoked activity propagates across neocortical layers and columns has yet not been fully characterized. In this study, we took advantage of the distinct organization of the rodent barrel cortex and recorded with multielectrode arrays simultaneously from up to 74 neurons localized in several functionally identified layers and columns of anesthetized adult Wistar rats in vivo. The flow of activity within neuronal populations was characterized by temporally precise spike sequences, which were repeatedly evoked by single-whisker stimulation. The majority of the spike sequences representing instantaneous responses were led by a subgroup of putative inhibitory neurons in the principal column at thalamo-recipient layers, thus revealing the presence of feedforward inhibition. However, later spike sequences were mainly led by infragranular excitatory neurons in neighboring columns. Although the starting point of the sequences was anatomically confined, their ending point was rather scattered, suggesting that the population responses are structurally dispersed. Our data show for the first time the simultaneous intra- and intercolumnar processing of information at high temporal resolution.

Subthreshold mechanisms underlying state-dependent modulation of visual responses

The processing of sensory information varies widely across behavioral states. However, little is known about how behavioral states modulate the intracellular activity of cortical neurons to effect changes in sensory responses. Here, we performed whole-cell recordings from neurons in upper-layer primary visual cortex of awake mice during locomotion and quiet wakefulness. We found that the signal-to-noise ratio for sensory responses was improved during locomotion by two mechanisms: (1) a decrease in membrane potential variability leading to a reduction in background firing rates and (2) an enhancement in the amplitude and reliability of visually evoked subthreshold responses mediated by an increase in total conductance and a depolarization of the stimulus-evoked reversal potential. Consistent with the enhanced signal-to-noise ratio for visual responses during locomotion, we demonstrate that performance is improved in a visual detection task during this behavioral state.

Cortical balance of excitation and inhibition is regulated by the rate of synaptic activity

Cortical activity is determined by the balance between excitation and inhibition. To examine how shifts in brain activity affect this balance, we recorded spontaneous excitatory and inhibitory synaptic inputs into layer 4 neurons from rat somatosensory cortex while altering the depth of anesthesia. The rate of excitatory and inhibitory events was reduced by ∼50% when anesthesia was deepened. However, whereas both the amplitude and width of inhibitory synaptic events profoundly increased under deep anesthesia, those of excitatory events were unaffected. These effects were found using three different types of anesthetics, suggesting that they are caused by the network state and not by local specific action of the anesthetics. To test our hypothesis that the size of inhibitory events increased because of the decreased rate of synaptic activity under deep anesthesia, we blocked cortical excitation and replayed the slow and fast patterns of inhibitory inputs using intracortical electrical stimulation. Evoked inhibition was larger under low-frequency stimulation, and, importantly, this change occurred regardless of the depth of anesthesia. Hence, shifts in the balance between excitation and inhibition across distinct states of cortical activity can be explained by the rate of inhibitory inputs combined with their short-term plasticity properties, regardless of the actual global brain activity.

Top-down control of cortical state

Sensory cortices receive inputs not only from thalamus but also from higher-order cortical regions. Here, Zagha et al. (2013) show that motor cortical inputs can switch barrel cortex into a desynchronized state that enables more faithful representation of subtle sensory stimuli.

Wave propagation of cortical population activity under urethane anesthesia is state dependent

BACKGROUND

Propagating waves of excitation have been observed extensively in the neocortex, during both spontaneous and sensory-evoked activity, and they play a critical role in spatially organizing information processing. However, the state-dependence of these spatiotemporal propagation patterns is largely unexplored. In this report, we use voltage-sensitive dye imaging in the rat visual cortex to study the propagation of spontaneous population activity in two discrete cortical states induced by urethane anesthesia.

RESULTS

While laminar current source density patterns of spontaneous population events in these two states indicate a considerable degree of similarity in laminar networks, lateral propagation in the more active desynchronized state is approximately 20% faster than in the slower synchronized state. Furthermore, trajectories of wave propagation exhibit a strong anisotropy, but the preferred direction is different depending on cortical state.

CONCLUSIONS

Our results show that horizontal wave propagation of spontaneous neural activity is largely dependent on the global activity states of local cortical circuits.