UP states protect ongoing cortical activity from thalamic inputs

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

The impact of the human thalamus on brain-wide information processing

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

Patterned activation of action potential patterns during offline states in the neocortex: replay and non-replay

Action potential generation (spiking) in the neocortex is organized into repeating non-random patterns during both awake experiential states and non-engaged states ranging from inattention to sleep to anaesthesia—and even occur in slice preparations. Repeating patterns in a given population of neurons between states may imply a common means by which cortical networks can be engaged despite brain state changes, but super-imposed on this common firing is a variability that is both specific to ongoing inputs and can be re-shaped by experience. This similarity with specifically induced variance may allow for a range of processes including perception, memory consolidation and network homeostasis. Here, we review how patterned activity in neocortical populations has been studied and what it may imply for a cortex that must be both static and plastic.

This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei

Cognitive activity emerges from large-scale neuronal dynamics that are constrained to a low-dimensional manifold. How this low-dimensional manifold scales with cognitive complexity, and which brain regions regulate this process, are not well understood. We addressed this issue by analyzing sub-second high-field fMRI data acquired during performance of a task that systematically varied the complexity of cognitive reasoning. We show that task performance reconfigures the low-dimensional manifold and that deviations from these patterns relate to performance errors. We further demonstrate that individual differences in thalamic activity relate to reconfigurations of the low-dimensional architecture during task engagement.

Sleep-wake control and the thalamus

Sleep is an essential component of animal behavior, controlled by both circadian and homeostatic processes. Typical brain oscillations for sleep and wake states are distinctive and reflect recurrent activity amongst neural circuits spanning localized to global brain regions. Since the original discovery of hypothalamic centers controlling both sleep and wakefulness, current views now implicate networks of neuronal and non-neuronal cells distributed brain-wide. Yet the mechanisms of sleep-wake control remain unclear. In light of recent studies, here we review experimental evidence from lesional, correlational, pharmacological and genetics studies, which support a role for the thalamus in several aspects of sleep-wake states. How these thalamo-cortical network mechanisms contribute to other executive functions such as memory consolidation and cognition, remains an open question with direct implications for neuro-psychiatric diseases and stands as a future challenge for basic science and healthcare research.

A novel mutual information estimator to measure spike train correlations in a model thalamocortical network

The impact of thalamic state on information transmission to the cortex remains poorly understood. This limitation exists due to the rich dynamics displayed by thalamocortical networks and because of inadequate tools to characterize those dynamics. Here, we introduce a novel estimator of mutual information and use it to determine the impact of a computational model of thalamic state on information transmission. Using several criteria, this novel estimator, which uses an adaptive partition, is shown to be superior to other mutual information estimators with uniform partitions when used to analyze simulated spike train data with different mean spike rates, as well as electrophysiological data from simultaneously recorded neurons. When applied to a thalamocortical model, the estimator revealed that thalamocortical cell T-type calcium current conductance influences mutual information between the input and output from this network. In particular, a T-type calcium current conductance of ~40 nS appears to produce maximal mutual information between the input to this network (conceptualized as afferent input to the thalamocortical cell) and the output of the network at the level of a layer 4 cortical neuron. Furthermore, at particular combinations of inputs to thalamocortical and thalamic reticular nucleus cells, thalamic cell bursting correlated strongly with recovery of mutual information between thalamic afferents and layer 4 neurons. These studies suggest that the novel mutual information estimator has advantages over previous estimators and that thalamic reticular nucleus activity can enhance mutual information between thalamic afferents and thalamorecipient cells in the cortex. NEW & NOTEWORTHY In this study, a novel mutual information estimator was developed to analyze information flow in a model thalamocortical network. Our findings suggest that this estimator is a suitable tool for signal transmission analysis, particularly in neural circuits with disparate firing rates, and that the thalamic reticular nucleus can potentiate ascending sensory signals, while thalamic recipient cells in the cortex can recover mutual information in ascending sensory signals that is lost due to thalamic bursting.

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain.

Sleep- and Wake-Like States in Small Networks In Vivo and In Vitro

Wakefulness and sleep are highly complex and heterogeneous processes, involving multiple neurotransmitter systems and a sophisticated interplay between global and local networks of neurons and non-neuronal cells. Macroscopic approaches applied at the level of the whole organism, view sleep as a global behaviour and allow for investigation into aspects such as the effects of insufficient or disrupted sleep on cognitive function, metabolism, thermoregulation and sensory processing. While significant progress has been achieved using such large-scale approaches, the inherent complexity of sleep-wake regulation has necessitated the development of methods which tackle specific aspects of sleep in isolation. One way this may be achieved is by investigating specific cellular or molecular phenomena in the whole organism in situ, either during spontaneous or induced sleep-wake states. This approach has greatly advanced our knowledge about the electrophysiology and pharmacology of ion channels, specific receptors, intracellular pathways and the small networks implicated in the control and regulation of the sleep-wake cycle. Importantly though, there are a variety of external and internal factors that influence global behavioural states which are difficult to control for using these approaches. For this reason, over the last few decades, ex vivo experimental models have become increasingly popular and have greatly advanced our understanding of many fundamental aspects of sleep, including the neuroanatomy and neurochemistry of sleep states, sleep regulation, the origin and dynamics of specific sleep oscillations, network homeostasis as well as the functional roles of sleep. This chapter will focus on the use of small neuronal networks as experimental models and will highlight the most significant and novel insights these approaches have provided.

An inhibitory gate for state transition in cortex

Large scale transitions between active (up) and silent (down) states during quiet wakefulness or NREM sleep regulate fundamental cortical functions and are known to involve both excitatory and inhibitory cells. However, if and how inhibition regulates these activity transitions is unclear. Using fluorescence-targeted electrophysiological recording and cell-specific optogenetic manipulation in both anesthetized and non-anesthetized mice, we found that two major classes of interneurons, the parvalbumin and the somatostatin positive cells, tightly control both up-to-down and down-to-up state transitions. Inhibitory regulation of state transition was observed under both natural and optogenetically-evoked conditions. Moreover, perturbative optogenetic experiments revealed that the inhibitory control of state transition was interneuron-type specific. Finally, local manipulation of small ensembles of interneurons affected cortical populations millimetres away from the modulated region. Together, these results demonstrate that inhibition potently gates transitions between cortical activity states, and reveal the cellular mechanisms by which local inhibitory microcircuits regulate state transitions at the mesoscale.

DOI:http://dx.doi.org/10.7554/eLife.26177.001

Imaging and Optically Manipulating Neuronal Ensembles

The neural code that relates the firing of neurons to the generation of behavior and mental states must be implemented by spatiotemporal patterns of activity across neuronal populations. These patterns engage selective groups of neurons, called neuronal ensembles, which are emergent building blocks of neural circuits. We review optical and computational methods, based on two-photon calcium imaging and two-photon optogenetics, to detect, characterize, and manipulate neuronal ensembles in three dimensions. We review data using these methods in the mammalian cortex that demonstrate the existence of neuronal ensembles in the spontaneous and evoked cortical activity in vitro and in vivo. Moreover, two-photon optogenetics enable the possibility of artificially imprinting neuronal ensembles into awake, behaving animals and of later recalling those ensembles selectively by stimulating individual cells. These methods could enable deciphering the neural code and also be used to understand the pathophysiology of and design novel therapies for neurological and mental diseases.

The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions

During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.

Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex

Understanding the development and differentiation of the neocortex remains a central focus of neuroscience. While previous studies have examined isolated aspects of cellular and synaptic organization, an integrated functional index of the cortical microcircuit is still lacking. Here we aimed to provide such an index, in the form of spontaneously recurring periods of persistent network activity -or Up states- recorded in mouse cortical slices. These coordinated network dynamics emerge through the orchestrated regulation of multiple cellular and synaptic elements and represent the default activity of the cortical microcircuit. To explore whether spontaneous Up states can capture developmental changes in intracortical networks we obtained local field potential recordings throughout the mouse lifespan. Two independent and complementary methodologies revealed that Up state activity is systematically modified by age, with the largest changes occurring during early development and adolescence. To explore possible regional heterogeneities we also compared the development of Up states in two distinct cortical areas and show that primary somatosensory cortex develops at a faster pace than primary motor cortex. Our findings suggest that in vitro Up states can serve as a functional index of cortical development and differentiation and can provide a baseline for comparing experimental and/or genetic mouse models.

Open-loop organization of thalamic reticular nucleus and dorsal thalamus: a computational model

The thalamic reticular nucleus (TRN) is a shell of GABAergic neurons that surrounds the dorsal thalamus. Previous work has shown that TRN neurons send GABAergic projections to thalamocortical (TC) cells to form reciprocal, closed-loop circuits. This has led to the hypothesis that the TRN is responsible for oscillatory phenomena, such as sleep spindles and absence seizures. However, there is emerging evidence that open-loop circuits are also found between TRN and TC cells. The implications of open-loop configurations are not yet known, particularly when they include time-dependent nonlinearities in TC cells such as low-threshold bursting. We hypothesized that low-threshold bursting in an open-loop circuit could be a mechanism by which the TRN could paradoxically enhance TC activation, and that enhancement would depend on the relative timing of TRN vs. TC cell stimulation. To test this, we modeled small circuits containing TC neurons, TRN neurons, and layer 4 thalamorecipient cells in both open- and closed-loop configurations. We found that open-loop TRN stimulation, rather than universally depressing TC activation, increased cortical output across a broad parameter space, modified the filter properties of TC neurons, and altered the mutual information between input and output in a frequency-dependent and T-type calcium channel-dependent manner. Therefore, an open-loop model of TRN-TC interactions, rather than suppressing transmission through the thalamus, creates a tunable filter whose properties may be modified by outside influences onto the TRN. These simulations make experimentally testable predictions about the potential role for the TRN for flexible enhancement of cortical activation.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation.

Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states.

Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods.

Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Decoding thalamic afferent input using microcircuit spiking activity

A behavioral response appropriate to a sensory stimulus depends on the collective activity of thousands of interconnected neurons. The majority of cortical connections arise from neighboring neurons, and thus understanding the cortical code requires characterizing information representation at the scale of the cortical microcircuit. Using two-photon calcium imaging, we densely sampled the thalamically evoked response of hundreds of neurons spanning multiple layers and columns in thalamocortical slices of mouse somatosensory cortex. We then used a biologically plausible decoder to characterize the representation of two distinct thalamic inputs, at the level of the microcircuit, to reveal those aspects of the activity pattern that are likely relevant to downstream neurons. Our data suggest a sparse code, distributed across lamina, in which a small population of cells carries stimulus-relevant information. Furthermore, we find that, within this subset of neurons, decoder performance improves when noise correlations are taken into account.

Preferential effect of isoflurane on top-down vs. bottom-up pathways in sensory cortex

The mechanism of loss of consciousness (LOC) under anesthesia is unknown. Because consciousness depends on activity in the cortico-thalamic network, anesthetic actions on this network are likely critical for LOC. Competing theories stress the importance of anesthetic actions on bottom-up “core” thalamo-cortical (TC) vs. top-down cortico-cortical (CC) and matrix TC connections. We tested these models using laminar recordings in rat auditory cortex in vivo and murine brain slices. We selectively activated bottom-up vs. top-down afferent pathways using sensory stimuli in vivo and electrical stimulation in brain slices, and compared effects of isoflurane on responses evoked via the two pathways. Auditory stimuli in vivo and core TC afferent stimulation in brain slices evoked short latency current sinks in middle layers, consistent with activation of core TC afferents. By contrast, visual stimuli in vivo and stimulation of CC and matrix TC afferents in brain slices evoked responses mainly in superficial and deep layers, consistent with projection patterns of top-down afferents that carry visual information to auditory cortex. Responses to auditory stimuli in vivo and core TC afferents in brain slices were significantly less affected by isoflurane compared to responses triggered by visual stimuli in vivo and CC/matrix TC afferents in slices. At a just-hypnotic dose in vivo, auditory responses were enhanced by isoflurane, whereas visual responses were dramatically reduced. At a comparable concentration in slices, isoflurane suppressed both core TC and CC/matrix TC responses, but the effect on the latter responses was far greater than on core TC responses, indicating that at least part of the differential effects observed in vivo were due to local actions of isoflurane in auditory cortex. These data support a model in which disruption of top-down connectivity contributes to anesthesia-induced LOC, and have implications for understanding the neural basis of consciousness.

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.

Circuit reactivation dynamically regulates synaptic plasticity in neocortex

Circuit reactivations involve a stereotyped sequence of neuronal firing and have been behaviourally linked to memory consolidation. Here we use multiphoton imaging and patch-clamp recording, and observe sparse and stereotyped circuit reactivations that correspond to UP states within active neurons. To evaluate the effect of the circuit on synaptic plasticity, we trigger a single spike-timing-dependent plasticity (STDP) pairing once per circuit reactivation. The pairings reliably fall within a particular epoch of the circuit sequence and result in long-term potentiation. During reactivation, the amplitude of plasticity significantly correlates with the preceding 20-25 ms of membrane depolarization rather than the depolarization at the time of pairing. This circuit-dependent plasticity provides a natural constraint on synaptic potentiation, regulating the inherent instability of STDP in an assembly phase-sequence model. Subthreshold voltage during endogenous circuit reactivations provides a critical informative context for plasticity and facilitates the stable consolidation of a spatiotemporal sequence.

Optogenetic patterning of whisker-barrel cortical system in transgenic rat expressing channelrhodopsin-2

The rodent whisker-barrel system has been an ideal model for studying somatosensory representations in the cortex. However, it remains a challenge to experimentally stimulate whiskers with a given pattern under spatiotemporal precision. Recently the optogenetic manipulation of neuronal activity has made possible the analysis of the neuronal network with precise spatiotemporal resolution. Here we identified the selective expression of channelrhodopsin-2 (ChR2), an algal light-driven cation channel, in the large mechanoreceptive neurons in the trigeminal ganglion (TG) as well as their peripheral nerve endings innervating the whisker follicles of a transgenic rat. The spatiotemporal pattern of whisker irradiation thus produced a barrel-cortical response with a specific spatiotemporal pattern as evidenced by electrophysiological and functional MRI (fMRI) studies. Our methods of generating an optogenetic tactile pattern (OTP) can be expected to facilitate studies on how the spatiotemporal pattern of touch is represented in the somatosensory cortex, as Hubel and Wiesel did in the visual cortex.

Decorrelating action of inhibition in neocortical networks

Inhibitory GABAergic interneurons have been extensively studied but their contribution to circuit dynamics remain poorly understood. Although it has been suggested that interneurons, especially those belonging to the same subclass, synchronize their activity and impart this synchrony onto their local network, recent theoretical and experimental work have challenged this view. To better understand the activity of interneurons during cortical activity, we combined molecular identification, two-photon imaging, and electrophysiological recordings in thalamocortical slices from mouse somatosensory cortex. Using calcium imaging to monitor cortical activity, we found low spiking correlations among parvalbumin or somatostatin interneurons during cortical UP states, indicating that interneurons do not synchronize their firing. Intracellular recordings confirmed that nearby interneurons do not display more synchronous spiking than excitatory cells. The lack of interneuron synchrony was also evident during slow oscillations, even among interneurons that were electrically coupled via gap junctions, suggesting that their coupling does not function to synchronize their activity. Using voltage-clamp recordings from nearby pyramidal cells, we found that inhibitory currents (IPSCs) are more correlated than excitatory ones, but that correlated IPSCs arise from the activation of common presynaptic inhibitory cells, rather than from synchronization of interneuron activity. Finally, we demonstrate that pharmacologically reducing inhibitory currents increases correlated excitatory activity. We conclude that inhibitory interneurons do not have synchronous activity during UP states, and that their function may be to decorrelate rather than to synchronize the firing of neurons within the local network.

Perceptual decision making "through the eyes" of a large-scale neural model of v1

Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper we investigate the complementary issue of sparse decoding, for example given activity generated by a realistic mapping of the visual scene to neuronal spike trains, how do downstream neurons best utilize this representation to generate a “decision.” Specifically we consider both sparse (L1-regularized) and non-sparse (L2 regularized) linear decoding for mapping the neural dynamics of a large-scale spiking neuron model of primary visual cortex (V1) to a two alternative forced choice (2-AFC) perceptual decision. We show that while both sparse and non-sparse linear decoding yield discrimination results quantitatively consistent with human psychophysics, sparse linear decoding is more efficient in terms of the number of selected informative dimension.

Chronic electrical stimulation homeostatically decreases spontaneous activity, but paradoxically increases evoked network activity

Neural dynamics generated within cortical networks play a fundamental role in brain function. However, the learning rules that allow recurrent networks to generate functional dynamic regimes, and the degree to which these regimes are themselves plastic, are not known. In this study we examined plasticity of network dynamics in cortical organotypic slices in response to chronic changes in activity. Studies have typically manipulated network activity pharmacologically; we used chronic electrical stimulation to increase activity in in vitro cortical circuits in a more physiological manner. Slices were stimulated with "implanted" electrodes for 4 days. Chronic electrical stimulation or treatment with bicuculline decreased spontaneous activity as predicted by homeostatic learning rules. Paradoxically, however, whereas bicuculline decreased evoked network activity, chronic stimulation actually increased the likelihood that evoked stimulation elicited polysynaptic activity, despite a decrease in evoked monosynaptic strength. Furthermore, there was an inverse correlation between spontaneous and evoked activity, suggesting a homeostatic tradeoff between spontaneous and evoked activity. Within-slice experiments revealed that cells close to the stimulated electrode exhibited more evoked polysynaptic activity and less spontaneous activity than cells close to a control electrode. Collectively, our results establish that chronic stimulation changes the dynamic regimes of networks. In vitro studies of homeostatic plasticity typically lack any external input, and thus neurons must rely on "spontaneous" activity to reach homeostatic "set points." However, in the presence of external input we propose that homeostatic learning rules seem to shift networks from spontaneous to evoked regimes.

Dissociated mean and functional connectivity BOLD signals in visual cortex during eyes closed and fixation

We investigated the effects of resting state type on blood oxygen level-dependent (BOLD) signal and functional connectivity in two paradigms: participants either alternated between fixation and eyes closed or maintained fixation or eyes closed throughout each scan. The BOLD signal and functional connectivity of lower and higher tiers of the visual cortical hierarchy were found to be differentially modulated during eyes closed versus fixation. Fixation was associated with greater mean BOLD signals in primary visual cortex and lower mean BOLD signals in extrastriate visual areas than periods of eyes closed. In addition, analysis of thalamocortical functional connectivity during scans in which participants maintained fixation showed synchronized BOLD fluctuations between those thalamic nuclei whose mean BOLD signal was systematically modulated during alternating epochs of eyes closed and fixation, primary visual cortex and the attention network, while during eyes closed negatively correlated fluctuations were seen between the same thalamic nuclei and extrastriate visual areas. Finally, in all visual areas the amplitude of spontaneous BOLD fluctuations was greater during eyes closed than during fixation. The dissociation between early and late tiers of visual cortex, which characterizes both mean and functionally connected components of the BOLD signal, may depend on the reorganization of thalamocortical networks. Since dissociated changes in local blood flow also characterize transitions between different stages of sleep and wakefulness (Braun AR, Balkin TJ, Wesenten NJ, Gwadry F, Carson RE, Varga M, Baldwin P, Belenky G, Herscovitch P. Science 279: 91-95, 1998), our results suggest that dissociated endogenous neural activity in primary and extrastriate cortex may represent a general aspect of brain function.

Populations of striatal medium spiny neurons encode vibrotactile frequency in rats: modulation by slow wave oscillations

Dorsolateral striatum (DLS) is implicated in tactile perception and receives strong projections from somatosensory cortex. However, the sensory representations encoded by striatal projection neurons are not well understood. Here we characterized the contribution of DLS to the encoding of vibrotactile information in rats by assessing striatal responses to precise frequency stimuli delivered to a single vibrissa. We applied stimuli in a frequency range (45-90 Hz) that evokes discriminable percepts and carries most of the power of vibrissa vibration elicited by a range of complex fine textures. Both medium spiny neurons and evoked potentials showed tactile responses that were modulated by slow wave oscillations. Furthermore, medium spiny neuron population responses represented stimulus frequency on par with previously reported behavioral benchmarks. Our results suggest that striatum encodes frequency information of vibrotactile stimuli which is dynamically modulated by ongoing brain state.

Peripheral facial nerve lesions induce changes in the firing properties of primary motor cortex layer 5 pyramidal cells

Facial nerve lesions elicit long-lasting changes in vibrissal primary motor cortex (M1) muscular representation in rodents. Reorganization of cortical representation has been attributed to potentiation of preexisting horizontal connections coming from neighboring muscle representation. However, changes in layer 5 pyramidal neuron activity induced by facial nerve lesion have not yet been explored. To do so, the effect of irreversible facial nerve injury on electrophysiological properties of layer 5 pyramidal neurons was characterized. Twenty-four adult male Wistar rats were randomly subjected to two experimental treatments: either surgical transection of mandibular and buccal branches of the facial nerve (n=18) or sham surgery (n=6). Unitary and population activity of vibrissal M1 layer 5 pyramidal neurons recorded in vivo under general anesthesia was compared between sham-operated and facial nerve-injured animals. Injured animals were allowed either one (n=6), three (n=6), or five (n=6) weeks recovery before recording in order to characterize the evolution of changes in electrophysiological activity. As compared to control, facial nerve-injured animals displayed the following sustained and significant changes in spontaneous activity: increased basal firing frequency, decreased spike-associated local field oscillation amplitude, and decreased spontaneous theta burst firing frequency. Significant changes in evoked-activity with whisker pad stimulation included: increased short latency population spike amplitude, decreased long latency population oscillations amplitude and frequency, and decreased peak frequency during evoked single-unit burst firing. Taken together, such changes demonstrate that peripheral facial nerve lesions induce robust and sustained changes of layer 5 pyramidal neurons in vibrissal motor cortex.

Default activity patterns at the neocortical microcircuit level

Even in absence of sensory stimuli cortical networks exhibit complex, self-organized activity patterns. While the function of those spontaneous patterns of activation remains poorly understood, recent studies both in vivo and in vitro have demonstrated that neocortical neurons activate in a surprisingly similar sequential order both spontaneously and following input into cortex. For example, neurons that tend to fire earlier within spontaneous bursts of activity also fire earlier than other neurons in response to sensory stimuli. These “default patterns” can last hundreds of milliseconds and are strongly conserved under a variety of conditions. In this paper, we will review recent evidence for these default patterns at the local cortical level. We speculate that cortical architecture imposes common constraints on spontaneous and evoked activity flow, which result in the similarity of the patterns.

Network activity and spike discharge oscillations in cortical slice cultures from neonatal rat

Network bursts and oscillations are forms of spontaneous activity in cortical circuits that have been described in vivo and in vitro. Searching for mechanisms involved in their generation, we investigated the collective network activity and spike discharge oscillations in cortical slice cultures of neonatal rats, combining multielectrode arrays with patch clamp recordings from individual neurons. The majority of these cultures showed spontaneous collective network activity [population bursts (PBs)] that could be described as neuronal avalanches. The largest of these PBs were followed by fast spike discharge oscillations in the beta to theta range, and sometimes additional repetitive PBs, together forming seizure-like episodes. During such episodes, all neurons showed sustained depolarization with increased spike rates. However, whereas regular-spiking (RS) and fast-spiking (FS) neurons fired during the PBs, only the FS neurons fired during the fast oscillations. Blockade of N-methyl-d-aspartate receptors reduced the depolarization and suppressed both the increased FS neuron firing and the oscillations. To investigate the generation of PBs, we studied the network responses to electrical stimulation. For most of the stimulation sites, the relationship between the stimulated inputs and the evoked PBs was linear. From a few stimulation sites, however, large PBs could be evoked with small inputs, indicating the activation of hub circuits. Taken together, our findings suggests that the oscillations originate from recurrent inhibition in local networks of depolarized inhibitory FS interneurons, whereas the PBs originate from recurrent excitation in networks of RS and FS neurons that is initiated in hub circuits.

Effects and mechanisms of wakefulness on local cortical networks

Mammalian brains generate internal activity independent of environmental stimuli. Internally generated states may bring about distinct cortical processing modes. To investigate how brain state impacts cortical circuitry, we recorded intracellularly from the same neurons, under anesthesia and subsequent wakefulness, in rat barrel cortex. In every cell examined throughout layers 2-6, wakefulness produced a temporal pattern of synaptic inputs differing markedly from those under anesthesia. Recurring periods of synaptic quiescence, prominent under anesthesia, were abolished by wakefulness, which produced instead a persistently depolarized state. This switch in dynamics was unaffected by elimination of afferent synaptic input from thalamus, suggesting that arousal alters cortical dynamics by neuromodulators acting directly on cortex. Indeed, blockade of noradrenergic, but not cholinergic, pathways induced synaptic quiescence during wakefulness. We conclude that global brain states can switch local recurrent networks into different regimes via direct neuromodulation.

Heuristically optimal path scanning for high-speed multiphoton circuit imaging

Population dynamics of patterned neuronal firing are fundamental to information processing in the brain. Multiphoton microscopy in combination with calcium indicator dyes allows circuit dynamics to be imaged with single-neuron resolution. However, the temporal resolution of fluorescent measures is constrained by the imaging frequency imposed by standard raster scanning techniques. As a result, traditional raster scans limit the ability to detect the relative timing of action potentials in the imaged neuronal population. To maximize the speed of fluorescence measures from large populations of neurons using a standard multiphoton laser scanning microscope (MPLSM) setup, we have developed heuristically optimal path scanning (HOPS). HOPS optimizes the laser travel path length, and thus the temporal resolution of neuronal fluorescent measures, using standard galvanometer scan mirrors. Minimizing the scan path alone is insufficient for prolonged high-speed imaging of neuronal populations. Path stability and the signal-to-noise ratio become increasingly important factors as scan rates increase. HOPS addresses this by characterizing the scan mirror galvanometers to achieve prolonged path stability. In addition, the neuronal dwell time is optimized to sharpen the detection of action potentials while maximizing scan rate. The combination of shortest path calculation and minimization of mirror positioning time allows us to optically monitor a population of neurons in a field of view at high rates with single-spike resolution, ∼ 125 Hz for 50 neurons and ∼ 8.5 Hz for 1,000 neurons. Our approach introduces an accessible method for rapid imaging of large neuronal populations using traditional MPLSMs, facilitating new insights into neuronal circuit dynamics.

Effects of acoustic stimuli on neuronal activity in the auditory cortex of the rat

Spontaneous activity of cortical neurons exhibits alternative fluctuations of membrane potential consisting of phased depolarization called "up-state" and persistent hyperpolarization called "down-state" during slow wave sleep and anesthesia. Here, we examined the effects of sound stimuli (noise bursts) on neuronal activity by intracellular recording in vivo from the rat auditory cortex (AC). Noise bursts increased the average time in the up-state by 0.81+/-0.65 s (range, 0.27-1.74 s) related to a 10 s recording duration. The rise times of the spontaneous up-events averaged 69.41+/-18.04 ms (range, 40.10-119.21 ms), while those of the sound-evoked up-events were significantly shorter (p<0.001) averaging only 22.54+/-8.81 ms (range, 9.31-45.74 ms). Sound stimulation did not influence ongoing spontaneous up-events. Our data suggest that a sound stimulus does not interfere with ongoing spontaneous neuronal activity in auditory cortex but can evoke new depolarizations in addition to the spontaneous ones.

Improved stimulus representation by short interspike intervals in primary auditory cortex

We analyzed the receptive field information conveyed by interspike intervals (ISIs) in the auditory cortex. In the visual system, different ISIs may both code for different visual features and convey differing amounts of stimulus information. To determine their potential role in auditory signal processing, we obtained extracellular recordings in the primary auditory cortex (AI) of the cat while presenting a dynamic moving ripple stimulus and then used the responses to construct spectrotemporal receptive fields (STRFs). For each neuron, we constructed three STRFs, one for short-ISI events (ISI < 15 ms); one for isolated, long-ISI events (ISI > 15 ms); and one including all events. To characterize stimulus encoding, we calculated the feature selectivity and event information for each of the STRFs. Short-ISI spikes were more feature selective and conveyed information more efficiently. The different ISI regimens of AI neurons did not represent different stimulus features, but short-ISI spike events did contribute over-proportionately to the full spike train STRF information. Thus short-ISIs constitute a robust representation of auditory features, and they are particularly effective at driving postsynaptic activity. This suggests that short-ISI events are especially suited to provide noise immunity and high-fidelity information transmission in AI.

The restless brain

The pressing need to better understand human brain organization is appreciated by all who have labored to explain the uniqueness of human behavior in health and disease. Early work on the cytoarchitectonics of the human brain by Brodmann and others accompanied by several centuries of lesion behavior work, although valuable, has left us far short of what we need. Fortunately, modern brain imaging techniques have, over the past 40 years, substantially changed the situation by permitting the safe appraisal of both anatomical and functional relationships within the living human brain. An unexpected feature of this work is the critical importance of ongoing, intrinsic activity, which accounts for the majority of brain's energy consumption and exhibits a surprising level of organization that emerges with dimensions of both space and time. In this essay, some of the unique features of intrinsic activity are reviewed, as it relates to our understanding of brain organization.

Electrophysiological correlates of sleep homeostasis in freely behaving rats

The electrical activity of the brain does not only reflect the current level of arousal, ongoing behavior, or involvement in a specific task but is also influenced by what kind of activity, and how much sleep and waking occurred before. The best marker of sleep-wake history is the electroencephalogram (EEG) spectral power in slow frequencies (slow-wave activity, 0.5-4 Hz, SWA) during sleep, which is high after extended wakefulness and low after consolidated sleep. While sleep homeostasis has been well characterized in various species and experimental paradigms, the specific mechanisms underlying homeostatic changes in brain activity or their functional significance remain poorly understood. However, several recent studies in humans, rats, and computer simulations shed light on the cortical mechanisms underlying sleep regulation. First, it was found that the homeostatic changes in SWA can be fully accounted for by the variations in amplitude and slope of EEG slow waves, which are in turn determined by the efficacy of corticocortical connectivity. Specifically, the slopes of sleep slow waves were steeper in early sleep compared to late sleep. Second, the slope of cortical evoked potentials, which is an established marker of synaptic strength, was steeper after waking, and decreased after sleep. Further, cortical long-term potentiation (LTP) was partially occluded if it was induced after a period of waking, but it could again be fully expressed after sleep. Finally, multiunit activity recordings during sleep revealed that cortical neurons fired more synchronously after waking, and less so after a period of consolidated sleep. The decline of all these electrophysiological measures-the slopes of slow waves and evoked potentials and neuronal synchrony-during sleep correlated with the decline of the traditional marker of sleep homeostasis, EEG SWA. Taken together, these data suggest that homeostatic changes in sleep EEG are the result of altered neuronal firing and synchrony, which in turn arise from changes in functional neuronal connectivity.

Inhibition dominates the early phase of up-states in the basolateral amygdala

Slow oscillations (<1 Hz) in neural activity occur during sleep and quiet wakefulness in both animals and humans. Single-cell recordings in cortical neurons have shown that these oscillations are driven by a combination of excitatory and inhibitory synaptic inputs. During up-states, although the ratio between them varies between cells, excitation and inhibition follow similar time courses. Neurons in the basolateral amygdala (BLA) also show slow oscillations between the resting membrane potential (down-state) and depolarized potentials (up-states). Delivery of footshock during the down-state fully reproduces up-states in these cells. Here we report that up-states in BLA principal neurons up-states begin with an excitatory drive that is rapidly (within ∼50 ms) overwhelmed by inhibitory input. This excess of inhibitory drive is short lasting (300-400 ms), after which up-states are maintained by a tight balance between excitation and inhibition. This initial large inhibitory input restricts action potential generation and reduces the firing frequency of these cells. These results indicate that, in contrast to cortical neurons, up-states in BLA neurons show an initial period of strong cortically driven feed-forward inhibition. For the remainder of the up-state, feedback inhibition then acts to balance excitatory input.

Self-organized criticality occurs in non-conservative neuronal networks during Up states

During sleep, under anesthesia and in vitro, cortical neurons in sensory, motor, association and executive areas fluctuate between Up and Down states (UDS) characterized by distinct membrane potentials and spike rates [1, 2, 3, 4, 5]. Another phenomenon observed in preparations similar to those that exhibit UDS, such as anesthetized rats [6], brain slices and cultures devoid of sensory input [7], as well as awake monkey cortex [8] is self-organized criticality (SOC). This is characterized by activity "avalanches" whose size distributions obey a power law with critical exponent of about [Formula: see text] and branching parameter near unity. Recent work has demonstrated SOC in conservative neuronal network models [9, 10], however critical behavior breaks down when biologically realistic non-conservatism is introduced [9]. We here report robust SOC behavior in networks of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression. We show analytically and numerically that these networks typically have 2 stable activity levels corresponding to Up and Down states, that the networks switch spontaneously between them, and that Up states are critical and Down states are subcritical.

Sensory input drives multiple intracellular information streams in somatosensory cortex

Stable perception arises from the interaction between sensory inputs and internal activity fluctuations in cortex. Here we analyzed how different types of activity contribute to cortical sensory processing at the cellular scale. We performed whole-cell recordings in the barrel cortex of anesthetized rats while applying ongoing whisker stimulation and measured the information conveyed about the time-varying stimulus by different types of input (membrane potential) and output (spiking) signals. We found that substantial, comparable amounts of incoming information are carried by two types of membrane potential signal: slow, large (up-down state) fluctuations, and faster (>20 Hz), smaller-amplitude synaptic activity. Both types of activity fluctuation are therefore significantly driven by the stimulus on an ongoing basis. Each stream conveys essentially independent information. Output (spiking) information is contained in spike timing not just relative to the stimulus but also relative to membrane potential fluctuations. Information transfer is favored in up states relative to down states. Thus, slow, ongoing activity fluctuations and finer-scale synaptic activity generate multiple channels for incoming and outgoing information within barrel cortex neurons during ongoing stimulation.

Fast nonnegative deconvolution for spike train inference from population calcium imaging

Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging >100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.

Two views of brain function

Traditionally studies of brain function have focused on task-evoked responses. By their very nature, such experiments tacitly encourage a reflexive view of brain function. Although such an approach has been remarkably productive, it ignores the alternative possibility that brain functions are mainly intrinsic, involving information processing for interpreting, responding to and predicting environmental demands. Here I argue that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources. Recognizing the importance of intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux.

Depolarizing effect of neocortical chandelier neurons

Chandelier (or axo-axonic) cells are one of the most distinctive types of GABAergic interneurons in the cortex. Although they have traditionally been considered inhibitory neurons, data from rat and human neocortical preparations suggest that chandelier cells have a depolarizing effect on pyramidal neurons at resting membrane potential, and could even activate synaptic chains of neurons. At the same time, recent results from rat hippocampal chandeliers indicate a predominantly inhibitory effect on their postsynaptic targets. To better understand the function of chandelier neurons, we generated Nkx2.1Cre MADM mice, a strain of genetically engineered animals that, by expressing GFP in a subset of neocortical interneurons, enable the identification and targeting of chandelier cells in living brain slices. Using these mice, we characterized the basic electrophysiological properties of a homogeneous population of chandelier neurons from upper layers of somatosensory cortical slices. These chandelier cells have characteristic axon cartridges and stereotypical electrophysiological features, distinguishable from basket cells. To investigate the effect of chandelier cells on target neurons, we performed paired recordings from chandeliers and postsynaptic pyramidal cells. In both perforated patch and cell-attached configurations, chandelier PSPs have in every case a reversal potential that is depolarized from rest. Our results support the idea that chandelier cells depolarize pyramidal neurons and could potentially have an excitatory effect on the network at rest.

Spontaneous and driven cortical activity: implications for computation

The traditional view of spontaneous neural activity as 'noise' has been challenged by recent findings suggesting that: (a) spontaneous activity in cortical populations is highly structured in both space and time, (b) the spatio-temporal structure of spontaneous activity is linked to the underlying connectivity of the cortical network, (c) spontaneous cortical activity interacts with external stimulation to generate responses to the individual presentations of a stimulus, (d) network connectivity is shaped in part by the statistics of natural signals and (e) ongoing cortical activity represents a continuous top-down prediction/expectation signal that interacts with incoming input to generate an updated representation of the world. These results can be integrated to provide a new framework for the study of cortical computation.

Oscillations in the basolateral amygdala: aversive stimulation is state dependent and resets the oscillatory phase

Slow oscillations (<1 Hz) in neural activity occur during sleep and quiet wakefulness in both animals and humans. Here we show that in urethan-anesthetized animals, neurons in the basolateral amygdala in vivo display a slow oscillation between resting membrane potential (down-state) and depolarized potentials (up-states) occurring at a frequency of approximately 0.3 Hz. This oscillation is insensitive to the holding potential and continues unabated under voltage clamp, indicating that up-states are synaptically driven. Somatosensory stimulation (footshock) delivered during the down-state evoked an all-or-none transition into an up-state. When delivered during down-states, footshocks triggered up-states and reset the phase of the neural oscillation, effectively synchronizing activity in the basolateral amygdala. This phase reset was reproduced by posterior thalamus stimulation, confirming that it was mediated by aversive sensory input. In contrast, a footshock delivered during the up-state was ineffective in stimulating BLA neurons. We conclude that oscillatory activity in the basolateral amygdala is driven by ensembles of cortical neurons. These ensembles gate the response of amygdala neurons to aversive stimulation in a state-dependent manner. Aversive stimulation is effective when the network is in the down-state but ineffective when the network is in an up-state.

Impact of persistent cortical activity (up States) on intracortical and thalamocortical synaptic inputs

The neocortex generates short epochs of persistent activity called up states, which are associated with changes in cellular and network excitability. Using somatosensory thalamocortical slices, we studied the impact of persistent cortical activity during spontaneous up states on intrinsic cellular excitability (input resistance) and on excitatory synaptic inputs of cortical cells. At the intrinsic excitability level, we found that the expected decrease in input resistance (high conductance) resulting from synaptic barrages during up states is counteracted by an increase in input resistance due to depolarization per se. The result is a variable but on average relatively small reduction in input resistance during up states. At the synaptic level, up states enhanced a late synaptic component of short-latency thalamocortical field potential responses but suppressed intracortical field potential responses. The thalamocortical enhancement did not reflect an increase in synaptic strength, as determined by measuring the evoked postsynaptic current, but instead an increase in evoked action potential (spike) probability due to depolarization during up states. In contrast, the intracortical suppression was associated with a reduction in synaptic strength, apparently driven by increased presynaptic intracortical activity during up states. In addition, intracortical suppression also reflected a reduction in evoked spike latency caused by depolarization and the abolishment of longer-latency spikes caused by stronger inhibitory drive during up states. In conclusion, depolarization during up states increases the success of excitatory synaptic inputs to reach firing. However, activity-dependent synaptic depression caused by increased presynaptic firing during up states and the enhancement of evoked inhibitory drive caused by depolarization suppress excitatory intracortical synaptic inputs.

Triggering slow waves during NREM sleep in the rat by intracortical electrical stimulation: effects of sleep/wake history and background activity

In humans, non-rapid eye movement (NREM) sleep slow waves occur not only spontaneously but can also be induced by transcranial magnetic stimulation. Here we investigated whether slow waves can also be induced by intracortical electrical stimulation during sleep in rats. Intracortical local field potential (LFP) recordings were obtained from several cortical locations while the frontal or the parietal area was stimulated intracortically with brief (0.1 ms) electrical pulses. Recordings were performed in early sleep (1st 2-3 h after light onset) and late sleep (6-8 h after light onset). The stimuli reliably triggered LFP potentials that were visually indistinguishable from naturally occurring slow waves. The induced slow waves shared the following features with spontaneous slow waves: they were followed by spindling activity in the same frequency range ( approximately 15 Hz) as spontaneously occurring sleep spindles; they propagated through the neocortex from the area of the stimulation; and compared with late sleep, waves triggered during early sleep were larger, had steeper slopes and fewer multipeaks. Peristimulus background spontaneous activity had a profound influence on the amplitude of the induced slow waves: they were virtually absent if the stimulus was delivered immediately after the spontaneous slow wave. These results show that in the rat a volley of electrical activity that is sufficiently strong to excite and recruit a large cortical neuronal population is capable of inducing slow waves during natural sleep.