Sequential structure of neocortical spontaneous activity in vivo

GABAB receptor-dependent modulation of network activity in the rat prefrontal cortex in vitro

GABA (gamma-aminobutyric acid) can mediate inhibition via pre- and post/extrasynaptic GABA receptors. In this paper we demonstrate potentially post/extrasynaptic GABA(B) receptor-dependent tonic inhibition in L2/3 pyramidal cells of rat medial prefrontal cortex (mPFC) in vitro. First, we show via voltage-clamp experiments the presence of a tonic GABA(B) receptor-dependent outward current in these neurons. This GABA(B)ergic current could be induced by ambient GABA when present at sufficient concentrations. To increase ambient GABA levels in the usually silent slice preparation, we amplified network activity and hence synaptic GABA release with a modified artificial cerebrospinal fluid. The amplitude of tonic GABA(B) current was similar at different temperatures. In addition to the tonic GABA(B) current, we found presynaptic GABA(B) effects, GABA(B)-mediated inhibitory postsynaptic currents and tonic GABA(A) currents. Second, we performed current-clamp experiments to evaluate the functional impact of GABA(B) receptor-mediated inhibition in the mPFC. Activating or inactivating GABA(B) receptors led to rightward (reduction of excitability) or leftward (increase of excitability) shifts, respectively, of the input-output function of mPFC L2/3 pyramidal cells without effects on the slope. Finally, we showed in electrophysiological recordings and epifluorescence Ca(2+)-imaging that GABA(B) receptor-mediated tonic inhibition is capable of regulating network activity. Blocking GABA(B) receptors increased the frequency of excitatory postsynaptic currents impinging on a neuron and prolonged network upstates. These results show that ambient GABA via GABA(B) receptors is powerful enough to modulate neuronal excitability and the activity of neural networks.

Wrestling model of the repertoire of activity propagation modes in quadruple neural networks

The spontaneous activity of engineered quadruple cultured neural networks (of four-coupled sub-networks) exhibits a repertoire of different types of mutual synchronization events. Each event corresponds to a specific activity propagation mode (APM) defined by the order of activity propagation between the sub-networks. We statistically characterized the frequency of spontaneous appearance of the different types of APMs. The relative frequencies of the APMs were then examined for their power-law properties. We found that the frequencies of appearance of the leading (most frequent) APMs have close to constant algebraic ratio reminiscent of Zipf's scaling of words. We show that the observations are consistent with a simplified “wrestling” model. This model represents an extension of the “boxing arena” model which was previously proposed to describe the ratio between the two activity modes in two coupled sub-networks. The additional new element in the “wrestling” model presented here is that the firing within each network is modeled by a time interval generator with similar intra-network Lévy distribution. We modeled the different burst-initiation zones’ interaction by competition between the stochastic generators with Gaussian inter-network variability. Estimation of the model parameters revealed similarity across different cultures while the inter-burst-interval of the cultures was similar across different APMs as numerical simulation of the model predicts.

Neurophysiological and computational principles of cortical rhythms in cognition

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

Integrated brain circuits: neuron-astrocyte interaction in sleep-related rhythmogenesis

Although astrocytes are increasingly recognized as important modulators of neuronal excitability and information transfer at the synapse, whether these cells regulate neuronal network activity has only recently started to be investigated. In this article, we highlight the role of astrocytes in the modulation of circuit function with particular focus on sleep-related rhythmogenesis. We discuss recent data showing that these glial cells regulate slow oscillations, a specific thalamocortical activity that characterizes non-REM sleep, and sleep-associated behaviors. Based on these findings, we predict that our understanding of the genesis and tuning of thalamocortical rhythms will necessarily go through an integrated view of brain circuits in which non-neuronal cells can play important neuromodulatory roles.

Laminar analysis of slow wave activity in humans

Brain electrical activity is largely composed of oscillations at characteristic frequencies. These rhythms are hierarchically organized and are thought to perform important pathological and physiological functions. The slow wave is a fundamental cortical rhythm that emerges in deep non-rapid eye movement sleep. In animals, the slow wave modulates delta, theta, spindle, alpha, beta, gamma and ripple oscillations, thus orchestrating brain electrical rhythms in sleep. While slow wave activity can enhance epileptic manifestations, it is also thought to underlie essential restorative processes and facilitate the consolidation of declarative memories. Animal studies show that slow wave activity is composed of rhythmically recurring phases of widespread, increased cortical cellular and synaptic activity, referred to as active- or up-state, followed by cellular and synaptic inactivation, referred to as silent- or down-state. However, its neural mechanisms in humans are poorly understood, since the traditional intracellular techniques used in animals are inappropriate for investigating the cellular and synaptic/transmembrane events in humans. To elucidate the intracortical neuronal mechanisms of slow wave activity in humans, novel, laminar multichannel microelectrodes were chronically implanted into the cortex of patients with drug-resistant focal epilepsy undergoing cortical mapping for seizure focus localization. Intracortical laminar local field potential gradient, multiple-unit and single-unit activities were recorded during slow wave sleep, related to simultaneous electrocorticography, and analysed with current source density and spectral methods. We found that slow wave activity in humans reflects a rhythmic oscillation between widespread cortical activation and silence. Cortical activation was demonstrated as increased wideband (0.3-200 Hz) spectral power including virtually all bands of cortical oscillations, increased multiple- and single-unit activity and powerful inward transmembrane currents, mainly localized to the supragranular layers. Neuronal firing in the up-state was sparse and the average discharge rate of single cells was less than expected from animal studies. Action potentials at up-state onset were synchronized within +/-10 ms across all cortical layers, suggesting that any layer could initiate firing at up-state onset. These findings provide strong direct experimental evidence that slow wave activity in humans is characterized by hyperpolarizing currents associated with suppressed cell firing, alternating with high levels of oscillatory synaptic/transmembrane activity associated with increased cell firing. Our results emphasize the major involvement of supragranular layers in the genesis of slow wave activity.

How do neurons work together? Lessons from auditory cortex

Recordings of single neurons have yielded great insights into the way acoustic stimuli are represented in auditory cortex. However, any one neuron functions as part of a population whose combined activity underlies cortical information processing. Here we review some results obtained by recording simultaneously from auditory cortical populations and individual morphologically identified neurons, in urethane-anesthetized and unanesthetized passively listening rats. Auditory cortical populations produced structured activity patterns both in response to acoustic stimuli, and spontaneously without sensory input. Population spike time patterns were broadly conserved across multiple sensory stimuli and spontaneous events, exhibiting a generally conserved sequential organization lasting approximately 100 ms. Both spontaneous and evoked events exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, and densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. Laminar propagation differed however, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. In both unanesthetized and urethanized rats, global activity fluctuated between "desynchronized" state characterized by low amplitude, high-frequency local field potentials and a "synchronized" state of larger, lower-frequency waves. Computational studies suggested that responses could be predicted by a simple dynamical system model fitted to the spontaneous activity immediately preceding stimulus presentation. Fitting this model to the data yielded a nonlinear self-exciting system model in synchronized states and an approximately linear system in desynchronized states. We comment on the significance of these results for auditory cortical processing of acoustic and non-acoustic information.

Perceptual qualia and local network behavior in the cerebral cortex

This paper explores the implications of a recently published theory that relates the experience of qualia to the attractor activity in networks of pyramidal cells in the cerebral cortex. The paper builds on this theory, and aims to link activity in different networks to the nature of the qualia experienced. Some basic links between network activity and qualia experiences are initially presented, showing the importance of learning, and the paper then proceeds to relate these mechanisms to the qualia experienced during sensory perception. The paper argues that attractor behavior in networks of layer 2/3 pyramidal neurons could underpin the vivid sensory qualia of perception, and attractor behavior in networks of layer 5A pyramidal neurons could have a role in the more understanding kind of perceptual qualia. Communication between these networks is explored to suggest their involvement in putting incoming sensory information into the context of all prior experience, and the understanding that could result.

Inhibitory modulation of cortical up states

The balance between excitation and inhibition is critical in the physiology of the cerebral cortex. To understand the influence of inhibitory control on the emergent activity of the cortical network, inhibition was progressively blocked in a slice preparation that generates spontaneous rhythmic up states at a similar frequency to those occurring in vivo during slow-wave sleep or anesthesia. Progressive removal of inhibition induced a parametric shortening of up state duration and elongation of the down states, the frequency of oscillations decaying. Concurrently, a gradual increase in the network firing rate during up states occurred. The slope of transitions between up and down states was quantified for different levels of inhibition. The slope of upward transitions reflects the recruitment of the local network and was progressively increased when inhibition was decreased, whereas the speed of activity propagation became faster. Removal of inhibition eventually resulted in epileptiform activity. Whereas gradual reduction of inhibition induced linear changes in up/down states and their propagation, epileptiform activity was the result of a nonlinear transformation. A computational network model showed that strong recurrence plus activity-dependent hyperpolarizing currents were sufficient to account for the observed up state modulations and predicted an increase in activity-dependent hyperpolarization following up states when inhibition was decreased, which was confirmed experimentally.

Automating the design of informative sequences of sensory stimuli

Adaptive stimulus design methods can potentially improve the efficiency of sensory neurophysiology experiments significantly; however, designing optimal stimulus sequences in real time remains a serious technical challenge. Here we describe two approximate methods for generating informative stimulus sequences: the first approach provides a fast method for scoring the informativeness of a batch of specific potential stimulus sequences, while the second method attempts to compute an optimal stimulus distribution from which the experimenter may easily sample. We apply these methods to single-neuron spike train data recorded from the auditory midbrain of zebra finches, and demonstrate that the resulting stimulus sequences do in fact provide more information about neuronal tuning in a shorter amount of time than do more standard experimental designs.

Large-scale microelectrode recordings of high-frequency gamma oscillations in human cortex during sleep

Gamma oscillations (40-120 Hz), usually associated with waking functions, can be recorded in the deepest stages of sleep in animals. The full details of their large-scale coordination across multiple cortical networks are still unknown. Furthermore, it is not known whether oscillations with similar characteristics are also present in the human brain. In this study, we examined the existence of gamma oscillations during polysomnographically defined sleep-wake states using large-scale microelectrode recordings (up to 56 channels), with single-cell and spike-time precision, in epilepsy patients. We report that low (40-80 Hz) and high (80-120 Hz) gamma oscillations recurrently emerged over time windows of several hundreds of milliseconds in all investigated cortical areas during slow-wave sleep. These patterns were correlated with positive peaks of EEG slow oscillations and marked increases in local cellular discharges, suggesting that they were associated with cortical UP states. These gamma oscillations frequently appeared at approximately the same time in many different cortical areas, including homotopic regions, forming large spatial patterns. Coincident firings with millisecond precision were strongly enhanced during gamma oscillations but only between cells within the same cortical area. Furthermore, in a significant number of cases, cortical gamma oscillations tended to occur within 100 ms after hippocampal ripple/sharp wave complexes. These data confirm and extend earlier animal studies reporting that gamma oscillations are transiently expressed during UP states during sleep. We speculate that these high-frequency patterns briefly restore "microwake" activity and are important for consolidation of memory traces acquired during previous awake periods.

Stored-trace reactivation in rat prefrontal cortex is correlated with down-to-up state fluctuation density

Spontaneous reactivation of previously stored patterns of neural activity occurs in hippocampus and neocortex during non-rapid eye movement (NREM) sleep. Notable features of the neocortical local field potential during NREM sleep are high-amplitude, low-frequency thalamocortical oscillations including K-complexes, low-voltage spindles, and high-voltage spindles. Using combined neuronal ensemble and local field potential recordings, we show that prefrontal stored-trace reactivation is correlated with the density of down-to-up state transitions of the population of simultaneously recorded cells, as well as K-complexes and low-voltage spindles in the local field potential. This result strengthens the connection between reactivation and learning, as these same NREM sleep features have been correlated with memory. Although memory trace reactivation is correlated with low-voltage spindles, it is not correlated with high-voltage spindles, indicating that despite their similar frequency characteristics, these two oscillations serve different functions.

Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials

During anesthesia, slow-wave sleep and quiet wakefulness, neuronal membrane potentials collectively switch between de- and hyperpolarized levels, the cortical UP and DOWN states. Previous studies have shown that these cortical UP/DOWN states affect the excitability of individual neurons in response to sensory stimuli, indicating that a significant amount of the trial-to-trial variability in neuronal responses can be attributed to ongoing fluctuations in network activity. However, as intracellular recordings are frequently not available, it is important to be able to estimate their occurrence purely from extracellular data. Here, we combine in vivo whole cell recordings from single neurons with multi-site extracellular microelectrode recordings, to quantify the performance of various approaches to predicting UP/DOWN states from the deep-layer local field potential (LFP). We find that UP/DOWN states in deep cortical layers of rat primary auditory cortex (A1) are predictable from the phase of LFP at low frequencies (< 4 Hz), and that the likelihood of a given state varies sinusoidally with the phase of LFP at these frequencies. We introduce a novel method of detecting cortical state by combining information concerning the phase of the LFP and ongoing multi-unit activity.

Origin of active states in local neocortical networks during slow sleep oscillation

Slow-wave sleep is characterized by spontaneous alternations of activity and silence in corticothalamic networks, but the causes of transition from silence to activity remain unknown. We investigated local mechanisms underlying initiation of activity, using simultaneous multisite field potential, multiunit recordings, and intracellular recordings from 2 to 4 nearby neurons in naturally sleeping or anesthetized cats. We demonstrate that activity may start in any neuron or recording location, with tens of milliseconds delay in other cells and sites. Typically, however, activity originated at deep locations, then involved some superficial cells, but appeared later in the middle of the cortex. Neuronal firing was also found to begin, after the onset of active states, at depths that correspond to cortical layer V. These results support the hypothesis that switch from silence to activity is mediated by spontaneous synaptic events, whereby any neuron may become active first. Due to probabilistic nature of activity onset, the large pyramidal cells from deep cortical layers, which are equipped with the most numerous synaptic inputs and large projection fields, are best suited for switching the whole network into active state.

Statistically optimal perception and learning: from behavior to neural representations

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and re-evaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.

Local field potentials indicate network state and account for neuronal response variability

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell's spiking activity relative to a behavior or stimulus, these network fluctuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

Coherence potentials: loss-less, all-or-none network events in the cortex

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.

The memory function of sleep

Sleep has been identified as a state that optimizes the consolidation of newly acquired information in memory, depending on the specific conditions of learning and the timing of sleep. Consolidation during sleep promotes both quantitative and qualitative changes of memory representations. Through specific patterns of neuromodulatory activity and electric field potential oscillations, slow-wave sleep (SWS) and rapid eye movement (REM) sleep support system consolidation and synaptic consolidation, respectively. During SWS, slow oscillations, spindles and ripples - at minimum cholinergic activity - coordinate the re-activation and redistribution of hippocampus-dependent memories to neocortical sites, whereas during REM sleep, local increases in plasticity-related immediate-early gene activity - at high cholinergic and theta activity - might favour the subsequent synaptic consolidation of memories in the cortex.

Sequential Reinstatement of Neocortical Activity during Slow Oscillations Depends on Cells' Global Activity

During Slow Wave Sleep (SWS), cortical activity is dominated by endogenous processes modulated by slow oscillations (0.1–1 Hz): cell ensembles fluctuate between states of sustained activity (UP states) and silent epochs (DOWN states). We investigate here the temporal structure of ensemble activity during UP states by means of multiple single unit recordings in the prefrontal cortex of naturally sleeping rats. As previously shown, the firing rate of each PFC cell peaks at a distinct time lag after the DOWN/UP transition in a consistent order. We show here that, conversely, the latency of the first spike after the UP state onset depends primarily on the session-averaged firing rates of cells (which can be considered as an indirect measure of their intrinsic excitability). This latency can be explained by a simple homogeneous process (Poisson model) of cell firing, with sleep averaged firing rates employed as parameters. Thus, at DOWN/UP transitions, neurons are affected both by a slow process, possibly originating in the cortical network, modulating the time course of firing for each cell, and by a fast, relatively stereotyped reinstatement of activity, related mostly to global activity levels.

Accounting for network effects in neuronal responses using L1 regularized point process models

Activity of a neuron, even in the early sensory areas, is not simply a function of its local receptive field or tuning properties, but depends on global context of the stimulus, as well as the neural context. This suggests the activity of the surrounding neurons and global brain states can exert considerable influence on the activity of a neuron. In this paper we implemented an L1 regularized point process model to assess the contribution of multiple factors to the firing rate of many individual units recorded simultaneously from V1 with a 96-electrode "Utah" array. We found that the spikes of surrounding neurons indeed provide strong predictions of a neuron's response, in addition to the neuron's receptive field transfer function. We also found that the same spikes could be accounted for with the local field potentials, a surrogate measure of global network states. This work shows that accounting for network fluctuations can improve estimates of single trial firing rate and stimulus-response transfer functions.

The slow (<1 Hz) rhythm of non-REM sleep: a dialogue between three cardinal oscillators

The slow (<1 Hz) rhythm, the most important electroencephalogram (EEG) signature of non-rapid eye movement (NREM) sleep, is generally viewed as originating exclusively from neocortical networks. Here we argue that the full manifestation of this fundamental sleep oscillation in a corticothalamic module requires the dynamic interaction of three cardinal oscillators: one predominantly synaptically based cortical oscillator and two intrinsic, conditional thalamic oscillators. The functional implications of this hypothesis are discussed in relation to other EEG features of NREM sleep, with respect to coordinating activities in local and distant neuronal assemblies and in the context of facilitating cellular and network plasticity during slow-wave sleep.

Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model

Despite the widespread use of EEGs to measure the large-scale dynamics of the human brain, little is known on how the dynamics of EEGs relates to that of the underlying spike rates of cortical neurons. However, progress was made by recent neurophysiological experiments reporting that EEG delta-band phase and gamma-band amplitude reliably predict some complementary aspects of the time course of spikes of visual cortical neurons. To elucidate the mechanisms behind these findings, here we hypothesize that the EEG delta phase reflects shifts of local cortical excitability arising from slow fluctuations in the network input due to entrainment to sensory stimuli or to fluctuations in ongoing activity, and that the resulting local excitability fluctuations modulate both the spike rate and the engagement of excitatory-inhibitory loops producing gamma-band oscillations. We quantitatively tested these hypotheses by simulating a recurrent network of excitatory and inhibitory neurons stimulated with dynamic inputs presenting temporal regularities similar to that of thalamic responses during naturalistic visual stimulation and during spontaneous activity. The network model reproduced in detail the experimental relationships between spike rate and EEGs, and suggested that the complementariness of the prediction of spike rates obtained from EEG delta phase or gamma amplitude arises from nonlinearities in the engagement of excitatory-inhibitory loops and from temporal modulations in the amplitude of the network input, which respectively limit the predictability of spike rates from gamma amplitude or delta phase alone. The model suggested also ways to improve and extend current algorithms for online prediction of spike rates from EEGs.

Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex

Spontaneous activity plays an important role in the function of neural circuits. Although many similarities between spontaneous and sensory-evoked neocortical activity have been reported, little is known about consistent differences between them. Here, using simultaneously recorded cortical populations and morphologically identified pyramidal cells, we compare the laminar structure of spontaneous and sensory-evoked population activity in rat auditory cortex. Spontaneous and evoked patterns both exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, with densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. However, the propagation of spontaneous and evoked activity differed, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. The similarity of sparseness patterns for both neural events and distinct spread of activity may reflect similarity of local processing and differences in the flow of information through cortical circuits, respectively.

Modelling spike trains and extracting response latency with Bayesian binning

The peristimulus time histogram (PSTH) and the spike density function (SDF) are commonly used in the analysis of neurophysiological data. The PSTH is usually obtained by binning spike trains, the SDF being a (Gaussian) kernel smoothed version of the PSTH. While selection of the bin width or kernel size is often relatively arbitrary there have been recent attempts to remedy this situation (Shimazaki and Shinomoto, 2007c,b,a). We further develop an exact Bayesian generative model approach to estimating PSTHs (Endres et al., 2008) and demonstrate its superiority to competing methods using data from early (LGN) and late (STSa) visual areas. We also highlight the advantages of our scheme's automatic complexity control and generation of error bars. Additionally, our approach allows extraction of excitatory and inhibitory response latency from spike trains in a principled way, both on repeated and single trial data. We show that the method can be applied to data with high background firing rates and inhibitory responses (LGN) as well as to data with low firing rate and excitatory responses (STSa). Furthermore, we demonstrate on simulated data that our latency extraction method works for a range of signal-to-noise ratios and background firing rates. While further studies are needed to examine the sensitivity of our method to, for example, gradual changes in firing rate and adaptation, the current results suggest that Bayesian binning is a powerful method for the estimation of firing rate and the extraction response latency from neuronal spike trains.

Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis

Neural representations of even temporally unstructured stimuli can show complex temporal dynamics. In many systems, neuronal population codes show 'progressive differentiation', whereby population responses to different stimuli grow further apart during a stimulus presentation. Here we analysed the response of auditory cortical populations in rats to extended tones. At onset (up to 300 ms), tone responses involved strong excitation of a large number of neurons; during sustained responses (after 500 ms) overall firing rate decreased, but most cells still showed statistically significant rate modulation. Population vector trajectories evoked by different tone frequencies expanded rapidly along an initially similar trajectory in the first tens of milliseconds after tone onset, later diverging to smaller amplitude fixed points corresponding to sustained responses. The angular difference between onset and sustained responses to the same tone was greater than between different tones in the same stimulus epoch. No clear orthogonalization of responses was found with time, and predictability of the stimulus from population activity also decreased during this period compared with onset. The question of whether population activity grew more or less sparse with time depended on the precise mathematical sense given to this term. We conclude that auditory cortical population responses to tones differ from those reported in many other systems, with progressive differentiation not seen for sustained stimuli. Sustained acoustic stimuli are typically not behaviorally salient: we hypothesize that the dynamics we observe may instead allow an animal to maintain a representation of such sounds, at low energetic cost.

Cortical firing and sleep homeostasis

The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here, we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.

A simple model of cortical dynamics explains variability and state dependence of sensory responses in urethane-anesthetized auditory cortex

The responses of neocortical cells to sensory stimuli are variable and state dependent. It has been hypothesized that intrinsic cortical dynamics play an important role in trial-to-trial variability; the precise nature of this dependence, however, is poorly understood. We show here that in auditory cortex of urethane-anesthetized rats, population responses to click stimuli can be quantitatively predicted on a trial-by-trial basis by a simple dynamical system model estimated from spontaneous activity immediately preceding stimulus presentation. Changes in cortical state correspond consistently to changes in model dynamics, reflecting a nonlinear, self-exciting system in synchronized states and an approximately linear system in desynchronized states. We propose that the complex and state-dependent pattern of trial-to-trial variability can be explained by a simple principle: sensory responses are shaped by the same intrinsic dynamics that govern ongoing spontaneous activity.

Correlations and brain states: from electrophysiology to functional imaging

Neural activity in cortex is correlated, an observation that has traditionally been attributed to neurons receiving input from a shared and limited presynaptic pool. Recent studies have shown that correlations are also strongly influenced by network fluctuations that operate over a range of spatial and temporal scales, extending in some cases across cortical areas. These fluctuations are sensitive to internal states and external drive, so that correlations themselves depend strongly on cognitive state and stimulus properties. Given the potential impact on population coding, this modulation of correlations may play an important role in sensory processing.

Discrete- and continuous-time probabilistic models and algorithms for inferring neuronal UP and DOWN states

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.

Spontaneous events outline the realm of possible sensory responses in neocortical populations

Neocortical assemblies produce complex activity patterns both in response to sensory stimuli and spontaneously without sensory input. To investigate the structure of these patterns, we recorded from populations of 40-100 neurons in auditory and somatosensory cortices of anesthetized and awake rats using silicon microelectrodes. Population spike time patterns were broadly conserved across multiple sensory stimuli and spontaneous events. Although individual neurons showed timing variations between stimuli, these were not sufficient to disturb a generally conserved sequential organization observed at the population level, lasting for approximately 100 ms with spiking reliability decaying progressively after event onset. Preserved constraints were also seen in population firing rate vectors, with vectors evoked by individual stimuli occupying subspaces of a larger but still constrained space outlined by the set of spontaneous events. These results suggest that population spike patterns are drawn from a limited "vocabulary," sampled widely by spontaneous events but more narrowly by sensory responses.

Evidence of multistability in a realistic computer simulation of hippocampus subfield CA1

The manner in which hippocampus processes neural signals is thought to be central to the memory encoding process. A theoretically oriented literature has suggested that this is carried out via "attractors" or distinctive spatio-temporal patterns of activity. However, these ideas have not been thoroughly investigated using computational models featuring both realistic single-cell physiology and detailed cell-to-cell connectivity. Here we present a 452 cell simulation based on Traub et al.'s pyramidal cell [Traub RD, Jefferys JG, Miles R, Whittington MA, Toth K. A branching dendritic model of a rodent CA3 pyramidal neurone. J Physiol (Lond) 1994;481:79-95] and interneuron [Traub RD, Miles R, Pyramidal cell-to-inhibitory cell spike transduction explicable by active dendritic conductances in inhibitory cell. J Comput Neurosci 1995;2:291-8] models, incorporating patterns of synaptic connectivity based on an extensive review of the neuroanatomic literature. When stimulated with a one second physiologically realistic input, our simulated tissue shows the ability to hold activity on-line for several seconds; furthermore, its spiking activity, as measured by frequency and interspike interval (ISI) distributions, resembles that of in vivo hippocampus. An interesting emergent property of the system is its tendency to transition from stable state to stable state, a behavior consistent with recent experimental findings [Sasaki T, Matsuki N, Ikegaya Y. Metastability of active CA3 networks. J Neurosci 2007;27:517-28]. Inspection of spike trains and simulated blockade of K(AHP) channels suggest that this is mediated by spike frequency adaptation. This finding, in conjunction with studies showing that apamin, a K(AHP) channel blocker, enhances the memory consolidation process in laboratory animals, suggests the formation of stable attractor states is central to the process by which memories are encoded. Ways that this methodology could shed light on the etiology of mental illness, such as schizophrenia, are discussed.

Competitive STDP-Based Spike Pattern Learning

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern.

Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges.

Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.

Rapid neocortical dynamics: cellular and network mechanisms

The highly interconnected local and large-scale networks of the neocortical sheet rapidly and dynamically modulate their functional connectivity according to behavioral demands. This basic operating principle of the neocortex is mediated by the continuously changing flow of excitatory and inhibitory synaptic barrages that not only control participation of neurons in networks but also define the networks themselves. The rapid control of neuronal responsiveness via synaptic bombardment is a fundamental property of cortical dynamics that may provide the basis of diverse behaviors, including sensory perception, motor integration, working memory, and attention.

Parvalbumin neurons and gamma rhythms enhance cortical circuit performance

Synchronized oscillations and inhibitory interneurons have important and interconnected roles within cortical microcircuits. In particular, interneurons defined by the fast-spiking phenotype and expression of the calcium-binding protein parvalbumin have been suggested to be involved in gamma (30-80 Hz) oscillations, which are hypothesized to enhance information processing. However, because parvalbumin interneurons cannot be selectively controlled, definitive tests of their functional significance in gamma oscillations, and quantitative assessment of the impact of parvalbumin interneurons and gamma oscillations on cortical circuits, have been lacking despite potentially enormous significance (for example, abnormalities in parvalbumin interneurons may underlie altered gamma-frequency synchronization and cognition in schizophrenia and autism). Here we use a panel of optogenetic technologies in mice to selectively modulate multiple distinct circuit elements in neocortex, alone or in combination. We find that inhibiting parvalbumin interneurons suppresses gamma oscillations in vivo, whereas driving these interneurons (even by means of non-rhythmic principal cell activity) is sufficient to generate emergent gamma-frequency rhythmicity. Moreover, gamma-frequency modulation of excitatory input in turn was found to enhance signal transmission in neocortex by reducing circuit noise and amplifying circuit signals, including inputs to parvalbumin interneurons. As demonstrated here, optogenetics opens the door to a new kind of informational analysis of brain function, permitting quantitative delineation of the functional significance of individual elements in the emergent operation and function of intact neural circuitry.

A putatively novel form of spontaneous coordination in neural activity

We simultaneously recorded local field potentials from three sites along the olfactory-entorhinal axis in rats lightly anesthetized with isoflurane, as part of another experiment. While analyzing the initial data from that experiment with spectrograms, we discovered a potentially novel form of correlated neural activity, with near-simultaneous occurrence across the three widely separated brain sites. After validating their existence further, we named these events Synchronous Frequency Bursts (SFBs). Here we report our initial investigations into their properties and their potential functional significance. In Experiment 1, we found that SFBs have highly regular properties, consisting of brief (approximately 250 ms), high amplitude bursts of LFP energy spanning frequency ranges from the delta band (1-4 Hz) to at least the low gamma band (30-50 Hz). SFBs occurred almost simultaneously across recording sites, usually with onsets <25 ms apart, and there was no clear pattern of temporal leading or lagging among the sites. While the SFBs had fairly typical, exponentially decaying power spectral density plots, their coherence structure was unusual, with high peaks in several narrow frequency ranges and little coherence in other bands. In Experiment 2, we found that SFBs occurred far more often under light anesthesia than deeper anesthetic states, and were especially prevalent as the animals regained consciousness. Finally, in Experiment 3 we showed that SFBs occur simultaneously at a significant rate across brain sites from putatively different functional subsystems--olfactory versus motor pathways. We suggest that SFBs do not carry information per se, but rather, play a role in coordinating activity in different frequency bands, potentially brain-wide, as animals progress from sleep or anesthesia toward full consciousness.

First-spike rank order as a reliable indicator of burst initiation and its relation with early-to-fire neurons

In this paper, we study the spontaneous cortical neuronal network in hopes of finding a reliable indicator of burst initiation pathway, which would allow us to study burst initiation in conjunction with burst propagation in future research. Electrical activity is recorded using a 96-electrode microelectrode array on a weekly batch culture (half of the medium was replaced twice every week). We hypothesize that the first-spike onset sequence, which we call first-spike rank order (FSRO) is a reliable indicator of burst initiation, and verified our hypothesis by studying evoked bursts using rearranged rank probability matrices. Under similar conditions, stimulating the same site reliably reproduces the same FSRO. Spontaneous bursts can be classified based on their FSRO using dendrogram clustering. Bursts with different first-spike sequences showed evidence of sharing common early-to-fire neurons, but early-to-fire neurons only consist of a minority of neuronal activity during burst initiation, which is in partial accordance with existing literature. In the study of early-to-fire neurons, we also noticed that our batch-cultured network did not show clear preburst activity, which may indicate fundamental difference compared to continuous perfusion culture.

Comparative molecular neuroanatomy of mammalian neocortex: what can gene expression tell us about areas and layers?

It is over 100 years since Brodmann proposed the homology of layer and area structure of the cerebral cortex across species. His proposal was based on the extensive comparative analyses of various mammalian brains. Although such homology is now well accepted, the recent data in our laboratory showed striking variations of gene expression patterns across areas and species. Are cortical layers and areas really homologous? If they are, to what extent and how are they similar or different? We are trying to answer these questions by identifying the homologous neuronal types common to various areas and species. Toward this goal, we started to classify the cortical pyramidal neurons by expression of particular sets of genes. By using fluorescent double in situ hybridization combined with retrograde tracers, we are characterizing the gene expression phenotypes and projection specificity of cortical excitatory neuron types. In this review, I discuss the recent findings in our laboratory in light of the past and present knowledge about cortical cell types, which provides insight to the homology (and lack thereof) of the mammalian neocortical organization.

Activation of the Cholinergic System Endows Compositional Properties to Striatal Cell Assemblies

Striatal cell assemblies are thought to encode network states related to associative learning, procedural memory, and the sequential organization of behavior. Cholinergic neurotransmission modulates memory processes in the striatum and other brain structures. This work asks if the activity of striatal microcircuits observed in living nervous tissue, with attributes similar to cell assemblies, exhibit some of the properties proposed to be necessary to compose memory traces. Accordingly, we used whole cell and calcium-imaging techniques to investigate the cholinergic modulation of striatal neuron pools that have been reported to exhibit several properties expected from cell assemblies such as synchronous states of activity and the alternation of this activity among different neuron pools. We analyzed the cholinergic modulation of the activity of neuron pools with multidimensional reduction techniques and vectorization of network dynamics. It was found that the activation of the cholinergic system enables striatal cell assemblies with properties that have been posited for recurrent neural artificial networks with memory storage capabilities. Graph theory techniques applied to striatal network states revealed sequences of vectors with a recursive dynamics similar to closed reverberating cycles. The cycles exhibited a modular architecture and a hierarchical organization. It is then concluded that, under certain conditions, the cholinergic system enables the striatal microcircuit with the ability to compose complex sequences of activity. Neuronal recurrent networks with the characteristics encountered in the present experiments are proposed to allow repeated sequences of activity to become memories and repeated memories to compose learned motor procedures.

Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity

Background

Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns.

Methodology/Principal Findings

Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time.

Conclusions/Significance

We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns.

Statistical significance of precisely repeated intracellular synaptic patterns

Can neuronal networks produce patterns of activity with millisecond accuracy? It may seem unlikely, considering the probabilistic nature of synaptic transmission. However, some theories of brain function predict that such precision is feasible and can emerge from the non-linearity of the action potential generation in circuits of connected neurons. Several studies have presented evidence for and against this hypothesis. Our earlier work supported the precision hypothesis, based on results demonstrating that precise patterns of synaptic inputs could be found in intracellular recordings from neurons in brain slices and in vivo. To test this hypothesis, we devised a method for finding precise repeats of activity and compared repeats found in the data to those found in surrogate datasets made by shuffling the original data. Because more repeats were found in the original data than in the surrogate data sets, we argued that repeats were not due to chance occurrence. Mokeichev et al. (2007) challenged these conclusions, arguing that the generation of surrogate data was insufficiently rigorous. We have now reanalyzed our previous data with the methods introduced from Mokeichev et al. (2007). Our reanalysis reveals that repeats are statistically significant, thus supporting our earlier conclusions, while also supporting many conclusions that Mokeichev et al. (2007) drew from their recent in vivo recordings. Moreover, we also show that the conditions under which the membrane potential is recorded contributes significantly to the ability to detect repeats and may explain conflicting results. In conclusion, our reevaluation resolves the methodological contradictions between Ikegaya et al. (2004) and Mokeichev et al. (2007), but demonstrates the validity of our previous conclusion that spontaneous network activity is non-randomly organized.

UP states protect ongoing cortical activity from thalamic inputs

Cortical neurons in vitro and in vivo fluctuate spontaneously between two stable membrane potentials: a depolarized UP state and a hyperpolarized DOWN state. UP states temporally correspond with multineuronal firing sequences which may be important for information processing. To examine how thalamic inputs interact with ongoing cortical UP state activity, we used calcium imaging and targeted whole-cell recordings of activated neurons in thalamocortical slices of mouse somatosensory cortex. Whereas thalamic stimulation during DOWN states generated multineuronal, synchronized UP states, identical stimulation during UP states had no effect on the subthreshold membrane dynamics of the vast majority of cells or on ongoing multineuronal temporal patterns. Both thalamocortical and corticocortical PSPs were significantly reduced and neuronal input resistance was significantly decreased during cortical UP states – mechanistically consistent with UP state insensitivity. Our results demonstrate that cortical dynamics during UP states are insensitive to thalamic inputs.

Reverberation of recent visual experience in spontaneous cortical waves

Spontaneous waves of activity propagating across large cortical areas may play important roles in sensory processing and circuit refinement. However, whether these waves are in turn shaped by sensory experience remains unclear. Here we report that visually evoked cortical activity reverberates in subsequent spontaneous waves. Voltage-sensitive dye imaging in rat visual cortex shows that following repetitive presentation of a given visual stimulus, spatiotemporal activity patterns resembling the evoked response appear more frequently in the spontaneous waves. This effect is specific to the response pattern evoked by the repeated stimulus, and it persists for several minutes without further visual stimulation. Such wave-mediated reverberation could contribute to short-term memory and help to consolidate the transient effects of recent sensory experience into long-lasting cortical modifications.

Complex events initiated by individual spikes in the human cerebral cortex

Synaptic interactions between neurons of the human cerebral cortex were not directly studied to date. We recorded the first dataset, to our knowledge, on the synaptic effect of identified human pyramidal cells on various types of postsynaptic neurons and reveal complex events triggered by individual action potentials in the human neocortical network. Brain slices were prepared from nonpathological samples of cortex that had to be removed for the surgical treatment of brain areas beneath association cortices of 58 patients aged 18 to 73 y. Simultaneous triple and quadruple whole-cell patch clamp recordings were performed testing mono- and polysynaptic potentials in target neurons following a single action potential fired by layer 2/3 pyramidal cells, and the temporal structure of events and underlying mechanisms were analyzed. In addition to monosynaptic postsynaptic potentials, individual action potentials in presynaptic pyramidal cells initiated long-lasting (37 ± 17 ms) sequences of events in the network lasting an order of magnitude longer than detected previously in other species. These event series were composed of specifically alternating glutamatergic and GABAergic postsynaptic potentials and required selective spike-to-spike coupling from pyramidal cells to GABAergic interneurons producing concomitant inhibitory as well as excitatory feed-forward action of GABA. Single action potentials of human neurons are sufficient to recruit Hebbian-like neuronal assemblies that are proposed to participate in cognitive processes.

We recorded the first connections, to our knowledge, between human nerve cells and reveal that a subset of interactions is so strong that some presynaptic cells are capable of eliciting action potentials in the postsynaptic target neurons. Interestingly, these strong connections selectively link pyramidal cells using the neurotransmitter glutamate to neurons releasing gamma aminobutyric acid (GABA). Moreover, the GABAergic neurons receiving the strong connections include different types: basket cells, which inhibit several target cell populations, and another type called the chandelier cells, which can be excitatory and target pyramidal cells only. Thus, the activation originating from a single pyramidal cell propagates to synchronously working inhibitory and excitatory GABAergic neurons. Inhibition then arrives to various neuron classes, but excitation finds only pyramidal cells, which in turn, can propagate excitation even further in the network of neurons. This chain of events revealed here leads to network activation approximately an order of magnitude longer than detected previously in response to a single action potential in a single neuron. Individual-neuron–activated groups of neurons resemble the so-called functional assemblies that were proposed as building blocks of higher order cognitive representations.

A novel study on connections between human neurons reveals that single spikes in pyramidal cells can activate synchronously timed assemblies through strong connections linking pyramidal cells with inhibitory and excitatory GABAergic neurons.

Spontaneous neuronal burst discharges as dependent and independent variables in the maturation of cerebral cortex tissue cultured in vitro: a review of activity-dependent studies in live 'model' systems for the development of intrinsically generated bioelectric slow-wave sleep patterns

A survey is presented of recent experiments which utilize spontaneous neuronal spike trains as dependent and/or independent variables in developing cerebral cortex cultures when synaptic transmission is interfered with for varying periods of time. Special attention is given to current difficulties in selecting suitable preparations for carrying out biologically relevant developmental studies, and in applying spike-train analysis methods with sufficient resolution to detect activity-dependent age and treatment effects. A hierarchy of synchronized nested burst discharges which approximate early slow-wave sleep patterns in the intact organism is established as a stable basis for isolated cortex function. The complexity of reported long- and short-term homeostatic responses to experimental interference with synaptic transmission is reviewed, and the crucial role played by intrinsically generated bioelectric activity in the maturation of cortical networks is emphasized.

Two distinct activity patterns of fast-spiking interneurons during neocortical UP states

During sleep, neocortical neuronal networks oscillate slowly (<1 Hz) between periods of activity (UP states) and silence (DOWN states). UP states favor the interaction between thalamic-generated spindles (7-14 Hz) and cortically generated gamma (30-80 Hz) waves. We studied how these three nested oscillations modulate fast-spiking interneuron (FSi) activity in vivo in VGAT-Venus transgenic rats. Our data describe a population of FSi that discharge "early" within UP states and another population that discharge "late." Early FSi tended to be silent during epochs of desynchronization, whereas late FSi were active. We hypothesize that late FSi may be responsible for generating the gamma oscillations associated with cognitive processing during wakefulness. Remarkably, FSi populations were differently modulated by spindle and gamma rhythms. Early FSi were robustly coupled to spindles and always discharged earlier than late FSi within spindle and gamma cycles. The preferred firing phase during spindle and gamma waves was strongly correlated in each cell, suggesting a cross-frequency coupling between oscillations. Our results suggest a precise spatiotemporal pattern of FSi activity during UP states, whereby information rapidly flows between early and late cells, initially promoted by spindles and efficiently extended by local gamma oscillations.

Interaction of slow cortical rhythm with somatosensory information processing in urethane-anesthetized rats

Slow cortical rhythm (SCR) is a rhythmic alteration of active (hypopolarized), and silent (hyperpolarized) epochs in cortical cells. SCR was found to influence sensory information processing in various models, but these studies yielded inconsistent results. We examined sensory processing in anesthetized rats during SCR by recording multiple unit activity (MUA) and evoked field potentials (eFPs). Evoked field potentials as well as spontaneous FP changes around spontaneous activations were analyzed by subsequent current source density (CSD) analysis. MUA responses and eFPs were recorded from the hindlimb area (HL) of the somatosensory cortex (SI) to electrical stimuli of the tibial nerve during active and silent states, respectively. Stimulus-associated MUA above the ongoing background activity did not differ significantly in active vs. silent states. Short-latency (<50 ms) eFP responses consisted of a sequence of deep-negative and deep-positive waves. Parameters of the first negative deflection were similar in both states. Stimulation in the silent state occasionally induced 500-700 ms long spindles in the alpha range (10-16 Hz). Spindles were never observed in responses to active state stimulation. CSD analysis showed moderately different cortical sink-source patterns when the stimulus was applied during active vs. silent state. Sinks first appeared in layer IV, V and VI, corresponding sources were in layer I/II, V and VI. Stronger activation appeared in the infraganular layers in the case of active state. CSD of spontaneous FPs revealed some sequential activation pattern in the cortex when strongest and earlier sink appeared in layer III during active states.

On how network architecture determines the dominant patterns of spontaneous neural activity

In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activity. These patterns are thought to reflect relevant properties of the network, including anatomical features like its modularity. It is also assumed that the synaptic connections of the network constrain the repertoire of emergent, spontaneous patterns. Although the link between network architecture and network activity has been extensively investigated in the last few years from different perspectives, our understanding of the relationship between the network connectivity and the structure of its spontaneous activity is still incomplete. Using a general mathematical model of neural dynamics we have studied the link between spontaneous activity and the underlying network architecture. In particular, here we show mathematically how the synaptic connections between neurons determine the repertoire of spatial patterns displayed in the spontaneous activity. To test our theoretical result, we have also used the model to simulate spontaneous activity of a neural network, whose architecture is inspired by the patchy organization of horizontal connections between cortical columns in the neocortex of primates and other mammals. The dominant spatial patterns of the spontaneous activity, calculated as its principal components, coincide remarkably well with those patterns predicted from the network connectivity using our theory. The equivalence between the concept of dominant pattern and the concept of attractor of the network dynamics is also demonstrated. This in turn suggests new ways of investigating encoding and storage capabilities of neural networks.

Spike correlations in a songbird agree with a simple markov population model

The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.

To deal with the vast complexity of the brain and its many degrees of freedom, many reductionist methods have been designed that can be used to simplify neural interactions to just a few key underlying macroscopic variables. Despite these theoretical advances, even today relatively few population models have been subjected to stringent experimental tests. We explore whether second-order spike correlations measured in songbirds can be explained by single-neuron statistics and population dynamics, both reflecting hypotheses on network connectivity. We formulate a Markov population model with essentially two degrees of freedom and associated with different behavioral states of birds such as waking, singing, or sleeping. Excellent agreement between spike-train data and model is achieved, given a few connectivity assumptions that strengthen the view of a hierarchical organization of songbird motor networks. This work is an important demonstration that a broad range of neural activity patterns can be compatible at the population level with few underlying degrees of freedom.