The upshot of up states in the neocortex: from slow oscillations to memory formation

Network state transitions during cortical development

Mammalian cortical networks are active before synaptogenesis begins in earnest, before neuronal migration is complete, and well before an animal opens its eyes and begins to actively explore its surroundings. This early activity undergoes several transformations during development. The most important of these is a transition from episodic synchronous network events, which are necessary for patterning the neocortex into functionally related modules, to desynchronized activity that is computationally more powerful and efficient. Network desynchronization is perhaps the most dramatic and abrupt developmental event in an otherwise slow and gradual process of brain maturation. In this Review, we summarize what is known about the phenomenology of developmental synchronous activity in the rodent neocortex and speculate on the mechanisms that drive its eventual desynchronization. We argue that desynchronization of network activity is a fundamental step through which the cortex transitions from passive, bottom-up detection of sensory stimuli to active sensory processing with top-down modulation.

Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity

Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.

Propofol-mediated Unconsciousness Disrupts Progression of Sensory Signals through the Cortical Hierarchy

A critical component of anesthesia is the loss of sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of nonhuman primates before and during propofol-mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-related synchronization between brain areas in the local field potential of Awake animals. By contrast, propofol-mediated unconsciousness eliminated stimulus-related synchrony and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in Awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just because of asynchronous Down states. Rather, both Down states and Up states reflect disrupted dynamics.

Exploring Ripple Waves in the Human Brain

Ripples are brief (<150 ms) high-frequency oscillatory neural activities in the brain with a range of 140 to 200 Hz in rodents and 80 to 140 Hz in humans. Ripples are regarded as playing an essential role in several aspects of memory function, mainly in the hippocampus. This type of ripple generally occurs with sharp waves and is called a sharp-wave ripple (SPW-R). Extensive research of SPW-Rs in the rodent brain while actively awake has also linked the function of these SPW-Rs to navigation and decision making. Although many studies with rodents unveiled SPW-R function, research in humans on this subject is still sparse. Therefore, unveiling SPW-R function in the human hippocampus is warranted. A certain type of ripples may also be a biomarker of epilepsy. This type of ripple is called a pathological ripple (p-ripple). p-ripples have a wider range of frequency (80-500 Hz) than SPW-Rs, and the range of frequency is especially higher in brain regions that are intrinsically linked to epilepsy onset. Brain regions producing ripples are too small for scalp electrode recording, and intracranial recording is typically needed to detect ripples. In addition, SPW-Rs in the human hippocampus have been recorded from patients with epilepsy who may have p-ripples. Differentiating SPW-Rs and p-ripples is often not easy. We need to develop more sophisticated methods to record SPW-Rs to differentiate them from p-ripples. This paper reviews the general features and roles of ripple waves.

Proceedings of the First Pediatric Coma and Disorders of Consciousness Symposium by the Curing Coma Campaign, Pediatric Neurocritical Care Research Group, and NINDS: Gearing for Success in Coma Advancements for Children and Neonates

This proceedings article presents the scope of pediatric coma and disorders of consciousness based on presentations and discussions at the First Pediatric Disorders of Consciousness Care and Research symposium held on September 14th, 2021. Herein we review the current state of pediatric coma care and research opportunities as well as shared experiences from seasoned researchers and clinicians. Salient current challenges and opportunities in pediatric and neonatal coma care and research were identified through the contributions of the presenters, who were Jose I. Suarez, MD, Nina F. Schor, MD, PhD, Beth S. Slomine, PhD Erika Molteni, PhD, and Jan-Marino Ramirez, PhD, and moderated by Varina L. Boerwinkle, MD, with overview by Mark Wainwright, MD, and subsequent audience discussion. The program, executively planned by Varina L. Boerwinkle, MD, Mark Wainwright, MD, and Michelle Elena Schober, MD, drove the identification and development of priorities for the pediatric neurocritical care community.

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Documenting a mouse's "real world" behavior in the "small world" of a laboratory cage with continuous video recordings offers insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Nevertheless, there are challenges in the design of a small world, the behavior selected for analysis, and the form of the analysis used. Here we offer insights into small world analyses by describing how acute behavioral procedures can guide continuous behavioral methodology. We show how algorithms can identify behavioral acts including walking and rearing, circadian patterns of action including sleep duration and waking activity, and the organization of patterns of movement into home base activity and excursions, and how they are altered with aging. We additionally describe how specific tests can be incorporated within a mouse's living arrangement. We emphasize how machine learning can condense and organize continuous activity that extends over extended periods of time.

Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

Stimuli-evoked and spontaneous brain activity propagates across the cortex in diverse spatiotemporal patterns. Despite extensive studies, the relationship between spontaneous and evoked activity is poorly understood. We investigate this relationship by comparing the amplitude, speed, direction, and complexity of propagation trajectories of spontaneous and evoked activity elicited with visual, auditory, and tactile stimuli using mesoscale wide-field imaging in mice. For both spontaneous and evoked activity, the speed and direction of propagation is modulated by the amplitude. However, spontaneous activity has a higher complexity of the propagation trajectories. For low stimulus strengths, evoked activity amplitude and speed is similar to that of spontaneous activity but becomes dissimilar at higher stimulus strengths. These findings are consistent with observations that primary sensory areas receive widespread inputs from other cortical regions, and during rest, the cortex tends to reactivate traces of complex multisensory experiences that might have occurred in exhibition of different behaviors.

Peripheral sensory stimulation elicits global slow waves by recruiting somatosensory cortex bilaterally

Slow waves (SWs) are globally propagating, low-frequency (0.5- to 4-Hz) oscillations that are prominent during sleep and anesthesia. SWs are essential to neural plasticity and memory. However, much remains unknown about the mechanisms coordinating SW propagation at the macroscale. To assess SWs in the context of macroscale networks, we recorded cortical activity in awake and ketamine/xylazine-anesthetized mice using widefield optical imaging with fluorescent calcium indicator GCaMP6f. We demonstrate that unilateral somatosensory stimulation evokes bilateral waves that travel across the cortex with state-dependent trajectories. Under anesthesia, we observe that rhythmic stimuli elicit globally resonant, front-to-back propagating SWs. Finally, photothrombotic lesions of S1 show that somatosensory-evoked global SWs depend on bilateral recruitment of homotopic primary somatosensory cortices. Specifically, unilateral lesions of S1 disrupt somatosensory-evoked global SW initiation from either hemisphere, while spontaneous SWs are largely unchanged. These results show that evoked SWs may be triggered by bilateral activation of specific, homotopically connected cortical networks.

Modulation of cortical slow oscillatory rhythm by GABAB receptors: an in vitro experimental and computational study

KEY POINTS

We confirm that GABAB receptors (GABAB -Rs) are involved in the termination of Up-states; their blockade consistently elongates Up-states. GABAB -Rs also modulate Down-states and the oscillatory cycle, thus having an impact on slow oscillation rhythm and its regularity. The most frequent effect of GABAB -R blockade is elongation of Down-states and subsequent decrease of oscillatory frequency, with an increased regularity. In a quarter of cases, GABAB -R blockade shortened Down-states and increased oscillatory frequency, changes that are independent of firing rates in Up-states. Our computer model provides mechanisms for the experimentally observed dynamics following blockade of GABAB -Rs, for Up/Down durations, oscillatory frequency and regularity. The time course of excitation, inhibition and adaptation can explain the observed dynamics of the network. This study brings novel insights into the role of GABAB -R-mediated slow inhibition on the slow oscillatory activity, which is considered the default activity pattern of the cortical network.

ABSTRACT

Slow wave oscillations (SWOs) dominate cortical activity during deep sleep, anaesthesia and in some brain lesions. SWOs are composed of periods of activity (Up states) interspersed with periods of silence (Down states). The rhythmicity expressed during SWOs integrates neuronal and connectivity properties of the network and is often altered under pathological conditions. Adaptation mechanisms as well as synaptic inhibition mediated by GABAB receptors (GABAB -Rs) have been proposed as mechanisms governing the termination of Up states. The interplay between these two mechanisms is not well understood, and the role of GABAB -Rs controlling the whole cycle of the SWO has not been described. Here we contribute to its understanding by combining in vitro experiments on spontaneously active cortical slices and computational techniques. GABAB -R blockade modified the whole SWO cycle, not only elongating Up states, but also affecting the subsequent Down state duration. Furthermore, while adaptation tends to yield a rather regular behaviour, we demonstrate that GABAB -R activation desynchronizes the SWOs. Interestingly, variability changes could be accomplished in two different ways: by either shortening or lengthening the duration of Down states. Even when the most common observation following GABAB -Rs blocking is the lengthening of Down states, both changes are expressed experimentally and also in numerical simulations. Our simulations suggest that the sluggishness of GABAB -Rs to follow the excitatory fluctuations of the cortical network can explain these different network dynamics modulated by GABAB -Rs.

Diazepam and ethanol differently modulate neuronal activity in organotypic cortical cultures

Background

The pharmacodynamic results of diazepam and ethanol administration are similar, in that each can mediate amnestic and sedative-hypnotic effects. Although each of these molecules effectively reduce the activity of central neurons, diazepam does so through modulation of a more specific set of receptor targets (GABA_A receptors containing a γ-subunit), while alcohol is less selective in its receptor bioactivity. Our investigation focuses on divergent actions of diazepam and ethanol on the firing patterns of cultured cortical neurons.

Method

We used electrophysiological recordings from organotypic slice cultures derived from Sprague–Dawley rat neocortex. We exposed these cultures to either diazepam (15 and 30 µM, n = 7) or ethanol (30 and 60 mM, n = 11) and recorded the electrical activity at baseline and experimental conditions. For analysis, we extracted the episodes of spontaneous activity, i.e., cortical up-states. After separation of action potential and local field potential (LFP) activity, we looked at differences in the number of action potentials, in the spectral power of the LFP, as well as in the coupling between action potential and LFP phase.

Results

While both substances seem to decrease neocortical action potential firing in a not significantly different (p = 0.659, Mann–Whitney U) fashion, diazepam increases the spectral power of the up-state without significantly impacting the spectral composition, whereas ethanol does not significantly change the spectral power but the oscillatory architecture of the up-state as revealed by the Friedman test with Bonferroni correction (p < 0.05). Further, the action potential to LFP-phase coupling reveals a synchronizing effect of diazepam for a wide frequency range and a narrow-band de-synchronizing effect for ethanol (p < 0.05, Kolmogorov–Smirnov test).

Conclusion

Diazepam and ethanol, induce specific patterns of network depressant actions. Diazepam induces cortical network inhibition and increased synchronicity via gamma subunit containing GABA_A receptors. Ethanol also induces cortical network inhibition, but without an increase in synchronicity via a wider span of molecular targets.

Visual hallucinations, thalamocortical physiology and Lewy body disease: A review

One of the core diagnostic criteria for Dementia with Lewy Bodies (DLB) is the presence of visual hallucinations. The presence of hallucinations, along with fluctuations in the level of arousal and sleep disturbance, point to potential pathological mechanisms at the level of the thalamus. However, the potential role of thalamic dysfunction in DLB, particularly as it relates to the presence of formed visual hallucinations is not known. Here, we review the literature on the pathophysiology of DLB with respect to modern theories of thalamocortical function and attempt to derive an understanding of how such hallucinations arise. Based on the available literature, we propose that combined thalamic-thalamic reticular nucleus and thalamocortical pathology may explain the phenomenology of visual hallucinations in DLB. In particular, diminished α7 cholinergic activity in the thalamic reticular nucleus may critically disinhibit thalamocortical activity. Further, concentrated pathological changes within the posterior regions of the thalamus may explain the predilection for the hallucinations to be visual in nature.

Theta Neurofeedback Effects on Motor Memory Consolidation and Performance Accuracy: An Apparent Paradox?

Previous studies have shown that theta neurofeedback enhances motor memory consolidation on an easy-to-learn finger-tapping task. However, the simplicity of the finger-tapping task precludes evaluating the putative effects of elevated theta on performance accuracy. Mastering a motor sequence is classically assumed to entail faster performance with fewer errors. The speed-accuracy tradeoff (SAT) principle states that as action speed increases, motor performance accuracy decreases. The current study investigated whether theta neurofeedback could improve both performance speed and performance accuracy, or would only enhance performance speed at the cost of reduced accuracy. A more complex task was used to study the effects of parietal elevated theta on 45 healthy volunteers The findings confirmed previous results on the effects of theta neurofeedback on memory consolidation. In contrast to the two control groups, in the theta-neurofeedback group the speed-accuracy tradeoff was reversed. The speed-accuracy tradeoff patterns only stabilized after a night's sleep implying enhancement in terms of both speed and accuracy.

Shaping the Default Activity Pattern of the Cortical Network

Slow oscillations have been suggested as the default emergent activity of the cortical network. This is a low complexity state that integrates neuronal, synaptic, and connectivity properties of the cortex. Shaped by variations of physiological parameters, slow oscillations provide information about the underlying healthy or pathological network. We review how this default activity is shaped, how it acts as a powerful attractor, and how getting out of it is necessary for the brain to recover the levels of complexity associated with conscious states. We propose that slow oscillations provide a robust unifying paradigm for the study of cortical function.

[Connections between sleep and Alzheimer's disease : Insomnia, amnesia and amyloid]

Sleep plays an essential role in memory consolidation. Although sleep problems are common in Alzheimer's disease, they are not usually thought to be key features of the disease; however, new experimental research has shown that sleep disturbances not only occur before the onset of typical cognitive deficits but are also associated with the pathogenesis of Alzheimer's disease and may have a decisive influence on the symptoms and course. Thus, sleep disturbances may be potentially modifiable risk factors for Alzheimer's disease that deserve more attention in research, diagnostics and treatment.

Pilot Study of Propofol-induced Slow Waves as a Pharmacologic Test for Brain Dysfunction after Brain Injury

Background

Slow waves (less than 1 Hz) are the most important electroencephalogram signatures of nonrapid eye movement sleep. While considered to have a substantial importance in, for example, providing conditions for single-cell rest and preventing long-term neural damage, a disturbance in this neurophysiologic phenomenon is a potential indicator of brain dysfunction.

Methods

Since, in healthy individuals, slow waves can be induced with anesthetics, the authors tested the possible association between hypoxic brain injury and slow-wave activity in comatose postcardiac arrest patients (n = 10) using controlled propofol exposure. The slow-wave activity was determined by calculating the low-frequency (less than 1 Hz) power of the electroencephalograms recorded approximately 48 h after cardiac arrest. To define the association between the slow waves and the potential brain injury, the patients’ neurologic recovery was then followed up for 6 months.

Results

In the patients with good neurologic outcome (n = 6), the low-frequency power of electroencephalogram representing the slow-wave activity was found to substantially increase (mean ± SD, 190 ± 83%) due to the administration of propofol. By contrast, the patients with poor neurologic outcome (n = 4) were unable to generate propofol-induced slow waves.

Conclusions

In this experimental pilot study, the comatose postcardiac arrest patients with poor neurologic outcome were unable to generate normal propofol-induced electroencephalographic slow-wave activity 48 h after cardiac arrest. The finding might offer potential for developing a pharmacologic test for prognostication of brain injury by measuring the electroencephalographic response to propofol.

Do Cortical Circuits Need Protecting from Themselves?

All hippocampal and neocortical networks can be driven to seize quite easily. This can be done using drugs, by altering the ionic constituency of the bathing medium [cerebrospinal fluid (CSF)], or by electrical stimulation (both experimentally and clinically, as in electroconvulsive therapy). It is worth asking why this is so, because this will both tell us more about potentially devastating neurological disorders and extend our understanding of cortical function and architecture. Here I review work examining the features of cortical networks that bias activity towards and away from hyperexcitability. I suggest that several cellular- and circuit-level features of rapidly responsive interneuron networks tip the balance away from seizure in the healthy brain.

Sharp Wave Ripples during Visual Exploration in the Primate Hippocampus

Hippocampal sharp-wave ripples (SWRs) are highly synchronous oscillatory field potentials that are thought to facilitate memory consolidation. SWRs typically occur during quiescent states, when neural activity reflecting recent experience is replayed. In rodents, SWRs also occur during brief locomotor pauses in maze exploration, where they appear to support learning during experience. In this study, we detected SWRs that occurred during quiescent states, but also during goal-directed visual exploration in nonhuman primates (Macaca mulatta). The exploratory SWRs showed peak frequency bands similar to those of quiescent SWRs, and both types were inhibited at the onset of their respective behavioral epochs. In apparent contrast to rodent SWRs, these exploratory SWRs occurred during active periods of exploration, e.g., while animals searched for a target object in a scene. SWRs were associated with smaller saccades and longer fixations. Also, when they coincided with target-object fixations during search, detection was more likely than when these events were decoupled. Although we observed high gamma-band field potentials of similar frequency to SWRs, only the SWRs accompanied greater spiking synchrony in neural populations. These results reveal that SWRs are not limited to off-line states as conventionally defined; rather, they occur during active and informative performance windows. The exploratory SWR in primates is an infrequent occurrence associated with active, attentive performance, which may indicate a new, extended role of SWRs during exploration in primates.

SIGNIFICANCE STATEMENT

Sharp-wave ripples (SWRs) are high-frequency oscillations that generate highly synchronized activity in neural populations. Their prevalence in sleep and quiet wakefulness, and the memory deficits that result from their interruption, suggest that SWRs contribute to memory consolidation during rest. Here, we report that SWRs from the monkey hippocampus occur not only during behavioral inactivity but also during successful visual exploration. SWRs were associated with attentive, focal search and appeared to enhance perception of locations viewed around the time of their occurrence. SWRs occurring in rest are noteworthy for their relation to heightened neural population activity, temporally precise and widespread synchronization, and memory consolidation; therefore, the SWRs reported here may have a similar effect on neural populations, even as experiences unfold.

Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation

Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep, the underlying neural mechanisms remain poorly understood. To investigate the neurophysiological basis for offline gains, we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task. We found that sleep improved movement speed with preservation of accuracy. These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement (NREM) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction. Interestingly, replay was linked to the coincidence of slow-wave events and bursts of spindle activity. Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task. Significantly, replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning (i.e., after motor kinematics had stabilized) did not show evidence of replay. Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning.

During non-REM sleep in rats, consolidation and offline improvements of a recently learned motor skill are linked to synchronous reactivation of task-related neural ensembles.

Sleep has been shown to help in consolidating learned motor tasks. In other words, sleep can induce “offline” gains in a new motor skill even in the absence of further training. However, how sleep induces this change has not been clearly identified. One hypothesis is that consolidation of memories during sleep occurs by “reactivation” of neurons engaged during learning. In this study, we tested this hypothesis by recording populations of neurons in the motor cortex of rats while they learned a new motor skill and during sleep both before and after the training session. We found that subsets of task-relevant neurons formed highly synchronized ensembles during learning. Interestingly, these same neural ensembles were reactivated during subsequent sleep blocks, and the degree of reactivation was correlated with several metrics of motor memory consolidation. Specifically, after sleep, the speed at which animals performed the task while maintaining accuracy was increased, and the activity of the neuronal assembles were more tightly bound to motor action. Further analyses showed that reactivation events occurred episodically and in conjunction with spindle-oscillations—common bursts of brain activity seen during sleep. This observation is consistent with previous findings in humans that spindle-oscillations correlate with consolidation of learned tasks. Our study thus provides insight into the neuronal network mechanism supporting consolidation of motor memory during sleep and may lead to novel interventions that can enhance skill learning in both healthy and injured nervous systems.

Memory impairment and alterations in prefrontal cortex gamma band activity following methamphetamine sensitization

RATIONALE

Repeated methamphetamine (MA) use leads to increases in the incentive motivational properties of the drug as well as cognitive impairments. These behavioral alterations persist for some time following abstinence, and neuroadaptations in the structure and function of the prefrontal cortex (PFC) are particularly important for their expression. However, there is a weak understanding of the changes in neural firing and oscillatory activity in the PFC evoked by repeated drug use, thus complicating the development of novel treatment strategies for addiction.

OBJECTIVES

The purpose of the current study was to assess changes in cognitive and brain function following MA sensitization.

METHODS

Sensitization was induced in rats, then temporal and recognition memory were assessed after 1 or 30 days of abstinence. Electrophysiological recordings from the medial PFC were also acquired from rats whereupon simultaneous measures of oscillatory and spiking activity were examined.

RESULTS

Impaired temporal memory was observed after 1 and 30 days of abstinence. However, recognition memory was only impaired after 1 day of abstinence. An injection of MA profoundly decreased neuronal firing rate and the anesthesia-induced slow oscillation (SO) in both sensitized (SENS) and control (CTRL) rats. Strong correlations were observed between the SO and gamma band power, which was altered in SENS animals. A decrease in the number of neurons phase-locked to the gamma oscillation was also observed in SENS animals.

CONCLUSIONS

The changes observed in PFC function may play an integral role in the expression of the altered behavioral phenotype evoked by MA sensitization.

Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform

Slow waves (<; 1 Hz) are considered to be the most important electroencephalogram (EEG) signature of non-rapid eye movement sleep and have substantial physiological importance. In addition to natural sleep, slow waves can be seen in the EEG during general anesthesia offering great potential for depth of anesthesia monitoring. In this paper, Hilbert-Huang Transform, an adaptive data-driven method designed for the analysis on non-stationary data, was used to investigate the dynamical changes in the EEG slow wave activity during induction of anesthesia with propofol. The method was found to be able to extract stable signal components representing slow wave activity that were consistent between patients. The signal analysis revealed a possible specific structure between different components dependent on the depth of anesthesia on which further studies are needed.

Re-encoding of associations by recurrent plasticity increases memory capacity

Recurrent networks have been proposed as a model of associative memory. In such models, memory items are stored in the strength of connections between neurons. These modifiable connections or synapses constitute a shared resource among all stored memories, limiting the capacity of the network. Synaptic plasticity at different time scales can play an important role in optimizing the representation of associative memories, by keeping them sparse, uncorrelated and non-redundant. Here, we use a model of sequence memory to illustrate how plasticity allows a recurrent network to self-optimize by gradually re-encoding the representation of its memory items. A learning rule is used to sparsify large patterns, i.e., patterns with many active units. As a result, pattern sizes become more homogeneous, which increases the network's dynamical stability during sequence recall and allows more patterns to be stored. Last, we show that the learning rule allows for online learning in that it keeps the network in a robust dynamical steady state while storing new memories and overwriting old ones.

Brain electrical activity obeys Benford's law

BACKGROUND

Monitoring and automated online analysis of brain electrical activity are frequently used for verifying brain diseases and for estimating anesthetic depth in subjects undergoing surgery. However, false diagnosis with potentially catastrophic consequences for patients such as intraoperative awareness may result from unnoticed irregularities in the process of signal analysis. Here we ask whether Benford's Law can be applied to detect accidental or intended modulation of neurophysiologic signals. This law states that the first digits of many datasets such as atomic weights or river lengths are distributed logarithmically and not equally. In particular, we tested whether data obtained from electrophysiological recordings of human patients representing global activity and organotypic slice cultures representing pure cortical activity follow the predictions of Benford's Law in the absence and in the presence of an anesthetic drug.

METHODS

Electroencephalographic (EEG) recordings from human subjects and local field potential recordings from cultured cortical brain slices were obtained before and after administration of sevoflurane. The first digit distribution of the datasets was compared with the Benford distribution.

RESULTS

All datasets showed a Benford-like distribution. Nevertheless, distributions belonging to different anesthetic levels could be distinguished in vitro and in human EEGs. With sevoflurane, the first digit distribution of the in vitro data becomes steeper, while it flattens for EEG data. In the presence of high frequency noise, the Benford distribution falls apart.

CONCLUSIONS

In vitro and EEG data show a Benford-like distribution which is altered by sevoflurane or destroyed by noise used to simulate artefacts. These findings suggest that algorithms based on Benford's Law can be successfully used to detect sevoflurane-induced signal modulations in electrophysiological recordings.

The spatial representations acquired in CA3 by self-organizing recurrent connections

Neural computation models have hypothesized that the dentate gyrus (DG) drives the storage in the CA3 network of new memories including, e.g., in rodents, spatial memories. Can recurrent CA3 connections self-organize, during storage, and form what have been called continuous attractors, or charts—so that they express spatial information later, when aside from a partial cue the information may not be available in the inputs? We use a simplified mathematical network model to contrast the properties of spatial representations self-organized through simulated Hebbian plasticity with those of charts pre-wired in the synaptic matrix, a control case closer to the ideal notion of continuous attractors. Both models form granular quasi-attractors, characterized by drift, which approach continuous ones only in the limit of an infinitely large network. The two models are comparable in terms of precision, but not of accuracy: with self-organized connections, the metric of space remains distorted, ill-adequate for accurate path integration, even when scaled up to the real hippocampus. While prolonged self-organization makes charts somewhat more informative about position in the environment, some positional information is surprisingly present also about environments never learned, borrowed, as it were, from unrelated charts. In contrast, context discrimination decreases with more learning, as different charts tend to collapse onto each other. These observations challenge the feasibility of the idealized CA3 continuous chart concept, and are consistent with a CA3 specialization for episodic memory rather than path integration.

Sevoflurane-induced loss of consciousness is paralleled by a prominent modification of neural activity during cortical down-states

Networks of neocortical neurons display a bistable activity pattern characterised by phases of high frequency action potential firing, so called up-states, and episodes of low discharge activity (down-states). We hypothesised that during down-states neocortical neurons are vulnerable to anaesthetic agents. To tackle this issue, it is necessary to identify analytical methods, which are sufficiently sensitive for resolving anaesthetic effects during phases of scarce neuronal activity. The local field potential was recorded in organotypic cultures (OTC) from rat neocortex under control conditions and in the presence of increasing concentrations of sevoflurane by extracellular electrodes. Epochs from down-states were cut from the local field potential and analysed using power spectrum density as well as non-linear parameters approximate entropy (ApEn) and order recurrence rate (ORR). ApEn and ORR proved to be suitable tools for analysing the actions of volatile anaesthetics on cortical down-states. During these phases of low neuronal activity, sevoflurane caused prominent changes in the local field potential. Time series analysis using ApEn showed a reduction of signal predictability in the presence of sevoflurane. Furthermore, the ORR displayed an abrupt decrease at sevoflurane concentrations corresponding to loss of consciousness in vivo, indicating a drug-induced decrease in the signal to noise ratio. The actions of volatile anaesthetics on cortical down-states have been neglected so far, perhaps due to the lack of suitable analysis tools. In the current in vitro study the non-linear parameters ApEn and ORR are introduced to characterise volatile anaesthetics actions. Sevoflurane alters cortical down-states as indicated by non-linear parameter analysis of local field potential recording from cultured neuronal networks. ORR even displays an abrupt change, i.e., a step-like behaviour indicating an increased signal complexity at concentrations of sevoflurane corresponding to loss of consciousness in humans.

Gating of sensory input by spontaneous cortical activity

The activity of neural populations is determined not only by sensory inputs but also by internally generated patterns. During quiet wakefulness, the brain produces spontaneous firing events that can spread over large areas of cortex and have been suggested to underlie processes such as memory recall and consolidation. Here we demonstrate a different role for spontaneous activity in sensory cortex: gating of sensory inputs. We show that population activity in rat auditory cortex is composed of transient 50-100 ms packets of spiking activity that occur irregularly during silence and sustained tone stimuli, but reliably at tone onset. Population activity within these packets had broadly consistent spatiotemporal structure, but the rate and also precise relative timing of action potentials varied between stimuli. Packet frequency varied with cortical state, with desynchronized state activity consistent with superposition of multiple overlapping packets. We suggest that such packets reflect the sporadic opening of a "gate" that allows auditory cortex to broadcast a representation of external sounds to other brain regions.

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.

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

Cortical circuits spontaneously generate coordinated activity even in the absence of external inputs. The character of this activity depends on cortical state. We investigated how state affects the organization of spontaneous activity across layers of rat auditory cortex in vivo, using juxtacellular recording of morphologically identified neurons and large-scale electrophysiological recordings. Superficial pyramidal cells (PCs) and putative fast-spiking interneurons (FSs) were consistently suppressed during cortical desynchronization. PCs in deep layers showed heterogeneous responses to desynchronization, with some cells showing increased rates, typically large tufted PCs of high baseline firing rate, but not FSs. Consistent results were found between desynchronization occurring spontaneously in unanesthetized animals, and desynchronization evoked by electrical stimulation of the pedunculopontine tegmental (PPT) nucleus under urethane anesthesia. We hypothesize that reduction in superficial layer firing may enhance the brain's extraction of behaviorally relevant signals from noisy brain activity.

Rewiring-induced chaos in pulse-coupled neural networks

The dependence of the dynamics of pulse-coupled neural networks on random rewiring of excitatory and inhibitory connections is examined. When both excitatory and inhibitory connections are rewired, periodic synchronization emerges with a Hopf-like bifurcation and a subsequent period-doubling bifurcation; chaotic synchronization is also observed. When only excitatory connections are rewired, periodic synchronization emerges with a saddle node-like bifurcation, and chaotic synchronization is also observed. This result suggests that randomness in the system does not necessarily contaminate the system, and sometimes it even introduces rich dynamics to the system such as chaos.

A Flicker Change Detection Task Reveals Object-in-Scene Memory Across Species

Tests of recognition memory in macaques typically assay memory for objects or isolated images, over time spans of seconds to hours from stimulus presentation, and/or require extensive training. Here, we propose a new application of the flicker change detection task that could measure object-in-scene memory days after single-trial exposures. In three experiments, participants searched for a changing object – or “target” – embedded within a scene as their eye movements were tracked. For new targets-in-scenes, the change is difficult to detect and requires extensive search. Once the target is found, however, the change becomes obvious. We reasoned that the decreased times required to find a target in a repeated scene would indicate memory for the target. In humans, targets were found faster when the targets-and-scenes were explicitly remembered than when they were forgotten, or had never been seen before. This led to faster repeated-trial compared to novel-trial search times. Based solely on repeated-trial search times, we were able to select distributions comprised of predominantly remembered or predominantly forgotten trials. Macaques exhibited the same repetition effects as humans, suggesting that remembered trials could be dissociated from novel or forgotten trials using the same procedures we established in humans. Finally, an anterograde amnesic patient with damage that included the medial temporal lobe (MTL) showed no search time differences, suggesting that memory revealed through search times on this task requires MTL integrity. Together, these findings indicate that the time required to locate a changing object reveals object-in-scene memory over long retention intervals in humans and macaques.

From sleep spindles of natural sleep to spike and wave discharges of typical absence seizures: is the hypothesis still valid?

The temporal coincidence of sleep spindles and spike-and-wave discharges (SWDs) in patients with idiopathic generalized epilepsies, together with the transformation of spindles into SWDs following intramuscular injection of the weak GABAA receptor (GABAAR) antagonist, penicillin, in an experimental model, brought about the view that SWDs may represent ‘perverted’ sleep spindles. Over the last 20 years, this hypothesis has received considerable support, in particular by in vitro studies of thalamic oscillations following pharmacological/genetic manipulations of GABAARs. However, from a critical appraisal of the evidence in absence epilepsy patients and well-established models of absence epilepsy it emerges that SWDs can occur as frequently during wakefulness as during sleep, with their preferential occurrence in either one of these behavioural states often being patient dependent. Moreover, whereas the EEG expression of both SWDs and sleep spindles requires the integrity of the entire cortico-thalamo-cortical network, SWDs initiates in cortex while sleep spindles in thalamus. Furthermore, the hypothesis of a reduction in GABAAR function across the entire cortico-thalamo-cortical network as the basis for the transformation of sleep spindles into SWDs is no longer tenable. In fact, while a decreased GABAAR function may be present in some cortical layers and in the reticular thalamic nucleus, both phasic and tonic GABAAR inhibitions of thalamo-cortical neurons are either unchanged or increased in this epileptic phenotype. In summary, these differences between SWDs and sleep spindles question the view that the EEG hallmark of absence seizures results from a transformation of this EEG oscillation of natural sleep.

Stereotypical spatiotemporal activity patterns during slow-wave activity in the neocortex

Alternating epochs of activity and silence are a characteristic feature of neocortical networks during certain sleep cycles and deep states of anesthesia. The mechanism and functional role of these slow oscillations (<1 Hz) have not yet been fully characterized. Experimental and theoretical studies show that slow-wave oscillations can be generated autonomously by neocortical tissue but become more regular through a thalamo-cortical feedback loop. Evidence for a functional role of slow-wave activity comes from EEG recordings in humans during sleep, which show that activity travels as stereotypical waves over the entire brain, thought to play a role in memory consolidation. We used an animal model to investigate activity wave propagation on a smaller scale, namely within the rat somatosensory cortex. Signals from multiple extracellular microelectrodes in combination with one intracellular recording in the anesthetized animal in vivo were utilized to monitor the spreading of activity. We found that activity propagation in most animals showed a clear preferred direction, suggesting that it often originated from a similar location in the cortex. In addition, the breakdown of active states followed a similar pattern with slightly weaker direction preference but a clear correlation to the direction of activity spreading, supporting the notion of a wave-like phenomenon similar to that observed after strong sensory stimulation in sensory areas. Taken together, our findings support the idea that activity waves during slow-wave sleep do not occur spontaneously at random locations within the network, as was suggested previously, but follow preferred synaptic pathways on a small spatial scale.

Spatiotemporal scales and links between electrical neuroimaging modalities

Recordings of brain electrophysiological activity provide the most direct reflect of neural function. Information contained in these signals varies as a function of the spatial scale at which recordings are done: from single cell recording to large scale macroscopic fields, e.g., scalp EEG. Microscopic and macroscopic measurements and models in Neuroscience are often in conflict. Solving this conflict might require the developments of a sort of bio-statistical physics, a framework for relating the microscopic properties of individual cells to the macroscopic or bulk properties of neural circuits. Such a framework can only emerge in Neuroscience from the systematic analysis and modeling of the diverse recording scales from simultaneous measurements. In this article we briefly review the different measurement scales and models in modern neuroscience to try to identify the sources of conflict that might ultimately help to create a unified theory of brain electromagnetic fields. We argue that seen the different recording scales, from the single cell to the large scale fields measured by the scalp electroencephalogram, as derived from a unique physical magnitude--the electric potential that is measured in all cases--might help to conciliate microscopic and macroscopic models of neural function as well as the animal and human neuroscience literature.

A molecular basis for interactions between sleep and memory

The electrophysiological properties of the sleeping brain profoundly influence memory function in various species, yet the molecular nature by which sleep and memory interact remains unclear. We summarize work that has established the cAMP-PKA-CREB intracellular signaling pathway as a major mechanism involved in the wakeful consolidation of memory in many organisms while highlighting newer evidence that this pathway has a role in sleep regulation, sleep deprivation and potentially sleep-memory interactions. We explore the possibility that sleep might influence memory processing by reactivating the same molecular cascades first recruited during learning during a sort of "molecular replay". Lastly, we discuss how new approaches together with established techniques will aid in our understanding of the nature of sleep-memory interactions.

Slow intrinsic oscillations in thick neocortical slices of hypoxia tolerant deep diving seals

Direct evidence that the mammalian neocortex is an important generator of intrinsic activity comes from isolated neocortical slices that spontaneously generate multiple rhythms including those in the beta, delta and gamma range. These oscillations are also seen in intact animals where they interact with other areas including the hippocampus, thalamus and basal ganglia. Here we show that thick isolated neocortical slices from hooded seals intrinsically generate persistent spontaneous activities, both repetitive non-rhythmic activity with activity states lasting for several minutes, and oscillating activity with rhythms that are much slower (<0.1 Hz) than the rhythms previously described in vitro. These intrinsic activities were very robust and persisted for up to 1 h even in severely hypoxic conditions. We hypothesize that the remarkable hypoxia tolerance of the hooded seal nervous system made it possible to maintain functional integrity in slices thick enough to preserve intact neuronal networks capable of generating these slow oscillations. The observed activities in seal neocortical slices support the notion that mammalian cortical networks intrinsically generate multiple states of activity that include oscillatory activity all the way down to <0.1 Hz. This intrinsic neocortical excitability is an important contributor not only to sleep but also to the default awake state of the neocortex.

Endogenous electric fields may guide neocortical network activity

Local field potentials and the underlying endogenous electric fields (EFs) are traditionally considered to be epiphenomena of structured neuronal network activity. Recently, however, externally applied EFs have been shown to modulate pharmacologically evoked network activity in rodent hippocampus. In contrast, very little is known about the role of endogenous EFs during physiological activity states in neocortex. Here, we used the neocortical slow oscillation in vitro as a model system to show that weak sinusoidal and naturalistic EFs enhance and entrain physiological neocortical network activity with an amplitude threshold within the range of in vivo endogenous field strengths. Modulation of network activity by positive and negative feedback fields based on the network activity in real-time provide direct evidence for a feedback loop between neuronal activity and endogenous EF. This significant susceptibility of active networks to EFs that only cause small changes in membrane potential in individual neurons suggests that endogenous EFs could guide neocortical network 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.

Mirrored bilateral slow-wave cortical activity within local circuits revealed by fast bihemispheric voltage-sensitive dye imaging in anesthetized and awake mice

Spontaneous slow-wave oscillations of neuronal membrane potential occur about once every second in the rodent cortex and may serve to shape the efficacy of evoked neuronal responses and consolidate memory during sleep. However, whether these oscillations reflect the entrainment of all cortical regions via propagating waves or whether they exhibit regional and temporal heterogeneity that reflects processing in local cortical circuits is unknown. Using voltage-sensitive dye (VSD) imaging within an adult C57BL/6J mouse cross-midline large craniotomy preparation, we recorded this depolarizing activity across most of both cortical hemispheres simultaneously in both anesthetized and quiet awake animals. Spontaneous oscillations in the VSD signal were highly synchronized between hemispheres, and acallosal I/LnJ mice indicated that synchrony depended on the corpus callosum. In both anesthetized and awake mice (recovered from anesthesia), the oscillations were not necessarily global changes in activity state but were made up of complex local patterns characterized by multiple discrete peaks that were unevenly distributed across cortex. Although the local patterns of depolarizing activity were complex and changed over tens of milliseconds, they were faithfully mirrored in both hemispheres in mice with an intact corpus callosum, to perhaps ensure parallel modification of related circuits in both hemispheres. We conclude that within global rhythms of spontaneous activity are complex events that reflect orchestrated processing within local cortical circuits.

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.

Low-frequency-induced synaptic potentiation: a paradigm shift in the field of memory-related plasticity mechanisms?

Long-term potentiation (LTP) and long-term depression (LTD) are two forms of synaptic plasticity thought to play functional roles in learning and memory processes. It is generally assumed that the direction of synaptic modifications (i.e., up- or down-regulation of synaptic strength) depends on the specific pattern of afferent inputs, with high frequency activity or stimulation effectively inducing LTP, while low-frequency patterns often elicit LTD. This dogma ("high frequency-LTP, low frequency-LTD") has recently been challenged by evidence demonstrating low frequency stimulation (LFS)-induced synaptic potentiation in the rodent hippocampus and amygdala. Extensive work in the past decades has focused on deciphering the mechanisms by which high frequency stimulation of afferent fiber systems results in LTP. With this review, we will compare and contrast the well-known synaptic and cellular mechanisms underlying classical, high-frequency-induced LTP to those mediating the more recently discovered phenomena of LFS-induced synaptic enhancement. In addition, we argue that LFS protocols provide a means to more accurately mimic some endogenous, oscillatory activity patterns present in hippocampal and extra-hippocampal (especially neocortical) circuits during periods of memory consolidation. Consequently, LFS-induced synaptic potentiation offers a novel and important avenue to investigate cellular and systems-level mechanisms mediating the encoding, consolidation, and transfer of information throughout multiple forebrain networks implicated in learning and memory processes. (c) 2009 Wiley-Liss, Inc.

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.

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.

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.

Slow oscillation electrical brain stimulation during waking promotes EEG theta activity and memory encoding

The application of transcranial slow oscillation stimulation (tSOS; 0.75 Hz) was previously shown to enhance widespread endogenous EEG slow oscillatory activity when applied during a sleep period characterized by emerging endogenous slow oscillatory activity. Processes of memory consolidation typically occurring during this state of sleep were also enhanced. Here, we show that the same tSOS applied in the waking brain also induced an increase in endogenous EEG slow oscillations (0.4-1.2 Hz), although in a topographically restricted fashion. Applied during wakefulness tSOS, additionally, resulted in a marked and widespread increase in EEG theta (4-8 Hz) activity. During wake, tSOS did not enhance consolidation of memories when applied after learning, but improved encoding of hippocampus-dependent memories when applied during learning. We conclude that the EEG frequency and related memory processes induced by tSOS critically depend on brain state. In response to tSOS during wakefulness the brain transposes stimulation by responding preferentially with theta oscillations and facilitated encoding.

Sleep function: current questions and new approaches

The mammalian brain oscillates through three distinct global activity states: wakefulness, non-rapid eye movement (NREM) sleep and REM sleep. The regulation and function of these 'vigilance' or 'behavioural' states can be investigated over a broad range of temporal and spatial scales and at different levels of functional organization, i.e. from gene expression to memory, in single neurons, cortical columns or the whole brain and organism. We summarize some basic questions that have arisen from recent approaches in the quest for the functions of sleep. Whereas traditionally sleep was viewed to be regulated through top-down control mechanisms, recent approaches have emphasized that sleep is emerging locally and regulated in a use-dependent (homeostatic) manner. Traditional markers of sleep homeostasis, such as the electroencephalogram slow-wave activity, have been linked to changes in connectivity and plasticity in local neuronal networks. Thus waking experience-induced local network changes may be sensed by the sleep homeostatic process and used to mediate sleep-dependent events, benefiting network stabilization and memory consolidation. Although many questions remain unanswered, the available data suggest that sleep function will best be understood by an analysis which integrates sleep's many functional levels with its local homeostatic regulation.

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.

From creation to consolidation: a novel framework for memory processing

Long after playing squash, your brain continues to process the events that occurred during the game, thereby improving your game, and more generally, enhancing adaptive behavior. Understanding these mysterious processes may require novel theories.

All asleep-but inhibition is wide awake

Depending on the arousal state, neuronal networks display discrete activity patterns that profoundly affect information processing in the brain. In this issue of Neuron, Kurotani et al. report bidirectional modification of inhibition by oscillatory patterns in the neocortex; a mechanism likely to control the impact of these neurons in a state-dependent fashion.