Up and down states

Adaptive rewiring: a general principle for neural network development

The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.e., the dynamic reorganization of a network according to the intensity of internal signal communication as measured by synchronization or diffusion, and its recent generalization for applications in directed networks. These have extended the principle of adaptive rewiring from highly oversimplified networks to more neurally plausible ones. Adaptive rewiring captures all the key features of the complex brain topology: it transforms initially random or regular networks into networks with a modular small-world structure and a rich-club core. This effect is specific in the sense that it can be tailored to computational needs, robust in the sense that it does not depend on a critical regime, and flexible in the sense that parametric variation generates a range of variant network configurations. Extreme variant networks can be associated at macroscopic level with disorders such as schizophrenia, autism, and dyslexia, and suggest a relationship between dyslexia and creativity. Adaptive rewiring cooperates with network growth and interacts constructively with spatial organization principles in the formation of topographically distinct modules and structures such as ganglia and chains. At the mesoscopic level, adaptive rewiring enables the development of functional architectures, such as convergent-divergent units, and sheds light on the early development of divergence and convergence in, for example, the visual system. Finally, we discuss future prospects for the principle of adaptive rewiring.

Brains are Probabilistic, Electrophysiologically Intricate and Triune: A Biased- Random Walk Perspective on Computational Neuroscience

The pursuit of a unified theory that captures the intricacies of the brain and mind continues to be a significant challenge in theoretical neuroscience. This paper presents a novel, triune framework that utilizes the concept of collective biased random walk (cBRW). Our approach strives to transcend biological specifics, offering a high-level abstraction that remains general and applicable across various neural phenomena. Despite the solid traditional foundation of computational neuroscience, the intricate delicacy of neural processes calls for a renewed probabilistic approach. We aim to utilize the intuitive nature of probability concepts -such as the probability of localization and state, and uniform probability distribution- to study the stochastic organization of electric charges and signals in the brain. This electrophysiological intricacy emerges from the seemingly paradoxical reality that tiny electric events, while random, collectively give rise to predictable, long-range oscillations. These oscillations manifest in three groups of activation states. Our framework categorizes the brain as a triune system, accommodating classical, semiclassical, and non-classical interpretations of both probabilistic phenomena and cBRW models, alongside three groups of states. We conclude that by appreciating, rather than overlooking, the tiny random walks of electric charges and signals in the brain, we can gain a triune mathematical foundation for theoretical brain science, the powerful capabilities of this organ, and the electromagnetic interfaces we can develop.

Short-term Hebbian learning can implement transformer-like attention

Transformers have revolutionized machine learning models of language and vision, but their connection with neuroscience remains tenuous. Built from attention layers, they require a mass comparison of queries and keys that is difficult to perform using traditional neural circuits. Here, we show that neurons can implement attention-like computations using short-term, Hebbian synaptic potentiation. We call our mechanism the match-and-control principle and it proposes that when activity in an axon is synchronous, or matched, with the somatic activity of a neuron that it synapses onto, the synapse can be briefly strongly potentiated, allowing the axon to take over, or control, the activity of the downstream neuron for a short time. In our scheme, the keys and queries are represented as spike trains and comparisons between the two are performed in individual spines allowing for hundreds of key comparisons per query and roughly as many keys and queries as there are neurons in the network.

Inferring causal connectivity from pairwise recordings and optogenetics

To understand the neural mechanisms underlying brain function, neuroscientists aim to quantify causal interactions between neurons, for instance by perturbing the activity of neuron A and measuring the effect on neuron B. Recently, manipulating neuron activity using light-sensitive opsins, optogenetics, has increased the specificity of neural perturbation. However, using widefield optogenetic interventions, multiple neurons are usually perturbed, producing a confound-any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which neurons produced the causal effect. Here, we show how such confounds produce large biases in interpretations. We explain how confounding can be reduced by combining instrumental variables (IV) and difference in differences (DiD) techniques from econometrics. Combined, these methods can estimate (causal) effective connectivity by exploiting the weak, approximately random signal resulting from the interaction between stimulation and the absolute refractory period of the neuron. In simulated neural networks, we find that estimates using ideas from IV and DiD outperform naïve techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.

Compressive sensing of functional connectivity maps from patterned optogenetic stimulation of neuronal ensembles

Mapping functional connectivity between neurons is an essential step toward probing the neural computations mediating behavior. Accurately determining synaptic connectivity maps in populations of neurons is challenging in terms of yield, accuracy, and experimental time. Here, we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two-photon cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we characterized mapping recall and precision as a function of network observability, sparsity, number of neurons stimulated, off-target stimulation, synaptic reliability, propagation latency, and network topology. We found that mapping can be achieved with far fewer measurements than the standard pairwise sequential approach, with network sparsity and synaptic reliability serving as primary determinants of the performance. Our results suggest a rapid and efficient method to reconstruct functional connectivity of sparsely connected neuronal networks.

Revisiting neural information, computing and linking capacity

Neural information theory represents a fundamental method to model dynamic relations in biological systems. However, the notion of information, its representation, its content and how it is processed are the subject of fierce debates. Since the limiting capacity of neuronal links strongly depends on how neurons are hypothesized to work, their operating modes are revisited by analyzing the differences between the results of the communication models published during the past seven decades and those of the recently developed generalization of the classical information theory. It is pointed out that the operating mode of neurons is in resemblance with an appropriate combination of the formerly hypothesized analog and digital working modes; furthermore that not only the notion of neural information and its processing must be reinterpreted. Given that the transmission channel is passive in Shannon's model, the active role of the transfer channels (the axons) may introduce further transmission limits in addition to the limits concluded from the information theory. The time-aware operating model enables us to explain why (depending on the researcher's point of view) the operation can be considered either purely analog or purely digital.

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 10⁶ neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.

Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.

The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

Understanding the brain’s function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanity’s great undertakings. In experimental and clinical work probing the brain’s activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

Seasonal changes in membrane structure and excitability in retinal neurons of goldfish (Carassius auratus) under constant environmental conditions

Seasonal modifications in the structure of cellular membranes occur as an adaptive measure to withstand exposure to prolonged environmental change. Little is known about whether such changes may occur independently of external cues, such as photoperiod or temperature, or how they may impact the central nervous system. We compared membrane properties of neurons isolated from the retina of goldfish (Carassius auratus), an organism well-adapted to extreme environmental change, during the summer and winter months. Goldfish were maintained in a facility under constant environmental conditions throughout the year. Analysis of whole-retina phospholipid composition using mass spectrometry-based lipidomics revealed a two-fold increase in phosphatidylethanolamine species during the winter, suggesting an increase in cell membrane fluidity. Atomic force microscopy was used to produce localized, nanoscale-force deformation of neuronal membranes. Measurement of Young's modulus indicated increased membrane-cortical stiffness (or decreased elasticity) in neurons isolated during the winter. Voltage-clamp electrophysiology was used to assess physiological changes in neurons between seasons. Winter neurons displayed a hyperpolarized reversal potential (Vrev) and a significantly lower input resistance (Rin) compared to summer neurons. This was indicative of a decrease in membrane excitability during the winter. Subsequent measurement of intracellular Ca2+ activity using Fura-2 microspectrofluorometry confirmed a reduction in action potential activity, including duration and action potential profile, in neurons isolated during the winter. These studies demonstrate chemical and biophysical changes that occur in retinal neurons of goldfish throughout the year without exposure to seasonal cues, and suggest a novel mechanism of seasonal regulation of retinal activity.

Relationships between correlated spikes, oxygen and LFP in the resting-state primate

Resting-state functional MRI (rsfMRI) provides a view of human brain organization based on correlation patterns of blood oxygen level dependent (BOLD) signals recorded across the whole brain. The neural basis of resting-state BOLD fluctuations and their correlation remains poorly understood. We simultaneously recorded oxygen level, spikes, and local field potential (LFP) at multiple sites in awake, resting monkeys. Following a spike, the average local oxygen and LFP voltage responses each resemble a task-driven BOLD response, with LFP preceding oxygen by 0.5 s. Between sites, features of the long-range correlation patterns of oxygen, LFP, and spikes are similar to features seen in rsfMRI. Most of the variance shared between sites lies in the infraslow frequency band (0.01-0.1 Hz) and in the infraslow envelope of higher-frequency bands (e.g. gamma LFP). While gamma LFP and infraslow LFP are both strong correlates of local oxygen, infraslow LFP explains significantly more of the variance shared between correlated oxygen signals than any other electrophysiological signal. Together these findings are consistent with a causal relationship between infraslow LFP and long-range oxygen correlations in the resting state.

Preventing neuronal edema increases network excitability after traumatic brain injury

Edema is an important target for clinical intervention after traumatic brain injury (TBI). We used in vivo cellular resolution imaging and electrophysiological recording to examine the ionic mechanisms underlying neuronal edema and their effects on neuronal and network excitability after controlled cortical impact (CCI) in mice. Unexpectedly, we found that neuronal edema 48 hours after CCI was associated with reduced cellular and network excitability, concurrent with an increase in the expression ratio of the cation-chloride cotransporters (CCCs) NKCC1 and KCC2. Treatment with the CCC blocker bumetanide prevented neuronal swelling via a reversal in the NKCC1/KCC2 expression ratio, identifying altered chloride flux as the mechanism of neuronal edema. Importantly, bumetanide treatment was associated with increased neuronal and network excitability after injury, including increased susceptibility to spreading depolarizations (SDs) and seizures, known agents of clinical worsening after TBI. Treatment with mannitol, a first-line edema treatment in clinical practice, was also associated with increased susceptibility to SDs and seizures after CCI, showing that neuronal volume reduction, regardless of mechanism, was associated with an excitability increase. Finally, we observed an increase in excitability when neuronal edema normalized by 1 week after CCI. We conclude that neuronal swelling may exert protective effects against damaging excitability in the aftermath of TBI and that treatment of edema has the potential to reverse these effects.

High-voltage, diffuse delta rhythms coincide with wakeful consciousness and complexity in Angelman syndrome

Abundant evidence from slow wave sleep, anesthesia, coma, and epileptic seizures links high-voltage, slow electroencephalogram (EEG) activity to loss of consciousness. This well-established correlation is challenged by the observation that children with Angelman syndrome (AS), while fully awake and displaying volitional behavior, display a hypersynchronous delta (1–4 Hz) frequency EEG phenotype typical of unconsciousness. Because the trough of the delta oscillation is associated with down-states in which cortical neurons are silenced, the presence of volitional behavior and wakefulness in AS amidst diffuse delta rhythms presents a paradox. Moreover, high-voltage, slow EEG activity is generally assumed to lack complexity, yet many theories view functional brain complexity as necessary for consciousness. Here, we use abnormal cortical dynamics in AS to assess whether EEG complexity may scale with the relative level of consciousness despite a background of hypersynchronous delta activity. As characterized by multiscale metrics, EEGs from 35 children with AS feature significantly greater complexity during wakefulness compared with sleep, even when comparing the most pathological segments of wakeful EEG to the segments of sleep EEG least likely to contain conscious mentation and when factoring out delta power differences across states. These findings (i) warn against reverse inferring an absence of consciousness solely on the basis of high-amplitude EEG delta oscillations, (ii) corroborate rare observations of preserved consciousness under hypersynchronization in other conditions, (iii) identify biomarkers of consciousness that have been validated under conditions of abnormal cortical dynamics, and (iv) lend credence to theories linking consciousness with complexity.

Oscillations in the auditory system and their possible role

GOURÉVITCH, B., C. Martin, O. Postal, J.J. Eggermont. Oscillations in the auditory system, their possible role. NEUROSCI BIOBEHAV REV XXX XXX-XXX, 2020. - Neural oscillations are thought to have various roles in brain processing such as, attention modulation, neuronal communication, motor coordination, memory consolidation, decision-making, or feature binding. The role of oscillations in the auditory system is less clear, especially due to the large discrepancy between human and animal studies. Here we describe many methodological issues that confound the results of oscillation studies in the auditory field. Moreover, we discuss the relationship between neural entrainment and oscillations that remains unclear. Finally, we aim to identify which kind of oscillations could be specific or salient to the auditory areas and their processing. We suggest that the role of oscillations might dramatically differ between the primary auditory cortex and the more associative auditory areas. Despite the moderate presence of intrinsic low frequency oscillations in the primary auditory cortex, rhythmic components in the input seem crucial for auditory processing. This allows the phase entrainment between the oscillatory phase and rhythmic input, which is an integral part of stimulus selection within the auditory system.

Computational Modeling of Genetic Contributions to Excitability and Neural Coding in Layer V Pyramidal Cells: Applications to Schizophrenia Pathology

Pyramidal cells in layer V of the neocortex are one of the most widely studied neuron types in the mammalian brain. Due to their role as integrators of feedforward and cortical feedback inputs, they are well-positioned to contribute to the symptoms and pathology in mental disorders-such as schizophrenia-that are characterized by a mismatch between the internal perception and external inputs. In this modeling study, we analyze the input/output properties of layer V pyramidal cells and their sensitivity to modeled genetic variants in schizophrenia-associated genes. We show that the excitability of layer V pyramidal cells and the way they integrate inputs in space and time are altered by many types of variants in ion-channel and Ca²⁺ transporter-encoding genes that have been identified as risk genes by recent genome-wide association studies. We also show that the variability in the output patterns of spiking and Ca²⁺ transients in layer V pyramidal cells is altered by these model variants. Importantly, we show that many of the predicted effects are robust to noise and qualitatively similar across different computational models of layer V pyramidal cells. Our modeling framework reveals several aspects of single-neuron excitability that can be linked to known schizophrenia-related phenotypes and existing hypotheses on disease mechanisms. In particular, our models predict that single-cell steady-state firing rate is positively correlated with the coding capacity of the neuron and negatively correlated with the amplitude of a prepulse-mediated adaptation and sensitivity to coincidence of stimuli in the apical dendrite and the perisomatic region of a layer V pyramidal cell. These results help to uncover the voltage-gated ion-channel and Ca²⁺ transporter-associated genetic underpinnings of schizophrenia phenotypes and biomarkers.

Operating in a Reverberating Regime Enables Rapid Tuning of Network States to Task Requirements

Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a “reverberating regime” because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call “dynamic adaptive computation,” presents a central organization principle of cortical networks and discuss first experimental evidence.

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise : Spontaneous activity in networks with synaptic noise

Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.

Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience

Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.

Embedded ensemble encoding hypothesis: The role of the "Prepared" cell

We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.

Acid-base physiology, neurobiology and behaviour in relation to CO2-induced ocean acidification

Experimental exposure to ocean and freshwater acidification affects the behaviour of multiple aquatic organisms in laboratory tests. One proposed cause involves an imbalance in plasma chloride and bicarbonate ion concentrations as a result of acid-base regulation, causing the reversal of ionic fluxes through GABA_A receptors, which leads to altered neuronal function. This model is exclusively based on differential effects of the GABA_A receptor antagonist gabazine on control animals and those exposed to elevated CO₂ However, direct measurements of actual chloride and bicarbonate concentrations in neurons and their extracellular fluids and of GABA_A receptor properties in aquatic organisms are largely lacking. Similarly, very little is known about potential compensatory mechanisms, and about alternative mechanisms that might lead to ocean acidification-induced behavioural changes. This article reviews the current knowledge on acid-base physiology, neurobiology, pharmacology and behaviour in relation to marine CO₂-induced acidification, and identifies important topics for future research that will help us to understand the potential effects of predicted levels of aquatic acidification on organisms.

Mechanisms of Neuronal Silencing After Cortical Spreading Depression

Cortical spreading depression (CSD) is associated with migraine, stroke, and traumatic brain injury, but its mechanisms remain poorly understood. One of the major features of CSD is an hour-long silencing of neuronal activity. Though this silencing has clear ramifications for CSD-associated disease, it has not been fully explained. We used in vivo whole-cell recordings to examine the effects of CSD on layer 2/3 pyramidal neurons in mouse somatosensory cortex and used in vitro recordings to examine their mechanism. We found that CSD caused a reduction in spontaneous synaptic activity and action potential (AP) firing that lasted over an hour. Both pre- and postsynaptic mechanisms contributed to this silencing. Reductions in frequency of postsynaptic potentials were due to a reduction in presynaptic transmitter release probability as well as reduced AP activity. Decreases in postsynaptic potential amplitude were due to an inhibitory shift in the ratio of excitatory and inhibitory postsynaptic currents. This inhibitory shift in turn contributed to the reduced frequency of APs. Thus, distinct but complementary mechanisms generate the long neuronal silence that follows CSD. These cellular changes could contribute to wider network dysfunction in CSD-associated disease, while the pre- and postsynaptic mechanisms offer separate targets for therapy.

On the temporal organization of neuronal avalanches

Spontaneous activity of cortex in vitro and in vivo has been shown to organize as neuronal avalanches. Avalanches are cascades of neuronal activity that exhibit a power law in their size and duration distribution, typical features of balanced systems in a critical state. Recently it has been shown that the distribution of quiet times between consecutive avalanches in rat cortex slice cultures displays a non-monotonic behavior with a power law decay at short time scales. This behavior has been attributed to the slow alternation between up and down-states. Here we further characterize the avalanche process and investigate how the functional behavior of the quiet time distribution depends on the fine structure of avalanche sequences. By systematically removing smaller avalanches from the experimental time series we show that size and quiet times are correlated and highlight that avalanche occurrence exhibits the characteristic periodicity of θ and β/γ oscillations, which jointly emerge in most of the analyzed samples. Furthermore, our analysis indicates that smaller avalanches tend to be associated with faster β/γ oscillations, whereas larger ones are associated with slower θ and 1-2 Hz oscillations. In particular, large avalanches corresponding to θ cycles trigger cascades of smaller ones, which occur at β/γ frequency. This temporal structure follows closely the one of nested θ - β/γ oscillations. Finally we demonstrate that, because of the multiple time scales characterizing avalanche dynamics, the distributions of quiet times between avalanches larger than a certain size do not collapse onto a unique function when rescaled by the average occurrence rate. However, when considered separately in the up-state and in the down-state, these distributions are solely controlled by the respective average rate and two different unique function can be identified.

Spiny neurons of amygdala, striatum, and cortex use dendritic plateau potentials to detect network UP states

Spiny neurons of amygdala, striatum, and cerebral cortex share four interesting features: (1) they are the most abundant cell type within their respective brain area, (2) covered by thousands of thorny protrusions (dendritic spines), (3) possess high levels of dendritic NMDA conductances, and (4) experience sustained somatic depolarizations in vivo and in vitro (UP states). In all spiny neurons of the forebrain, adequate glutamatergic inputs generate dendritic plateau potentials (“dendritic UP states”) characterized by (i) fast rise, (ii) plateau phase lasting several hundred milliseconds, and (iii) abrupt decline at the end of the plateau phase. The dendritic plateau potential propagates toward the cell body decrementally to induce a long-lasting (longer than 100 ms, most often 200–800 ms) steady depolarization (∼20 mV amplitude), which resembles a neuronal UP state. Based on voltage-sensitive dye imaging, the plateau depolarization in the soma is precisely time-locked to the regenerative plateau potential taking place in the dendrite. The somatic plateau rises after the onset of the dendritic voltage transient and collapses with the breakdown of the dendritic plateau depolarization. We hypothesize that neuronal UP states in vivo reflect the occurrence of dendritic plateau potentials (dendritic UP states). We propose that the somatic voltage waveform during a neuronal UP state is determined by dendritic plateau potentials. A mammalian spiny neuron uses dendritic plateau potentials to detect and transform coherent network activity into a ubiquitous neuronal UP state. The biophysical properties of dendritic plateau potentials allow neurons to quickly attune to the ongoing network activity, as well as secure the stable amplitudes of successive UP states.

Connexin hemichannels contribute to spontaneous electrical activity in the human fetal cortex

Before the human cortex is able to process sensory information, young postmitotic neurons must maintain occasional bursts of action-potential firing to attract and keep synaptic contacts, to drive gene expression, and to transition to mature membrane properties. Before birth, human subplate (SP) neurons are spontaneously active, displaying bursts of electrical activity (plateau depolarizations with action potentials). Using whole-cell recordings in acute cortical slices, we investigated the source of this early activity. The spontaneous depolarizations in human SP neurons at midgestation (17-23 gestational weeks) were not completely eliminated by tetrodotoxin--a drug that blocks action potential firing and network activity--or by antagonists of glutamatergic, GABAergic, or glycinergic synaptic transmission. We then turned our focus away from standard chemical synapses to connexin-based gap junctions and hemichannels. PCR and immunohistochemical analysis identified the presence of connexins (Cx26/Cx32/Cx36) in the human fetal cortex. However, the connexin-positive cells were not found in clusters but, rather, were dispersed in the SP zone. Also, gap junction-permeable dyes did not diffuse to neighboring cells, suggesting that SP neurons were not strongly coupled to other cells at this age. Application of the gap junction and hemichannel inhibitors octanol, flufenamic acid, and carbenoxolone significantly blocked spontaneous activity. The putative hemichannel antagonist lanthanum alone was a potent inhibitor of the spontaneous activity. Together, these data suggest that connexin hemichannels contribute to spontaneous depolarizations in the human fetal cortex during the second trimester of gestation.

Patch-clamp recordings and calcium imaging followed by single-cell PCR reveal the developmental profile of 13 genes in iPSC-derived human neurons

Molecular genetic studies are typically performed on homogenized biological samples, resulting in contamination from non-neuronal cells. To improve expression profiling of neurons we combined patch recordings with single-cell PCR. Two iPSC lines (healthy subject and 22q11.2 deletion) were differentiated into neurons. Patch electrode recordings were performed on 229 human cells from Day-13 to Day-88, followed by capture and single-cell PCR for 13 genes: ACTB, HPRT, vGLUT1, βTUBIII, COMT, DISC1, GAD1, PAX6, DTNBP1, ERBB4, FOXP1, FOXP2, and GIRK2. Neurons derived from both iPSC lines expressed βTUBIII, fired action potentials, and experienced spontaneous depolarizations (UP states) ~2 weeks before vGLUT1, GAD1 and GIRK2 appeared. Multisite calcium imaging revealed that these UP states were not synchronized among hESC-H9-derived neurons. The expression of FOXP1, FOXP2 and vGLUT1 was lost after 50 days in culture, in contrast to other continuously expressed genes. When gene expression was combined with electrophysiology, two subsets of genes were apparent; those irrelevant to spontaneous depolarizations (including vGLUT1, GIRK2, FOXP2 and DISC1) and those associated with spontaneous depolarizations (GAD1 and ERBB4). The results demonstrate that in the earliest stages of neuron development, it is useful to combine genetic analysis with physiological characterizations, on a cell-to-cell basis.

Stochastic amplification of fluctuations in cortical up-states

Cortical neurons are bistable; as a consequence their local field potentials can fluctuate between quiescent and active states, generating slow Hz oscillations which are widely known as transitions between Up and Down States. Despite a large number of studies on Up-Down transitions, deciphering its nature, mechanisms and function are still today challenging tasks. In this paper we focus on recent experimental evidence, showing that a class of spontaneous oscillations can emerge within the Up states. In particular, a non-trivial peak around Hz appears in their associated power-spectra, what produces an enhancement of the activity power for higher frequencies (in the Hz band). Moreover, this rhythm within Ups seems to be an emergent or collective phenomenon given that individual neurons do not lock to it as they remain mostly unsynchronized. Remarkably, similar oscillations (and the concomitant peak in the spectrum) do not appear in the Down states. Here we shed light on these findings by using different computational models for the dynamics of cortical networks in presence of different levels of physiological complexity. Our conclusion, supported by both theory and simulations, is that the collective phenomenon of “stochastic amplification of fluctuations” – previously described in other contexts such as Ecology and Epidemiology – explains in an elegant and parsimonious manner, beyond model-dependent details, this extra-rhythm emerging only in the Up states but not in the Downs.

The decade of the dendritic NMDA spike

In the field of cortical cellular physiology, much effort has been invested in understanding thick apical dendrites of pyramidal neurons and the regenerative sodium and calcium spikes that take place in the apical trunk. Here we focus on thin dendrites of pyramidal cells (basal, oblique, and tuft dendrites), and we discuss one relatively novel form of an electrical signal ("NMDA spike") that is specific for these branches. Basal, oblique, and apical tuft dendrites receive a high density of glutamatergic synaptic contacts. Synchronous activation of 10-50 neighboring glutamatergic synapses triggers a local dendritic regenerative potential, NMDA spike/plateau, which is characterized by significant local amplitude (40-50 mV) and an extraordinary duration (up to several hundred milliseconds). The NMDA plateau potential, when it is initiated in an apical tuft dendrite, is able to maintain a good portion of that tuft in a sustained depolarized state. However, if NMDA-dominated plateau potentials originate in proximal segments of basal dendrites, they regularly bring the neuronal cell body into a sustained depolarized state, which resembles a cortical Up state. At each dendritic initiation site (basal, oblique, and tuft) an NMDA spike creates favorable conditions for causal interactions of active synaptic inputs, including the spatial or temporal binding of information, as well as processes of short-term and long-term synaptic modifications (e.g., long-term potentiation or long-term depression). Because of their strong amplitudes and durations, local dendritic NMDA spikes make up the cellular substrate for multisite independent subunit computations that enrich the computational power and repertoire of cortical pyramidal cells. We propose that NMDA spikes are likely to play significant roles in cortical information processing in awake animals (spatiotemporal binding, working memory) and during slow-wave sleep (neuronal Up states, consolidation of memories).

Mechanisms Gating the Flow of Information in the Cortex: What They Might Look Like and What Their Uses may be

The notion of gating as a mechanism capable of controlling the flow of information from one set of neurons to another, has been studied in many regions of the central nervous system. In the nucleus accumbens, where evidence is especially clear, gating seems to rely on the action of bistable neurons, i.e., of neurons that oscillate between a quiescent "down" state and a firing "up" state, and that act as AND-gates relative to their entries. Independently from these observations, a growing body of evidence now indicates that bistable neurons are also quite abundant in the cortex, although their exact functions in the dynamics of the brain remain to be determined. Here, we propose that at least some of these bistable cortical neurons are part of circuits devoted to gating information flow within the cortex. We also suggest that currently available structural, electrophysiological, and imaging data support the existence of at least three different types of gating architectures. The first architecture involves gating directly by the cortex itself. The second architecture features circuits spanning the cortex and the thalamus. The third architecture extends itself through the cortex, the basal ganglia, and the thalamus. These propositions highlight the variety of mechanisms that could regulate the passage of action potentials between cortical neurons sets. They also suggest that gating mechanisms require larger-scale neural circuitry to control the state of the gates themselves, in order to fit in the overall wiring of the brain and complement its dynamics.

Presetting basal ganglia for volitional actions

The basal ganglia (BG) have been considered as a key structure for volitional action preparation. Neurons in the striatum, the main BG input stage, increase activity gradually before volitional action initiation. However, because of the diversity of striatal motor commands, such as automatic (sensory driven) and volitional (internally driven) actions, it is still unclear whether an appropriate set of neurons encoding volitional actions are activated selectively for volitional action preparation. Here, using the antisaccade paradigm (look away from a visual stimulus), we dissociated neurons in the caudate nucleus, the oculomotor striatum, encoding predominantly automatic saccades toward the stimulus and volitional saccades in the opposite direction of the stimulus in monkeys. We found that before actual saccade directions were defined by visual stimulus appearance, neurons encoding volitional saccades increased activity with elapsed time from fixation initiation and by a temporal gap between fixation point disappearance and stimulus appearance. Their activity was further enhanced by an antisaccade instruction and correlated with antisaccade behavior. Neurons encoding automatic saccades also increased activity with elapsed time from fixation initiation and by a fixation gap. However, the activity of this type of neuron was not enhanced by an antisaccade instruction nor correlated with antisaccade behavior. We conclude that caudate neurons integrate nonspatial signals, such as elapsed time from fixation initiation, fixation gap, and task instructions, to preset BG circuits in favor of volitional actions to compete against automatic actions even before automatic and volitional commands are programmed with spatial information.