Internal Dynamics Determine the Cortical Response to Thalamic Stimulation

Representing information in cell assemblies: persistent activity mediated by semilunar granule cells

Using rat hippocampal slices, we found that perforant path stimulation evokes long-lasting barrages of synaptic inputs in subpopulations of dentate gyrus mossy cells and hilar interneurons. Synaptic barrages could trigger persistent firing in hilar neurons. We found that synaptic barrages originate from semilunar granule cells (SGCs), glutamatergic neurons in the inner molecular layer that generate long-duration plateau potentials in response to excitatory synaptic input. MK801, nimodipine, and nickel all abolished stimulus-evoked plateau potentials in SGCs, and synaptic barrages in downstream hilar neurons, without blocking fast synaptic transmission. Hilar up-states triggered functional inhibition in granule cells that persisted for >10 s. Hilar cell assemblies, assayed by simultaneous triple and paired intracellular recordings, were linked by persistent firing in SGCs. Population responses recorded in hilar neurons accurately encoded stimulus identity. Stimulus-evoked up-states in dentate gyrus represent a potential cellular basis for hippocampal working memory.

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.

Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method

Noninvasive parcellation of the human cerebral cortex is an important goal for understanding and examining brain functions. Recently, the patterns of anatomical connections using diffusion tensor imaging (DTI) have been used to parcellate brain regions. Here, we present a noninvasive parcellation approach that uses "functional fingerprints" obtained by correlation measures on resting state functional magnetic resonance imaging (fMRI) data to parcellate brain regions. In other terms, brain regions are parcellated based on the similarity of their connection--as reflected by correlation during resting state--to the whole brain. The proposed method was used to parcellate the medial frontal cortex (MFC) into supplementary motor areas (SMA) and pre-SMA subregions. In agreement with anatomical landmark-based parcellation, we find that functional fingerprint clustering of the MFC results in anterior and posterior clusters. The probabilistic maps from 12 subjects showed that the anterior cluster is mainly located rostral to the vertical commissure anterior (VCA) line, whereas the posterior cluster is mainly located caudal to VCA line, suggesting the homologues of pre-SMA and SMA. The functional connections from the putative pre-SMA cluster were connected to brain regions which are responsible for complex/cognitive motor control, whereas those from the putative SMA cluster were connected to brain regions which are related to the simple motor control. These findings demonstrate the feasibility of the functional connectivity-based parcellation of the human cerebral cortex using resting state fMRI.

Spike inference from calcium imaging using sequential Monte Carlo methods

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest--spike trains and/or time varying intracellular calcium concentrations--are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., approximately 5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.

Spontaneous and driven cortical activity: implications for computation

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

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

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

Distinct roles of GABA(A) and GABA(B) receptors in balancing and terminating persistent cortical activity

Cortical networks spontaneously fluctuate between persistently active Up states and quiescent Down states. The Up states are maintained by recurrent excitation within local circuits, and can be turned on and off by synaptic input. GABAergic inhibition is believed to be important for stabilizing such persistent activity by balancing the excitation, and could have an additional role in terminating the Up state. Here, we report that GABA(A) and GABA(B) receptor-mediated inhibition have distinct and complementary roles in balancing and terminating persistent activity. In a model of Up-Down states expressed in slices of rat entorhinal cortex, the GABA(A) receptor antagonist, gabazine (50-500 nM), concentration-dependently decreased Up state duration, eventually leading to epileptiform bursts. In contrast, the GABA(B) receptor antagonist, CGP55845 (50 nM to 1 microM), increased the duration of persistent network activity, and prevented stimulus-induced Down state transitions. These results suggest that while GABA(A) receptor-mediated inhibition is necessary for balancing persistent activity, activation of GABA(B) receptors contributes to terminating Up states.

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

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

Neural coding and contextual influences in the whisker system

A fundamental problem in neuroscience, to which Prof. Segundo has made seminal contributions, is to understand how action potentials represent events in the external world. The aim of this paper is to review the issue of neural coding in the context of the rodent whiskers, an increasingly popular model system. Key issues we consider are: the role of spike timing; mechanisms of spike timing; decoding and context-dependence. Significant insight has come from the development of rigorous, information theoretic frameworks for tackling these questions, in conjunction with suitably designed experiments. We review both the theory and experimental studies. In contrast to the classical view that neurons are noisy and unreliable, it is becoming clear that many neurons in the subcortical whisker pathway are remarkably reliable and, by virtue of spike timing with millisecond-precision, have high bandwidth for conveying sensory information. In this way, even small (approximately 200 neuron) subcortical modules are able to support the sensory processing underlying sophisticated whisker-dependent behaviours. Future work on neural coding in cortex will need to consider new findings that responses are highly dependent on context, including behavioural and internal states.

An ongoing subthreshold neuronal state established through dynamic coassembling of cortical cells

Ensemble activation of neurons, triggered or spontaneous, sometimes involves a common (overlapping) neuronal population known as core cells. It is speculated that the core cells functioning as a core nucleus have a role in dictating noncore cells' behavior and thus overall local network dynamics. However, the truth and its significance in neuronal information processing still remain to be seen. To address this issue, a neural network model of an early sensory cortical area was simulated. In the network model, noncore cells that have selective responsiveness to sensory features constituted noncore cell assemblies. Core cells, having unselective responsiveness, constituted a single core cell assembly. Sensory stimulation activated neuronal ensembles that were indistinguishable from those activated spontaneously. The core cells were active in every ensemble activation and recruited a changing complement of noncore cells, which varied from spontaneous event to spontaneous event or from triggered event to triggered event. Ensemble activation of neurons was established through what we call dynamic coassembling, in which the core cell assembly and one of the noncore cell assemblies were dynamically linked together. Transient dynamic coassembling frequently and randomly took place during the ongoing (spontaneous) neuronal activity period, and persistent dynamic coassembling did during the stimulus-triggered neuronal activity period. The frequent ongoing activation of core cells mediated through transient dynamic coassembling depolarized noncore cells just below firing threshold, whereby the noncore cells could respond rapidly to sensory stimulation. The persistent dynamic coassembling enhanced the responsiveness of noncore cells. We suggest that the core cells, functioning as a core nucleus, dictate how the noncore cells oscillate at a subthreshold level during the ongoing period and how to respond when stimulated. The transient and persistent dynamic coassembling may be an essential neuronal mechanism for the cortex to prepare and respond effectively to sensory input.

Working memory dynamics and spontaneous activity in a flip-flop oscillations network model with a Milnor attractor

Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is associated with both a slow oscillation (leading to the up and down states) and the presence of the theta rhythm. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here to enlighten neural implication of working memory under these complicated dynamics, we propose a phenomenological network model with biologically plausible neural dynamics and recurrent connections. Each unit embeds an internal oscillation at the theta rhythm which can be triggered during up-state of the membrane potential. As a result, the resting state of a single unit is no longer a classical fixed point attractor but rather the Milnor attractor, and multiple oscillations appear in the dynamics of a coupled system. In conclusion, the interplay between the up and down states and theta rhythm endows high potential in working memory operation associated with complexity in spontaneous activities.

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.

State dependence of network output: modeling and experiments

Emerging experimental evidence suggests that both networks and their component neurons respond to similar inputs differently, depending on the state of network activity. The network state is determined by the intrinsic dynamical structure of the network and may change as a function of neuromodulation, the balance or stochasticity of synaptic inputs to the network, and the history of network activity. Much of the knowledge on state-dependent effects comes from comparisons of awake and sleep states of the mammalian brain. Yet, the mechanisms underlying these states are difficult to unravel. Several vertebrate and invertebrate studies have elucidated cellular and synaptic mechanisms of state dependence resulting from neuromodulation, sensory input, and experience. Recent studies have combined modeling and experiments to examine the computational principles that emerge when network state is taken into account; these studies are highlighted in this article. We discuss these principles in a variety of systems (mammalian, crustacean, and mollusk) to demonstrate the unifying theme of state dependence of network output.

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

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

Statistical significance of precisely repeated intracellular synaptic patterns

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

UP states protect ongoing cortical activity from thalamic inputs

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

Synchronization dynamics of two coupled neural oscillators receiving shared and unshared noisy stimuli

The response of neurons to external stimuli greatly depends on the intrinsic dynamics of the network. Here, the intrinsic dynamics are modeled as coupling and the external input is modeled as shared and unshared noise. We assume the neurons are repetitively firing action potentials (i.e., neural oscillators), are weakly and identically coupled, and the external noise is weak. Shared noise can induce bistability between the synchronous and anti-phase states even though the anti-phase state is the only stable state in the absence of noise. We study the Fokker-Planck equation of the system and perform an asymptotic reduction rho(0). The rho(0) solution is more computationally efficient than both the Monte Carlo simulations and the 2D Fokker-Planck solver, and agrees remarkably well with the full system with weak noise and weak coupling. With moderate noise and coupling, rho(0) is still qualitatively correct despite the small noise and coupling assumption in the asymptotic reduction. Our phase model accurately predicts the behavior of a realistic synaptically coupled Morris-Lecar system.

Imbalance of neocortical excitation and inhibition and altered UP states reflect network hyperexcitability in the mouse model of fragile X syndrome

Despite the pronounced neurological deficits associated with mental retardation and autism, it is unknown if altered neocortical circuit function occurs in these prevalent disorders. Here we demonstrate specific alterations in local synaptic connections, membrane excitability, and circuit activity of defined neuron types in sensory neocortex of the mouse model of Fragile X Syndrome-the Fmr1 knockout (KO). Overall, these alterations result in hyperexcitability of neocortical circuits in the Fmr1 KO. Specifically, we observe a substantial deficit in local excitatory drive ( approximately 50%) targeting fast-spiking (FS) inhibitory neurons in layer 4 of somatosensory, barrel cortex. This persists until at least 4 wk of age suggesting it may be permanent. In contrast, monosynaptic GABAergic synaptic transmission was unaffected. Overall, these changes indicate that local feedback inhibition in neocortical layer 4 is severely impaired in the Fmr1 KO mouse. An increase in the intrinsic membrane excitability of excitatory neurons may further contribute to hyperexcitability of cortical networks. In support of this idea, persistent neocortical circuit activity, or UP states, elicited by thalamic stimulation was longer in duration in the Fmr1 KO mouse. In addition, network inhibition during the UP state was less synchronous, including a 14% decrease in synchrony in the gamma frequency range (30-80 Hz). These circuit changes may be involved in sensory stimulus hypersensitivity, epilepsy, and cognitive impairment associated with Fragile X and autism.

The statistics of repeating patterns of cortical activity can be reproduced by a model network of stochastic binary neurons

Calcium imaging of the spontaneous activity in cortical slices has revealed repeating spatiotemporal patterns of transitions between so-called down states and up states (Ikegaya et al., 2004). Here we fit a model network of stochastic binary neurons to data from these experiments, and in doing so reproduce the distributions of such patterns. We use two versions of this model: (1) an unconnected network in which neurons are activated as independent Poisson processes; and (2) a network with an interaction matrix, estimated from the data, representing effective interactions between the neurons. The unconnected model (model 1) is sufficient to account for the statistics of repeating patterns in 11 of the 15 datasets studied. Model 2, with interactions between neurons, is required to account for pattern statistics of the remaining four. Three of these four datasets are the ones that contain the largest number of transitions, suggesting that long datasets are in general necessary to render interactions statistically visible. We then study the topology of the matrix of interactions estimated for these four datasets. For three of the four datasets, we find sparse matrices with long-tailed degree distributions and an overrepresentation of certain network motifs. The remaining dataset exhibits a strongly interconnected, spatially localized subgroup of neurons. In all cases, we find that interactions between neurons facilitate the generation of long patterns that do not repeat exactly.

Emerging views of corticothalamic function

Although it is now generally accepted that the thalamus is more than a simple relay of sensory signals to the cortex, we are just beginning to gain an understanding of how corticothalamic feedback influences sensory processing. Results from an increasing number of studies across sensory systems and different species reveal effects of feedback both on the receptive fields of thalamic neurons and on the transmission of sensory information between the thalamus and cortex. Importantly, these studies demonstrate that the cortico-thalamic projection cannot be viewed in isolation, but must be considered as an integral part of a thalamo-corticothalamic circuit which intimately interconnects the thalamus and cortex for sensory processing.

Of mice and men, and chandeliers

How does the human neocortex reliably propagate information through neural circuits? One mechanism appears to involve relying on strong connections from pyramidal neurons to interneurons and a depolarizing action of cortical chandelier cells.

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

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

Complex evolution of spike patterns during burst propagation through feed-forward networks

Stable signal transmission is crucial for information processing by the brain. Synfire-chains, defined as feed-forward networks of spiking neurons, are a well-studied class of circuit structure that can propagate a packet of single spikes while maintaining a fixed packet profile. Here, we studied the stable propagation of spike bursts, rather than single spike activities, in a feed-forward network of a general class of excitable bursting neurons. In contrast to single spikes, bursts can propagate stably without converging to any fixed profiles. Spike timings of bursts continue to change cyclically or irregularly during propagation depending on intrinsic properties of the neurons and the coupling strength of the network. To find the conditions under which bursts lose fixed profiles, we propose an analysis based on timing shifts of burst spikes similar to the phase response analysis of limit-cycle oscillators.

Behavioral states, network states, and sensory response variability

We review data demonstrating that single-neuron sensory responses change with the states of the neural networks (indexed in terms of spectral properties of local field potentials) in which those neurons are embedded. We start with broad network changes--different levels of anesthesia and sleep--and then move to studies demonstrating that the sensory response plasticity associated with attention and experience can also be conceptualized as functions of network state changes. This leads naturally to the recent data that can be interpreted to suggest that even brief experience can change sensory responses via changes in network states and that trial-to-trial variability in sensory responses is a nonrandom function of network fluctuations, as well. We suggest that the CNS may have evolved specifically to deal with stimulus variability and that the coupling with network states may be central to sensory processing.

Fast and accurate detection of action potentials from somatic calcium fluctuations

Large-scale recording from a population of neurons is a promising strategy for approaching the study of complex brain functions. Taking advantage of the fact that action potentials reliably evoke transient calcium fluctuations in the cell body, functional multineuron calcium imaging (fMCI) monitors the suprathreshold activity of hundreds of neurons. However, a limitation of fMCI is its semi-manual procedure of spike extraction from somatic calcium fluctuations, which is not only time consuming but is also associated with human errors. Here we describe a novel automatic method that combines principal-component analysis and support vector machine. This simple algorithm determines the timings of the spikes in calcium fluorescence traces more rapidly and reliably than human operators.

Topological analysis of population activity in visual cortex

Information in the cortex is thought to be represented by the joint activity of neurons. Here we describe how fundamental questions about neural representation can be cast in terms of the topological structure of population activity. A new method, based on the concept of persistent homology, is introduced and applied to the study of population activity in primary visual cortex (V1). We found that the topological structure of activity patterns when the cortex is spontaneously active is similar to those evoked by natural image stimulation and consistent with the topology of a two sphere. We discuss how this structure could emerge from the functional organization of orientation and spatial frequency maps and their mutual relationship. Our findings extend prior results on the relationship between spontaneous and evoked activity in V1 and illustrates how computational topology can help tackle elementary questions about the representation of information in the nervous system.

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

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

Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP-DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.

Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior

The resting brain is not silent, but exhibits organized fluctuations in neuronal activity even in the absence of tasks or stimuli. This intrinsic brain activity persists during task performance and contributes to variability in evoked brain responses. What is unknown is if this intrinsic activity also contributes to variability in behavior. In the current fMRI study, we identify a relationship between human brain activity in the left somatomotor cortex and spontaneous trial-to-trial variability in button press force. We then demonstrate that 74% of this brain-behavior relationship is attributable to ongoing fluctuations in intrinsic activity similar to those observed during resting fixation. In addition to establishing a functional and behavioral significance of intrinsic brain activity, these results lend new insight into the origins of variability in human behavior.

The impact of magnetic resonance imaging-detected white matter hyperintensities on longitudinal changes in regional cerebral blood flow

White matter hyperintensities are frequently detected on cranial magnetic resonance imaging (MRI) scans of older adults. Given the presumed ischemic contribution to the etiology of these lesions and the posited import of resting brain activity on cognitive function, we hypothesized that longitudinal changes in MRI-detected white matter disease, and its severity at a given time point, would be associated with changes in regional cerebral blood flow (rCBF) over time. We evaluated MRI scans and resting H(2)(15)O positron emission tomographic rCBF at baseline and after an average of 7.7-year follow-up in Baltimore Longitudinal Study of Aging participants without dementia. Differences in patterns of rCBF were evident at baseline and at follow-up between the group of subjects showing increased white matter disease over the 8-year interval compared with the group with stable white matter ratings. Furthermore, longitudinal changes over time in rCBF also differed between the two groups. Specifically, the group with progressive white matter abnormalities showed greater increase in the right inferior temporal gyrus/fusiform gyrus, right anterior cingulate, and the rostral aspect of the left superior temporal gyrus. Regions of greater longitudinal decrease in this group were evident in the right inferior parietal lobule and at the right occipital pole. Changes in white matter disease over time and its severity at any given time are associated significantly with both cross-sectional and longitudinal patterns of rCBF. The longitudinal increases may reflect cortical compensation mechanisms for reduced efficacy of interregional neural communications that result from white matter deterioration.

The source of afterdischarge activity in neocortical tonic-clonic epilepsy

Tonic-clonic seizures represent a common pattern of epileptic discharges, yet the relationship between the various phases of the seizure remains obscure. Here we contrast propagation of the ictal wavefront with the propagation of individual discharges in the clonic phase of the event. In an in vitro model of tonic-clonic epilepsy, the afterdischarges (clonic phase) propagate with relative uniform speed and are independent of the speed of the ictal wavefront (tonic phase). For slowly propagating ictal wavefronts, the source of the afterdischarges, relative to a given recording electrode, switched as the wavefront passed by, indicating that afterdischarges are seeded from wavefront itself. In tissue that has experienced repeated ictal events, the wavefront generalizes rapidly, and the afterdischarges in this case show a different "flip-flop" pattern, with frequent switches in their direction of propagation. This same flip-flop pattern is also seen in subdural EEG recordings in patients suffering intractable focal seizures caused by cortical dysplasias. Thus, in both slowly and rapidly generalizing ictal events, there is not a single source of afterdischarge activity: rather, the source is continuously changing. Our data suggest a complex view of seizures in which the ictal event and its constituent discharges originate from distinct locations.

Spontaneous electrical activity and behavior in the leech hirudo medicinalis

In the absence of external stimuli, animals explore the environment by performing irregular movements, but the neuronal mechanisms underlying this arrhythmic motion are largely unknown. In this paper, we studied the relationship between the spontaneous neuronal activity in the leech (Hirudo medicinalis) and its behavior. We analyzed the electrical activity of isolated ganglia, chains of two connected ganglia, and semi-intact preparations. The spontaneous electrical activity in ganglia was characterized by the occurrence of irregular bursts of spikes with variable duration and size. Properties of these bursts were modified by synaptic inputs arriving from the neighboring ganglia and from the two primitive brains located in the head and tail. In fact, in semi-intact preparations, unusually large bursts of spikes occurring spontaneously were recorded and caused the leech to move even in the absence of any external sensory stimulation. These large bursts appear to act as internal triggers controlling the spontaneous leech behavior and determining the duration of stereotypical motor patterns.

Cortical activity influences geniculocortical spike efficacy in the macaque monkey

Thalamocortical communication is a dynamic process influenced by both presynaptic and postsynaptic mechanisms. In this study, we recorded single-unit responses from cortical neurons that received direct input from the lateral geniculate nucleus (LGN) to address the question of whether prior patterns of cortical activity affect the ability of LGN inputs to drive cortical responses. By examining the ongoing activity that preceded the arrival of electrically evoked spikes from the LGN, we identified a number of activity patterns that were predictive of suprathreshold communication. Namely, cortical neurons were more likely to respond to LGN stimulation when their activity levels increased to 30-40Hz and/or their activity displayed rhythmic patterns (30 ms intervals) with increased power in the gamma frequency band. Cortical neurons were also more likely to respond to LGN stimulation when their activity increased 30-40 ms prior to stimulation, suggesting that the phase of gamma activity also contributes to geniculocortical communication. Based on these results, we conclude that ongoing activity in the cortex is not random, but rather organized in a manner that can influence the dynamics of thalamocortical communication.

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.

Network model of spontaneous activity exhibiting synchronous transitions between up and down States

Both in vivo and in vitro recordings indicate that neuronal membrane potentials can make spontaneous transitions between distinct up and down states. At the network level, populations of neurons have been observed to make these transitions synchronously. Although synaptic activity and intrinsic neuron properties play an important role, the precise nature of the processes responsible for these phenomena is not known. Using a computational model, we explore the interplay between intrinsic neuronal properties and synaptic fluctuations. Model neurons of the integrate-and-fire type were extended by adding a nonlinear membrane current. Networks of these neurons exhibit large amplitude synchronous spontaneous fluctuations that make the neurons jump between up and down states, thereby producing bimodal membrane potential distributions. The effect of sensory stimulation on network responses depends on whether the stimulus is applied during an up state or deeply inside a down state. External noise can be varied to modulate the network continuously between two extreme regimes in which it remains permanently in either the up or the down state.

Voltage and calcium transients in basal dendrites of the rat prefrontal cortex

Higher cortical functions (perception, cognition, learning and memory) are in large part based on the integration of electrical and calcium signals that takes place in thin dendritic branches of neocortical pyramidal cells (synaptic integration). The mechanisms underlying the synaptic integration in thin basal dendrites are largely unexplored. We use a recently developed technique, multisite voltage-calcium imaging, to compare voltage and calcium transients from multiple locations along individual dendritic branches. Our results reveal characteristic electrical transients (plateau potentials) that trigger and shape dendritic calcium dynamics and calcium distribution during suprathreshold glutamatergic synaptic input. We regularly observed three classes of voltage-calcium interactions occurring simultaneously in three different zones of the same dendritic branch: (1) proximal to the input site, (2) at the input site, and (3) distal to the input site. One hundred micrometers away from the synaptic input site, both proximally and distally, dendritic calcium transients are in tight temporal correlation with the dendritic plateau potential. However, on the same dendrite, at the location of excitatory input, calcium transients outlast local dendritic plateau potentials by severalfold. These Ca2+ plateaus (duration 0.5-2 s) are spatially restricted to the synaptic input site, where they cause a brief down-regulation of dendritic excitability. Ca2+ plateaus are not mediated by Ca2+ release from intracellular stores, but rather by an NMDA-dependent small-amplitude depolarization, which persists after the collapse of the dendritic plateau potential. These unique features of dendritic voltage and calcium distributions may provide distinct zones for simultaneous long-term (bidirectional) modulation of synaptic contacts along the same basal branch.

Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures

Recurring patterns of neural activity, a potential substrate of both information transfer and transformation in cortical networks, have been observed in the intact brain and in brain slices. Do these patterns require the inherent cortical microcircuitry of such preparations or are they a general property of self-organizing neuronal networks? In networks of dissociated cortical neurons from rats--which lack evidence of the intact brain's intrinsic cortical architecture--we have observed a robust set of spontaneously repeating spatiotemporal patterns of neural activity, using a template-matching algorithm that has been successful both in vivo and in brain slices. The observed patterns in cultured monolayer networks are stable over minutes of extracellular recording, occur throughout the culture's development, and are temporally precise within milliseconds. The identification of these patterns in dissociated cultures opens a powerful methodological avenue for the study of such patterns, and their persistence despite the topological and morphological rearrangements of cellular dissociation is further evidence that precisely timed patterns are a universal emergent feature of self-organizing neuronal networks.

Are corticothalamic 'up' states fragments of wakefulness?

The slow (<1 Hz) oscillation, with its alternating 'up' and 'down' states in individual neurons, is a defining feature of the electroencephalogram (EEG) during slow-wave sleep (SWS). Although this oscillation is well preserved across mammalian species, its physiological role is unclear. Electrophysiological and computational evidence from the cortex and thalamus now indicates that slow-oscillation 'up' states and the 'activated' state of wakefulness are remarkably similar dynamic entities. This is consistent with behavioural experiments suggesting that slow-oscillation 'up' states provide a context for the replay, and possible consolidation, of previous experience. In this scenario, the T-type Ca(2+) channel-dependent bursts of action potentials that initiate each 'up' state in thalamocortical (TC) neurons might function as triggers for synaptic and cellular plasticity in corticothalamic networks. This review is part of the INMED/TINS special issue Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com).

Spontaneous activity associated with primary visual cortex: a resting-state FMRI study

Brain functions during the resting state have attracted considerable attention in the past several years. However, little has been known about spontaneous activity in the sensory cortices in the task-free state. This study used functional magnetic resonance imaging (fMRI) to investigate the existence of spontaneous activity in the primary visual areas (PVA) of normal-sighted subjects and to explore the physiological implications of such activity. Our results revealed that we were able to detect spontaneous activity, which was nonrandom in that it was distinctly clustered both temporally and spatially in the PVA of each subject. In addition, the neural network associated with the PVA-related spontaneous activity included the visual association areas, the precuneus, the precentral/postcentral gyrus, the middle frontal gyrus, the fusiform gyrus, the inferior/middle temporal gyrus, and the parahippocampal gyrus. After considering the functions of these regions, we speculated that the PVA-related spontaneous activity may be associated with memory-related mental imagery and/or visual memory consolidation processes. These findings confirm the presence of spontaneous activity in the PVA and related brain areas. This confirmation supports the perspective that brain is a system intrinsically operating on its own, and sensory information interacts with rather than determines the operation of the system.

Development and plasticity of spontaneous activity and Up states in cortical organotypic slices

Cortical computations are an emergent property of neural dynamics. To understand how neural dynamics emerges within local cortical networks, we characterized the development and underlying mechanisms of spontaneous dynamics in cortical organotypic slices. We observed not only a quantitative increase in the levels of spontaneous dynamics, but a qualitative transition from brief bursts of activity to well defined Up states during the first 4 weeks in vitro. Analysis of cellular and synaptic properties indicates that these changes are driven by increasing excitatory drive accompanied by changes in the balance of excitation and inhibition. Examination of the structure of spontaneous dynamics revealed no evidence of precisely repeating patterns. Slices exposed to chronic patterned stimulation exhibited decreased levels of spontaneous activity, suggesting homeostatic control of the levels of network activity. Together, these results suggest that Up states reflect a fundamental mode of network dynamics that emerges through the orchestrated regulation of multiple cellular and synaptic properties in parallel.

The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex

Simple cells in primary visual cortex exhibit contrast-invariant orientation tuning, in seeming contradiction to feed-forward models that rely on lateral geniculate nucleus (LGN) input alone. Contrast invariance has therefore been thought to depend on the presence of intracortical lateral inhibition. In vivo intracellular recordings instead suggest that contrast invariance can be explained by three properties of the excitatory pathway. (1) Depolarizations evoked by orthogonal stimuli are determined by the amount of excitation a cell receives from the LGN, relative to the excitation it receives from other cortical cells. (2) Depolarizations evoked by preferred stimuli saturate at lower contrasts than the spike output of LGN relay cells. (3) Visual stimuli evoke contrast-dependent changes in trial-to-trial variability, which lead to contrast-dependent changes in the relationship between membrane potential and spike rate. Thus, high-contrast, orthogonally oriented stimuli that evoke significant depolarizations evoke few spikes. Together these mechanisms, without lateral inhibition, can account for contrast-invariant stimulus selectivity.

Intrinsic functional architecture in the anaesthetized monkey brain

The traditional approach to studying brain function is to measure physiological responses to controlled sensory, motor and cognitive paradigms. However, most of the brain's energy consumption is devoted to ongoing metabolic activity not clearly associated with any particular stimulus or behaviour. Functional magnetic resonance imaging studies in humans aimed at understanding this ongoing activity have shown that spontaneous fluctuations of the blood-oxygen-level-dependent signal occur continuously in the resting state. In humans, these fluctuations are temporally coherent within widely distributed cortical systems that recapitulate the functional architecture of responses evoked by experimentally administered tasks. Here, we show that the same phenomenon is present in anaesthetized monkeys even at anaesthetic levels known to induce profound loss of consciousness. We specifically demonstrate coherent spontaneous fluctuations within three well known systems (oculomotor, somatomotor and visual) and the 'default' system, a set of brain regions thought by some to support uniquely human capabilities. Our results indicate that coherent system fluctuations probably reflect an evolutionarily conserved aspect of brain functional organization that transcends levels of consciousness.

Thalamocortical Up states: differential effects of intrinsic and extrinsic cortical inputs on persistent activity

During behavioral quiescence, the neocortex generates spontaneous slow oscillations that consist of Up and Down states. Up states are short epochs of persistent activity that resemble the activated neocortex during arousal and cognition. Although Up states are generated within the cortex, the impact of extrinsic (thalamocortical) and intrinsic (intracortical) inputs on the persistent activity is not known. Using thalamocortical slices, we found that the persistent cortical activity during spontaneous Up states effectively drives thalamocortical relay cells through corticothalamic connections. However, thalamic activity can also precede the onset of cortical Up states, which suggests a role of thalamic activity in triggering cortical Up states through thalamocortical connections. In support of this hypothesis, we found that cutting the connections between thalamus and cortex reduced the incidence of spontaneous Up states in the cortex. Consistent with a facilitating role of thalamic activity on Up states, electrical or chemical stimulation of the thalamus triggered cortical Up states very effectively and enhanced those occurring spontaneously. In contrast, stimulation of the cortex triggered Up states only at very low intensities but otherwise had a suppressive effect on Up states. Moreover, cortical stimulation suppressed the facilitating effect of thalamic stimulation on Up states. In conclusion, thalamocortical inputs facilitate and intracortical inputs suppress cortical Up states. Thus, extrinsic and intrinsic cortical inputs differentially regulate persistent activity, which may serve to adjust the processing state of thalamocortical networks during behavior.

Novel and distinct operational principles of intralaminar thalamic neurons and their striatal projections

Neurons of the intralaminar thalamus, including central lateral (CL) and parafascicular (Pf) nuclei, innervate the cortex and striatum and are important for cognitive, sensory, and motor processes. We tested the hypothesis that CL and Pf neurons provide functionally distinct inputs to the striatum. We performed recordings of single CL and Pf neurons in anesthetized rats and, after juxtacellularly labeling the neurons, their somatodendritic features and synaptic connections were characterized. All CL neurons (n = 31) discharged classic low-threshold Ca2+ spike bursts during cortical slow-wave activity in vivo. In contrast, Pf neurons (n = 52) rarely fired such bursts, but instead discharged groups of spikes at relatively low frequencies. The activity of CL and Pf neurons was often temporally coupled to cortical slow oscillations. Identified CL neurons possessed archetypal "bushy" dendrites and preferentially established synapses with dendritic spines (91% of synapses) of striatal projection neurons. Pf neurons possessed "reticular-like" dendrites, and, on average, preferentially established synapses with dendritic shafts (63%) in striatum, although connectivity was markedly heterogeneous across neurons. Two of the six Pf neurons studied exclusively targeted dendritic shafts, whereas another neuron almost exclusively (97%) targeted spines. The remaining three neurons preferentially targeted dendritic shafts (53-70%). Thus, the fundamental properties of CL and Pf neurons differ (the latter do not express the typical operational principles of thalamic relay neurons), and they provide different temporally patterned inputs to distinct striatal targets. This mechanistic diversity likely underpins the transmission of specific and discrete information from intralaminar thalamic nuclei to striatal and cortical targets.

Combined Voltage and Calcium Epifluorescence Imaging In Vitro and In Vivo Reveals Subthreshold and Suprathreshold Dynamics of Mouse Barrel Cortex

Cortical dynamics can be imaged at high spatiotemporal resolution with voltage-sensitive dyes (VSDs) and calcium-sensitive dyes (CaSDs). We combined these two imaging techniques using epifluorescence optics together with whole cell recordings to measure the spatiotemporal dynamics of activity in the mouse somatosensory barrel cortex in vitro and in the supragranular layers in vivo. The two optical signals reported distinct aspects of cortical function. VSD fluorescence varied linearly with membrane potential and was dominated by subthreshold postsynaptic potentials, whereas the CaSD signal predominantly reflected local action potential firing. Combining VSDs and CaSDs allowed us to monitor the synaptic drive and the spiking activity of a given area at the same time in the same preparation. The spatial extent of the two dye signals was different, with VSD signals spreading further than CaSD signals, reflecting broad subthreshold and narrow suprathreshold receptive fields. Importantly, the signals from the dyes were differentially affected by pharmacological manipulations, stimulation strength, and depth of isoflurane anesthesia. Combined VSD and CaSD measurements can therefore be used to specify the temporal and spatial relationships between subthreshold and suprathreshold activity of the neocortex.

Feedforward inhibition contributes to the control of epileptiform propagation speed

It is still poorly understood how epileptiform events can recruit cortical circuits. Moreover, the speed of propagation of epileptiform discharges in vivo and in vitro can vary over several orders of magnitude (0.1-100 mm/s), a range difficult to explain by a single mechanism. We previously showed how epileptiform spread in neocortical slices is opposed by a powerful feedforward inhibition ahead of the ictal wave. When this feedforward inhibition is intact, epileptiform spreads very slowly (approximately 100 microm/s). We now investigate whether changes in this inhibitory restraint can also explain much faster propagation velocities. We made use of a very characteristic pattern of evolution of ictal activity in the zero magnesium (0 Mg2+) model of epilepsy. With each successive ictal event, the number of preictal inhibitory barrages dropped, and in parallel with this change, the propagation velocity increased. There was a highly significant correlation (p < 0.001) between the two measures over a 1000-fold range of velocities, indicating that feedforward inhibition was the prime determinant of the speed of epileptiform propagation. We propose that the speed of propagation is set by the extent of the recruitment steps, which in turn is set by how successfully the feedforward inhibitory restraint contains the excitatory drive. Thus, a single mechanism could account for the wide range of propagation velocities of epileptiform events observed in vitro and in vivo.