Neuronal assemblies: single cortical neurons are obedient members of a huge orchestra

Neuronal ensembles: Building blocks of neural circuits

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

Languaging psychopathology: neurobiology and metaphor

Explanatory models of the mind inform our working assumptions about mental illness with direct implications for clinical practice. Neurobiological models assert that the mind can be understood in terms of genetics, chemistry, and neuronal circuits. Growing evidence suggests that clinical deployment of neurobiological models of illness may have unintended adverse effects on patient attitudes, public perception, provider empathy, and the effectiveness of psychiatric treatment. New approaches are needed to find a better language for describing (let alone explaining) the experience of mental illness. To address this gap, we draw upon interdisciplinary sources and semiotic theory to characterize the role of metaphor in the conceptualization and communication of psychopathology. We examine the metaphors recruited by contemporary neurobiological models and metaphor's role in facilitating descriptive clarity or evocative creativity, depending on intention and context. These multiple roles reveal the implications of metaphorical reasoning in clinical practice, including cognitive flexibility, personalized communication, and uncertainty tolerance. With this analysis, we propose a clinical approach that embraces the meta-process of ongoing novel metaphor generation and co-elaboration, or languaging metaphors of psychopathology. Our goal is to bring attention to the value of employing ever-evolving, shapeable metaphorical depictions of psychiatric illness: metaphors that enable a capacity for change in individuals and society, reduce stigma, and nurture recovery.

Revealing and reshaping attractor dynamics in large networks of cortical neurons

Attractors play a key role in a wide range of processes including learning and memory. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking. By following the spontaneous network bursts of cultured cortical networks, we are able to define a vocabulary of spatiotemporal patterns and show that they function as discrete attractors in the network dynamics. We show that electrically stimulating specific attractors eliminates them from the spontaneous vocabulary, while they are still robustly evoked by the electrical stimulation. This seemingly paradoxical finding can be explained by a Hebbian-like strengthening of specific pathways into the attractors, at the expense of weakening non-evoked pathways into the same attractors. We verify this hypothesis and provide a mechanistic explanation for the underlying changes supporting this effect.

Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI

The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.

Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

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

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

Single-Neuron Level One-Photon Voltage Imaging With Sparsely Targeted Genetically Encoded Voltage Indicators

Voltage imaging of many neurons simultaneously at single-cell resolution is hampered by the difficulty of detecting small voltage signals from overlapping neuronal processes in neural tissue. Recent advances in genetically encoded voltage indicator (GEVI) imaging have shown single-cell resolution optical voltage recordings in intact tissue through imaging naturally sparse cell classes, sparse viral expression, soma restricted expression, advanced optical systems, or a combination of these. Widespread sparse and strong transgenic GEVI expression would enable straightforward optical access to a densely occurring cell type, such as cortical pyramidal cells. Here we demonstrate that a recently described sparse transgenic expression strategy can enable single-cell resolution voltage imaging of cortical pyramidal cells in intact brain tissue without restricting expression to the soma. We also quantify the functional crosstalk in brain tissue and discuss optimal imaging rates to inform future GEVI experimental design.

Protective and reversal actions of a novel peptidomimetic against a pivotal toxin implicated in Alzheimer's disease

Despite the many attempts to understand the aetiology of Alzheimer's disease, the basic mechanisms accounting for the progressive cycle of neuronal loss are still unknown. Previous work has suggested that the pivotal molecule mediating neurodegeneration could be an independently acting peptide cleaved from acetylcholinesterase. This previously unidentified agent acts as a signalling molecule in selectively vulnerable groups of cells where erstwhile developmental mechanisms are activated inappropriately to have a toxic effect in the context of the mature brain. We have previously shown that the toxic actions of this peptide, whose level is doubled in the Alzheimer brain, can be blocked by a cyclised variant (NBP14). However, the size and properties of NBP14 would render it unlikely as a feasible therapeutic candidate. Here therefore we test a synthetic peptidomimetic (NB-0193), modelled on the binding of NBP14 to the target alpha-7 nicotinic receptor, and benchmarked against it to screen for reversal effects using real-time optical imaging in rat brain slices. The blocking action of NB-0193 was confirmed by testing its effect against peptide-induced calcium influx in cell cultures, where it showed a dose-dependent profile over a trophic-toxic range. Moreover, NB-0193 presented promising pharmacokinetic characteristics and could therefore prompt a new therapeutic approach against Alzheimer's disease.

Interneuron Cooperativity in Cortical Circuits

Neocortical neurons tend to be coactive in groups called ensembles. However, sometimes, individual neurons also spike alone, independent of the ensemble. What processes regulate the transition between individual and cooperative action? Inspired by classical work in biochemistry, we apply the concept of neuronal cooperativity to explore this question. With a focus on neocortical inhibitory interneurons, we offer a working definition of neuronal cooperativity, review its recorded incidences and proposed mechanisms, and describe experimental approaches that will demonstrate and further describe this action. We suggest that cooperativity of "neuron teams" is manifested in vivo through their coactivity, as well as via the action of individual "soloist neurons" in the low end of the sigmoidal cooperativity curve. Finally, we explore the evidence for and implications of individual and team action of neurons.

Collective Behavior of Place and Non-place Neurons in the Hippocampal Network

Discussions of the hippocampus often focus on place cells, but many neurons are not place cells in any given environment. Here we describe the collective activity in such mixed populations, treating place and non-place cells on the same footing. We start with optical imaging experiments on CA1 in mice as they run along a virtual linear track and use maximum entropy methods to approximate the distribution of patterns of activity in the population, matching the correlations between pairs of cells but otherwise assuming as little structure as possible. We find that these simple models accurately predict the activity of each neuron from the state of all the other neurons in the network, regardless of how well that neuron codes for position. Our results suggest that understanding the neural activity may require not only knowledge of the external variables modulating it but also of the internal network state.

Mammalian cortical voltage imaging using genetically encoded voltage indicators: a review honoring professor Amiram Grinvald

The pioneering work of Amiram Grinvald established voltage-sensitive dye imaging (VSDI) in the mammalian cortex in the 1980s and inspired decades of cortical voltage imaging and the associated technological developments. The recent conception and development of genetically encoded voltage indicators (GEVIs) overcome many of the limitations of classical VSDI, and open experimental approaches that provide accruing support for orchestrated neuronal circuit dynamics of spatially distributed neuronal circuit underlying behaviors. We will review recent achievements using GEVIs to optically monitor the cortical activity in mammalian brains in vivo and provide a perspective for potential future directions.

Endogenous sequential cortical activity evoked by visual stimuli

Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time.

Electrophysiological and firing properties of neurons: Categorizing soloists and choristers in primary visual cortex

Visual processing in the cortex involves various aspects of neuronal properties such as morphological, electrophysiological and molecular. In particular, the neural firing pattern is an important indicator of dynamic circuitry within a neuronal population. Indeed, in microcircuits, neurons act as soloists or choristers wherein the characteristical activity of a 'soloist' differs from the firing pattern of a 'chorister'. Both cell types correlate their respective firing rate with the global populational activity in a unique way. In the present study, we sought to examine the relationship between the spike shape (thin spike neurons and broad spike neurons) of cortical neurons recorded from V1, their firing levels and their propensity to act as soloists or choristers. We found that thin spike neurons, which exhibited higher levels of firing, generally correlate their activity with the neuronal population (choristers). On the other hand, broad spike neurons showed lower levels of firing and demonstrated weak correlations with the assembly (soloists). A major consequence of the present study is: estimating the correlation of neural spike trains with their neighboring population is a predictive indicator of spike waveforms and firing level. Indeed, we found a continuum distribution of coupling strength ranging from weak correlation-strength (attributed to low-firing neurons) to high correlation-strength (attributed to high-firing neurons). The tendency to exhibit high- or low-firing is conducive to the spike shape of neurons. Our results offer new insights into visual processing by showing how high-firing rate neurons (mostly thin spike neurons) could modulate the neuronal responses within cell-assemblies.

From the neuron doctrine to neural networks

For over a century, the neuron doctrine--which states that the neuron is the structural and functional unit of the nervous system--has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.

The restless brain: how intrinsic activity organizes brain function

Traditionally studies of brain function have focused on task-evoked responses. By their very nature such experiments tacitly encourage a reflexive view of brain function. While such an approach has been remarkably productive at all levels of neuroscience, it ignores the alternative possibility that brain functions are mainly intrinsic and ongoing, involving information processing for interpreting, responding to and predicting environmental demands. I suggest that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources, its limited access to sensory information and a dynamic, intrinsic functional organization. The nature of this intrinsic activity, which exhibits a surprising level of organization with dimensions of both space and time, is revealed in the ongoing activity of the brain and its metabolism. As we look to the future, understanding the nature of this intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. The reward for doing so will be a much better understanding of human behaviour in health and disease.

Lag threads organize the brain's intrinsic activity

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term "lag threads," are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.

Visual stimuli recruit intrinsically generated cortical ensembles

The cortical microcircuit is built with recurrent excitatory connections, and it has long been suggested that the purpose of this design is to enable intrinsically driven reverberating activity. To understand the dynamics of neocortical intrinsic activity better, we performed two-photon calcium imaging of populations of neurons from the primary visual cortex of awake mice during visual stimulation and spontaneous activity. In both conditions, cortical activity is dominated by coactive groups of neurons, forming ensembles whose activation cannot be explained by the independent firing properties of their contributing neurons, considered in isolation. Moreover, individual neurons flexibly join multiple ensembles, vastly expanding the encoding potential of the circuit. Intriguingly, the same coactive ensembles can repeat spontaneously and in response to visual stimuli, indicating that stimulus-evoked responses arise from activating these intrinsic building blocks. Although the spatial properties of stimulus-driven and spontaneous ensembles are similar, spontaneous ensembles are active at random intervals, whereas visually evoked ensembles are time-locked to stimuli. We conclude that neuronal ensembles, built by the coactivation of flexible groups of neurons, are emergent functional units of cortical activity and propose that visual stimuli recruit intrinsically generated ensembles to represent visual attributes.

A review of cell assemblies

Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs' complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.

Genetically encoded optical indicators for the analysis of neuronal circuits

In a departure from previous top-down or bottom-up strategies used to understand neuronal circuits, many forward-looking research programs now place the circuit itself at their centre. This has led to an emphasis on the dissection and elucidation of neuronal circuit elements and mechanisms, and on studies that ask how these circuits generate behavioural outputs. This movement towards circuit-centric strategies is progressing rapidly as a result of technological advances that combine genetic manipulation with light-based methods. The core tools of these new approaches are genetically encoded optical indicators and actuators that enable non-destructive interrogation and manipulation of neuronal circuits in behaving animals with cellular-level precision. This Review examines genetically encoded reporters of neuronal function and assesses their value for circuit-oriented neuroscientific investigations.

Two-photon scanning microscopy of in vivo sensory responses of cortical neurons genetically encoded with a fluorescent voltage sensor in rat

A fluorescent voltage sensor protein “Flare” was created from a Kv1.4 potassium channel with YFP situated to report voltage-induced conformational changes in vivo. The RNA virus Sindbis introduced Flare into neurons in the binocular region of visual cortex in rat. Injection sites were selected based on intrinsic optical imaging. Expression of Flare occurred in the cell bodies and dendritic processes. Neurons imaged in vivo using two-photon scanning microscopy typically revealed the soma best, discernable against the background labeling of the neuropil. Somatic fluorescence changes were correlated with flashed visual stimuli; however, averaging was essential to observe these changes. This study demonstrates that the genetic modification of single neurons to express a fluorescent voltage sensor can be used to assess neuronal activity in vivo.

Robust Working Memory in an Asynchronously Spiking Neural Network Realized with Neuromorphic VLSI

We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of leaky integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of “high” and “low”-firing activity. Depending on the overall excitability, transitions to the “high” state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the “high” state retains a “working memory” of a stimulus until well after its release. In the latter case, “high” states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated “corrupted” “high” states comprising neurons of both excitatory populations. Within a “basin of attraction,” the network dynamics “corrects” such states and re-establishes the prototypical “high” state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons.

Dopaminergic modulation of striatal neurons, circuits, and assemblies

In recent years, there has been a great deal of progress toward understanding the role of the striatum and dopamine in action selection. The advent of new animal models and the development of optical techniques for imaging and stimulating select neuronal populations have provided the means by which identified synapses, cells, and circuits can be reliably studied. This review attempts to summarize some of the key advances in this broad area, focusing on dopaminergic modulation of intrinsic excitability and synaptic plasticity in canonical microcircuits in the striatum as well as recent work suggesting that there are neuronal assemblies within the striatum devoted to particular types of computation and possibly action selection.

Imaging voltage in neurons

In the last decades, imaging membrane potential has become a fruitful approach to study neural circuits, especially in invertebrate preparations with large, resilient neurons. At the same time, particularly in mammalian preparations, voltage imaging methods suffer from poor signal to noise and secondary side effects, and they fall short of providing single-cell resolution when imaging of the activity of neuronal populations. As an introduction to these techniques, we briefly review different voltage imaging methods (including organic fluorophores, SHG chromophores, genetic indicators, hybrid, nanoparticles, and intrinsic approaches) and illustrate some of their applications to neuronal biophysics and mammalian circuit analysis. We discuss their mechanisms of voltage sensitivity, from reorientation, electrochromic, or electro-optical phenomena to interaction among chromophores or membrane scattering, and highlight their advantages and shortcomings, commenting on the outlook for development of novel voltage imaging methods.

Effects of urethane anaesthesia on sensory processing in the rat barrel cortex revealed by combined optical imaging and electrophysiology

The spatiotemporal dynamics of neuronal assemblies evoked by sensory stimuli have not yet been fully characterised, especially the extent to which they are modulated by prevailing brain states. In order to examine this issue, we induced different levels of anaesthesia, distinguished by specific electroencephalographic indices, and compared somatosensory-evoked potentials (SEPs) with voltage-sensitive dye imaging (VSDI) responses in the rat barrel cortex evoked by whisker deflection. At deeper levels of anaesthesia, all responses were reduced in amplitude but, surprisingly, only VSDI responses exhibited prolonged activation resulting in a delayed return to baseline. Further analysis of the optical signal demonstrated that the reduction in response amplitude was constant across the area of activation, resulting in a global down-scaling of the population response. The manner in which the optical signal relates to the various neuronal generators that produce the SEP signal is also discussed. These data provide information regarding the impact of anaesthetic agents on the brain, and show the value of combining spatial analyses from neuroimaging approaches with more traditional electrophysiological techniques.

Attractors and noise: twin drivers of decisions and multistability

Perceptual decisions are made not only during goal-directed behavior such as choice tasks, but also occur spontaneously while multistable stimuli are being viewed. In both contexts, the formation of a perceptual decision is best captured by noisy attractor dynamics. Noise-driven attractor transitions can accommodate a wide range of timescales and a hierarchical arrangement with "nested attractors" harbors even more dynamical possibilities. The attractor framework seems particularly promising for understanding higher-level mental states that combine heterogeneous information from a distributed set of brain areas.

Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex

Successful decision making requires an ability to monitor contexts, actions, and outcomes. The anterior cingulate cortex (ACC) is thought to be critical for these functions, monitoring and guiding decisions especially in challenging situations involving conflict and errors. A number of different single-unit correlates have been observed in the ACC that reflect the diverse cognitive components involved. Yet how ACC neurons function as an integrated network is poorly understood. Here we show, using advanced population analysis of multiple single-unit recordings from the rat ACC during performance of an ecologically valid decision-making task, that ensembles of neurons move through different coherent and dissociable states as the cognitive requirements of the task change. This organization into distinct network patterns with respect to both firing-rate changes and correlations among units broke down during trials with numerous behavioral errors, especially at choice points of the task. These results point to an underlying functional organization into cell assemblies in the ACC that may monitor choices, outcomes, and task contexts, thus tracking the animal's progression through "task space."

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.

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.

Crossmodal propagation of sensory-evoked and spontaneous activity in the rat neocortex

In the cortex, neural responses to crossmodal stimulation are seen both in higher association areas and in primary sensory areas, and are thought to play a role in integration of crossmodal sensations. We used voltage-sensitive dye imaging (VSDI) to study the spatiotemporal characteristics of such crossmodal neural activity. We imaged three cortical regions in rat: primary visual cortex (V1), barrel field of primary somatosensory cortex (S1bf) and parietal association area (PA, flanked by V1 and S1bf). We find that sensory-evoked population activity can propagate in the form of a distinct propagating wave, robustly in either crossmodal direction. In single trials, the waveforms changed continuously during propagation, with dynamic variability from trial to trial, which we interpret as evidence for cortical involvement in the spreading process. To further characterize the functional anatomy of PA, we also studied the propagation of spontaneous sleep-like waves in this area. Using a novel flow-detection algorithm, we detected a propagation bias within PA of spontaneous waves--these tend to propagate parallel to the crossmodal axis, rather than orthogonal to it. Taken together, these findings demonstrate that intracortical networks show pre-attentive crossmodal propagation of activity, and suggest a potential mechanism for the establishment of crossmodal integration.

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.

Persistently active, pacemaker-like neurons in neocortex

The neocortex is spontaneously active, however, the origin of this self-generated, patterned activity remains unknown. To detect potential “pacemaker cells,” we use calcium imaging to directly identify neurons that discharge action potentials in the absence of synaptic transmissionin slices from juvenile mouse visual cortex. We characterize 60 of these neurons electrophysiologically and morphologically, finding that they belong to two classes of cells: one class composed of pyramidal neurons with a thin apical dendritic tree and a second class composed of ascending axon interneurons (Martinotti cells) located in layer 5. In both types of neurons, persistent sodium currents are necessary for the generation of the spontaneous activity. Our data demonstrate that subtypes of neocortical neurons have intrinsic mechanisms to generate persistent activity. Like in central pattern generators (CPGs), these neurons may act as “pacemakers” to initiate or pattern spontaneous activity in the neocortex.

Cortical functional network organization from autoregressive modeling of local field potential oscillations

A framework is presented for quantifying functional network organization in the brain by spectral analysis based on autoregressive modeling. Local field potentials (LFPs), simultaneously recorded from distributed sites in the cerebral cortex of monkeys, are treated as signals generated by local neuronal assemblies. During the delay period of a visual pattern discrimination task, oscillatory assembly activity is manifested in the LFPs in the beta-frequency range (14-30 Hz). Coherence analysis has shown that these oscillations are phase synchronized in functional networks in the sensorimotor cortex in relation to maintenance of contralateral hand position, and in the visual cortex in relation to anticipation of the visual stimulus. Granger causality analysis has revealed information flow in the sensorimotor network that is consistent with a peripheral sensorimotor feedback loop, and in the visual network that is consistent with top-down anticipatory modulation of assemblies in the primary visual cortex.

Basic difference between brain and computer: integration of asynchronous processes implemented as hardware model of the retina

There exists a common view that the brain acts like a Turing machine: The machine reads information from an infinite tape (sensory data) and, on the basis of the machine's state and information from the tape, an action (decision) is made. The main problem with this model lies in how to synchronize a large number of tapes in an adaptive way so that the machine is able to accomplish tasks such as object classification. We propose that such mechanisms exist already in the eye. A popular view is that the retina, typically associated with high gain and adaptation for light processing, is actually performing local preprocessing by means of its center-surround receptive field. We would like to show another property of the retina: The ability to integrate many independent processes. We believe that this integration is implemented by synchronization of neuronal oscillations. In this paper, we present a model of the retina consisting of a series of coupled oscillators which can synchronize on several scales. Synchronization is an analog process which is converted into a digital spike train in the output of the retina. We have developed a hardware implementation of this model, which enables us to carry out rapid simulation of multineuron oscillatory dynamics. We show that the properties of the spike trains in our model are similar to those found in vivo in the cat retina.

Fine spatiotemporal structure of phase in human intracranial EEG

OBJECTIVE

To transfer to the clinic for humans the technology and theory for high-resolution EEG analysis that have been developed in the laboratory with animals.

METHODS

EEGs were recorded at high spatial resolution from a 1 x 1 cm 8 x 8 electrode array on the right inferior temporal gyrus of a patient undergoing preoperative monitoring for epilepsy surgery. Cosines were fitted to EEG segments to measure frequency and phase and compute location, size, latency, phase velocity, duration, and recurrence rate of radially symmetric spatial patterns called phase cones. The Hilbert transform was also used to get high temporal resolution.

RESULTS

In the awake state, the power spectral density (PSD) showed power-law decrease in log power with log frequency at 1/falpha, alpha approximately 2, but with peaks in the standard empirical ranges. The phase in beta and gamma ranges had spatial gradients in conic form. Resetting of these stable spatial patterns of phase cones was spatially coincident at intermittent discontinuities ('phase slip') recurring at theta rates. Cones had half power diameters from 2 to 50+ mm; their durations had power-law distributions with values ranging from 6 to 300+ ms depending on length of the analysis window. In slow wave sleep PSD decreased at 1/falpha, alpha approximately 3,with loss of beta-gamma spectral peaks and diminished or absent oscillations and spatiotemporal phase structure.

CONCLUSIONS

Spatiotemporal structures in awake human and rabbit EEG showed striking similarities. The only clear differences were ascribable to differing scales of measurement. These fine spatiotemporal structures of EEG were diminished or lost in slow wave sleep.

SIGNIFICANCE

The fine structure indicates that neocortical stability is sustained at self-organized criticality; that synaptic input in the awake state drives neocortex away from criticality causing beta-gamma oscillations in re-stabilizing 'neural avalanches'; and that diminished input in slow wave sleep allows return toward criticality but with some added risk of instability and seizure.

Timing in cognition and EEG brain dynamics: discreteness versus continuity

This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, with an emphasis on the framework of operational architectonics (OA) of brain functioning (Fingelkurts and Fingelkurts in Brain Mind 2:291-29, 2001; Neurosci Biobehav Rev 28:827-836, 2005). This framework explores the temporal structure of information flow and interarea interactions within the network of functional neuronal populations by examining topographic sharp transition processes in the scalp EEG, on the millisecond scale. We conclude, based on the OA framework, that brain functioning is best conceptualized in terms of continuity-discreteness unity which is also the characteristic property of cognition. At the end we emphasize where one might productively proceed for the future research.

The human brain is intrinsically organized into dynamic, anticorrelated functional networks

During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.

Architectural and synaptic mechanisms underlying coherent spontaneous activity in V1

To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale ( approximately 5 x 10(5)) conductance-based, integrate-and-fire neuronal network model of an approximately 16-mm(2) patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity.

Intrinsic brain activity sets the stage for expression of motivated behavior

Research in many species has provided increasingly detailed information on relevant, primarily subcortical brain systems supporting the expression of basic appetites and drives. While basic appetites and drives are essential for adaptation and survival in any environment, they are naturally constrained by an organism's inherent biology and modulated as circumstances dictate. The brain mechanisms which serve to constrain and modulate them, however, remain much less well understood. We suggest that the manner in which such constraint and potential modulation is achieved likely involves processes that emerge from the coordinated behavior of multiple brain systems, and functional brain imaging techniques such as PET and fMRI are beginning to help us understand aspects of such coordination. In this review we argue that, in pursuit of this understanding, we must focus not only on changes evoked in brain systems during various behaviors, but also on the ongoing and very costly intrinsic activity within these systems, for the latter may be at least as important as the evoked activity in terms of brain function in general and the constraint and modulation of basic appetites and drives in particular. Distinguishing intrinsic from evoked activity in the context of functional brain imaging experiments is challenging, however. Here we review some evolving strategies for doing so.

Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity

How can neural activity propagate through cortical networks built with weak, stochastic synapses? We find precise repetitions of spontaneous patterns of synaptic inputs in neocortical neurons in vivo and in vitro. These patterns repeat after minutes, maintaining millisecond accuracy. Calcium imaging of slices reveals reactivation of sequences of cells during the occurrence of repeated intracellular synaptic patterns. The spontaneous activity drifts with time, engaging different cells. Sequences of active neurons have distinct spatial structures and are repeated in the same order over tens of seconds, revealing modular temporal dynamics. Higher order sequences are replayed with compressed timing.