Repeated patterns of distributed synchrony in neuronal assemblies

Neuronal mechanisms underlying opioid-induced respiratory depression: our current understanding

Opioid-induced respiratory depression (OIRD) represents the primary cause of death associated with therapeutic and recreational opioid use. Within the United States, the rate of death from opioid abuse since the early 1990s has grown disproportionally, prompting the classification as a nationwide "epidemic." Since this time, we have begun to unravel many fundamental cellular and systems-level mechanisms associated with opioid-related death. However, factors such as individual vulnerability, neuromodulatory compensation, and redundancy of opioid effects across central and peripheral nervous systems have created a barrier to a concise, integrative view of OIRD. Within this review, we bring together multiple perspectives in the field of OIRD to create an overarching viewpoint of what we know, and where we view this essential topic of research going forward into the future.

Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

The respiratory motor pattern is coordinated with cardiovascular system regulation. Inspiratory drive and respiratory phase durations are tuned by blood pressure and baroreceptor reflexes. We hypothesized that perturbations of systemic arterial blood pressure modulate inspiratory drive through a raphe-pontomedullary network. In 15 adult decerebrate vagotomized neuromuscular-blocked cats, we used multielectrode arrays to record the activities of 704 neurons within the medullary ventral respiratory column, pons, and raphe areas during baroreceptor-evoked perturbations of breathing, as measured by altered peak activity in integrated efferent phrenic nerve activity and changes in respiratory phase durations. Blood pressure was transiently (30 s) elevated or reduced by inflations of an embolectomy catheter in the descending aorta or inferior vena cava. S-transform time-frequency representations were calculated for multiunit phrenic nerve activity and some spike trains to identify changes in rhythmic activity during perturbations. Altered firing rates in response to either or both conditions were detected for 474 of 704 tested cells. Spike trains of 17,805 neuron pairs were evaluated for short-time scale correlational signatures indicative of functional connectivity with standard cross-correlation analysis, supplemented with gravitational clustering; ∼70% of tested (498 of 704) and responding neurons (333 of 474) were involved in a functional correlation with at least one other cell. Changes in high-frequency oscillations in the spiking of inspiratory neurons and the evocation or resetting of slow quasi-periodic fluctuations in the respiratory motor pattern associated with oscillations of arterial pressure were observed. The results support a linked-loop pontomedullary network architecture for multispectral tuning of inspiration.NEW & NOTEWORTHY The brain network that supports cardiorespiratory coupling remains poorly understood. Using multielectrode arrays, we tested the hypothesis that blood pressure and baroreceptor reflexes "tune" the breathing motor pattern via a raphe-pontomedullary network. Neuron responses to changes in arterial pressure and identified functional connectivity, together with altered high frequency and slow Lundberg B-wave oscillations, support a model with linked recurrent inhibitory loops that stabilize the respiratory network and provide a path for transmission of baroreceptor signals.

Defining the Rhythmogenic Elements of Mammalian Breathing

Breathing's remarkable ability to adapt to changes in metabolic, environmental, and behavioral demands stems from a complex integration of its rhythm-generating network within the wider nervous system. Yet, this integration complicates identification of its specific rhythmogenic elements. Based on principles learned from smaller rhythmic networks of invertebrates, we define criteria that identify rhythmogenic elements of the mammalian breathing network and discuss how they interact to produce robust, dynamic breathing.

The Dynamic Basis of Respiratory Rhythm Generation: One Breath at a Time

Rhythmicity is a universal timing mechanism in the brain, and the rhythmogenic mechanisms are generally dynamic. This is illustrated for the neuronal control of breathing, a behavior that occurs as a one-, two-, or three-phase rhythm. Each breath is assembled stochastically, and increasing evidence suggests that each phase can be generated independently by a dedicated excitatory microcircuit. Within each microcircuit, rhythmicity emerges through three entangled mechanisms: ( a) glutamatergic transmission, which is amplified by ( b) intrinsic bursting and opposed by ( c) concurrent inhibition. This rhythmogenic triangle is dynamically tuned by neuromodulators and other network interactions. The ability of coupled oscillators to reconfigure and recombine may allow breathing to remain robust yet plastic enough to conform to nonventilatory behaviors such as vocalization, swallowing, and coughing. Lessons learned from the respiratory network may translate to other highly dynamic and integrated rhythmic systems, if approached one breath at a time.

Ensemble stacking mitigates biases in inference of synaptic connectivity

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Methods for identification of spike patterns in massively parallel spike trains

Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.

Carotid chemoreceptors tune breathing via multipath routing: reticular chain and loop operations supported by parallel spike train correlations

We tested the hypothesis that carotid chemoreceptors tune breathing through parallel circuit paths that target distinct elements of an inspiratory neuron chain in the ventral respiratory column (VRC). Microelectrode arrays were used to monitor neuronal spike trains simultaneously in the VRC, peri-nucleus tractus solitarius (p-NTS)-medial medulla, the dorsal parafacial region of the lateral tegmental field (FTL-pF), and medullary raphe nuclei together with phrenic nerve activity during selective stimulation of carotid chemoreceptors or transient hypoxia in 19 decerebrate, neuromuscularly blocked, and artificially ventilated cats. Of 994 neurons tested, 56% had a significant change in firing rate. A total of 33,422 cell pairs were evaluated for signs of functional interaction; 63% of chemoresponsive neurons were elements of at least one pair with correlational signatures indicative of paucisynaptic relationships. We detected evidence for postinspiratory neuron inhibition of rostral VRC I-Driver (pre-Bötzinger) neurons, an interaction predicted to modulate breathing frequency, and for reciprocal excitation between chemoresponsive p-NTS neurons and more downstream VRC inspiratory neurons for control of breathing depth. Chemoresponsive pericolumnar tonic expiratory neurons, proposed to amplify inspiratory drive by disinhibition, were correlationally linked to afferent and efferent "chains" of chemoresponsive neurons extending to all monitored regions. The chains included coordinated clusters of chemoresponsive FTL-pF neurons with functional links to widespread medullary sites involved in the control of breathing. The results support long-standing concepts on brain stem network architecture and a circuit model for peripheral chemoreceptor modulation of breathing with multiple circuit loops and chains tuned by tegmental field neurons with quasi-periodic discharge patterns. NEW & NOTEWORTHY We tested the long-standing hypothesis that carotid chemoreceptors tune the frequency and depth of breathing through parallel circuit operations targeting the ventral respiratory column. Responses to stimulation of the chemoreceptors and identified functional connectivity support differential tuning of inspiratory neuron burst duration and firing rate and a model of brain stem network architecture incorporating tonic expiratory "hub" neurons regulated by convergent neuronal chains and loops through rostral lateral tegmental field neurons with quasi-periodic discharge patterns.

Pattern reverberation in networks of excitable systems with connection delays

We consider the recurrent pulse-coupled networks of excitable elements with delayed connections, which are inspired by the biological neural networks. If the delays are tuned appropriately, the network can either stay in the steady resting state, or alternatively, exhibit a desired spiking pattern. It is shown that such a network can be used as a pattern-recognition system. More specifically, the application of the correct pattern as an external input to the network leads to a self-sustained reverberation of the encoded pattern. In terms of the coupling structure, the tolerance and the refractory time of the individual systems, we determine the conditions for the uniqueness of the sustained activity, i.e., for the functionality of the network as an unambiguous pattern detector. We point out the relation of the considered systems with cyclic polychronous groups and show how the assumed delay configurations may arise in a self-organized manner when a spike-time dependent plasticity of the connection delays is assumed. As excitable elements, we employ the simplistic coincidence detector models as well as the Hodgkin-Huxley neuron models. Moreover, the system is implemented experimentally on a Field-Programmable Gate Array.

Role of the dorsal medulla in the neurogenesis of airway protection

The dorsal medulla encompassing the nucleus of the tractus solitarius (NTS) and surrounding reticular formation (RF) has an important role in processing sensory information from the upper and lower airways for the generation and control of airway protective behaviors. These behaviors, such as cough and swallow, historically have been studied in isolation. However, recent information indicates that these and other airway protective behaviors are coordinated to minimize risk of aspiration. The dorsal medullary neural circuits that include the NTS are responsible for rhythmogenesis for repetitive swallowing, but previous models have assigned a role for this portion of the network for coughing that is restricted to monosynaptic sensory processing. We propose a more complex NTS/RF circuit that controls expression of swallowing and coughing and the coordination of these behaviors. The proposed circuit is supported by recordings of activity patterns of selected neural elements in vivo and simulations of a computational model of the brainstem circuit for breathing, coughing, and swallowing. This circuit includes separate rhythmic sub-circuits for all three behaviors. The revised NTS/RF circuit can account for the mode of action of antitussive drugs on the cough motor pattern, as well as the unique coordination of cough and swallow by a meta-behavioral control system for airway protection.

STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony

There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron-like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The framework provides both linear and nonlinear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of nonlinear differential equations leading to chaotic attractors.

Gibbs distribution analysis of temporal correlations structure in retina ganglion cells

We present a method to estimate Gibbs distributions with spatio-temporal constraints on spike trains statistics. We apply this method to spike trains recorded from ganglion cells of the salamander retina, in response to natural movies. Our analysis, restricted to a few neurons, performs more accurately than pairwise synchronization models (Ising) or the 1-time step Markov models (Marre et al., 2009) to describe the statistics of spatio-temporal spike patterns and emphasizes the role of higher order spatio-temporal interactions.

Analysis and modeling of ensemble recordings from respiratory pre-motor neurons indicate changes in functional network architecture after acute hypoxia

We have combined neurophysiologic recording, statistical analysis, and computational modeling to investigate the dynamics of the respiratory network in the brainstem. Using a multielectrode array, we recorded ensembles of respiratory neurons in perfused in situ rat preparations that produce spontaneous breathing patterns, focusing on inspiratory pre-motor neurons. We compared firing rates and neuronal synchronization among these neurons before and after a brief hypoxic stimulus. We observed a significant decrease in the number of spikes after stimulation, in part due to a transient slowing of the respiratory pattern. However, the median interspike interval did not change, suggesting that the firing threshold of the neurons was not affected but rather the synaptic input was. A bootstrap analysis of synchrony between spike trains revealed that both before and after brief hypoxia, up to 45% (but typically less than 5%) of coincident spikes across neuronal pairs was not explained by chance. Most likely, this synchrony resulted from common synaptic input to the pre-motor population, an example of stochastic synchronization. After brief hypoxia most pairs were less synchronized, although some were more, suggesting that the respiratory network was transiently “rewired” after the stimulus. To investigate this hypothesis, we created a simple computational model with feed-forward divergent connections along the inspiratory pathway. Assuming that (1) the number of divergent projections was not the same for all presynaptic cells, but rather spanned a wide range and (2) that the stimulus increased inhibition at the top of the network; this model reproduced the reduction in firing rate and bootstrap-corrected synchrony subsequent to hypoxic stimulation observed in our experimental data.

Effect of network topology on neuronal encoding based on spatiotemporal patterns of spikes

Despite significant progress in our understanding of the brain at both microscopic and macroscopic scales, the mechanisms by which low-level neuronal behavior gives rise to high-level mental processes such as memory still remain unknown. In this paper, we assess the plausibility and quantify the performance of polychronization, a newly proposed mechanism of neuronal encoding, which has been suggested to underlie a wide range of cognitive phenomena. We then investigate the effect of network topology on the reliability with which input stimuli can be distinguished based on their encoding in the form of so-called polychronous groups or spatiotemporal patterns of spikes. We find that small-world networks perform an order of magnitude better than random ones, enabling reliable discrimination between inputs even when prompted by increasingly incomplete recall cues. Furthermore, we show that small-world architectures operate at significantly reduced energetic costs and that their memory capacity scales favorably with network size. Finally, we find that small-world topologies introduce biologically realistic constraints on the optimal input stimuli, favoring especially the topographic inputs known to exist in many cortical areas. Our results suggest that mammalian cortical networks, by virtue of being both small-world and topographically organized, seem particularly well-suited to information processing through polychronization. This article addresses the fundamental question of encoding in neuroscience. In particular, evidence is presented in support of an emerging model of neuronal encoding in the neocortex based on spatiotemporal patterns of spikes.

A color-based visualization technique for multielectrode spike trains

Multi electrode recordings of neuronal activity provide an overwhelming amount of data that is often difficult to analyze and interpret. Although various methods exist for treating multielectrode datasets quantitatively, there is a particularly prominent lack of techniques that enable a quick visual exploration of such datasets. Here, by using Kohonen self-organizing maps, we propose a simple technique that allows for the representation of multiple spike trains through a sequence of color-coded population activity vectors. When multiple color sequences are grouped according to a certain criterion, e.g., by stimulation condition or recording time, one can inspect an entire dataset visually and extract quickly information about the identity, stimulus-locking and temporal distribution of multi-neuron activity patterns. Color sequences can be computed on various time scales revealing different aspects of the temporal dynamics and can emphasize high-order correlation patterns that are not detectable with pairwise techniques. Furthermore, this technique is useful for determining the stability of neuronal responses during a recording session. Due to its simplicity and reliance on perceptual grouping, the method is useful for both quick on-line visualization of incoming data and for more detailed post hoc analyses.

Functional identification of biological neural networks using reservoir adaptation for point processes

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.

Maximum decoding abilities of temporal patterns and synchronized firings: application to auditory neurons responding to click trains and amplitude modulated white noise

Simultaneous recordings of an increasing number of neurons have recently become available, but few methods have been proposed to handle this activity. Here, we extract and investigate all the possible temporal neural activity patterns based on synchronized firings of neurons recorded on multiple electrodes, or based on bursts of single-electrode activity in cat primary auditory cortex. We apply this to responses to periodic click trains or sinusoïdal amplitude modulated noise by obtaining for each pattern its temporal modulation transfer function. An algorithm that maximizes the mutual information between all patterns and stimuli subsequently leads to the identification of patterns that optimally decode modulation frequency (MF). We show that stimulus information contained in multi-electrode synchronized firing is not redundant with single-electrode firings and leads to improved efficiency of MF decoding. We also show that the combined use of firing rate and temporal codes leads to a better discrimination of the MF.

A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro

Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90-99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 +/- 7% (mean +/- SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.

Computational properties of networks of synchronous groups of spiking neurons

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

Polychronization: Computation with Spikes

We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as “attention to memories.”

Two enhancements of the gravity algorithm for multiple spike train analysis

The gravity method for neuronal assembly analysis represents each neuron as a particle in N-space with a time varying charge that is a filtered version of the corresponding spike train, with appropriate rules for forces between and movements of the charged particles. Resulting trajectories reflect neuronal timing relationships. The usual short time constants in the filter restrict aggregation to highly synchronized neurons and reduce the sensitivity for delayed correlations; long time constants in the filter reduce selectivity. Here we describe an enhancement that modifies rules for assigning charge increment times to allow mixtures of short and long lag correlations. Charge increments for each pair are offset from the actual spike times by time lags defined by features in corresponding cross-correlograms; no such charge offsets are invoked if the correlogram is flat. Tuning increases charge products and aggregation of long lag correlated pairs. A second enhancement uses a new three-dimensional display of particle pair trajectories to parse the type of neuronal relationship. For each pair, we record and display the inter-particle distance and the distance each particle moves from its original location in the N-space. The resulting trajectories cluster according to the type of interaction between the represented neurons. Results from simulated networks and in vivo multi-site recordings show that these modifications detect assembly properties not identified by the standard methods.

Invited review: Neural network plasticity in respiratory control

Respiratory network plasticity is a modification in respiratory control that persists longer than the stimuli that evoke it or that changes the behavior produced by the network. Different durations and patterns of hypoxia can induce different types of respiratory memories. Lateral pontine neurons are required for decreases in respiratory frequency that follow brief hypoxia. Changes in synchrony and firing rates of ventrolateral and midline medullary neurons may contribute to the long-term facilitation of breathing after brief intermittent hypoxia. Long-term changes in central respiratory motor control may occur after spinal cord injury, and the brain stem network implicated in the production of the respiratory rhythm could be reconfigured to produce the cough motor pattern. Preliminary analysis suggests that elements of brain stem respiratory neural networks respond differently to hypoxia and hypercapnia and interact with areas involved in cardiovascular control. Plasticity or alterations in these networks may contribute to the chronic upregulation of sympathetic nerve activity and hypertension in sleep apnea syndrome and may also be involved in sudden infant death syndrome.

Scattered-light imaging in vivo tracks fast and slow processes of neurophysiological activation

We imaged fast optical changes associated with evoked neural activation in the dorsal brainstem of anesthetized rats, using a novel imaging device. The imager consisted of a gradient-index (GRIN) lens, a microscope objective, and a miniature charged-coupled device (CCD) video camera. We placed the probe in contact with tissue above cardiorespiratory areas of the nucleus of the solitary tract and illuminated the tissue with 780-nm light through flexible fibers around the probe perimeter. The focus depth was adjusted by moving the camera and microscope objective relative to the fixed GRIN lens. Back-scattered light images were relayed through the GRIN lens to the CCD camera. Video frames were digitized at 100 frames per second, along with tracheal pressure, arterial blood pressure, and electrocardiogram signals recorded at 1 kHz per channel. A macroelectrode placed under the GRIN lens recorded field potentials from the imaged area. Aortic, vagal, and superior laryngeal nerves were dissected free of surrounding tissue within the neck. Separate shocks to each dissected nerve elicited evoked electrical responses and caused localized optical activity patterns. The optical response was modeled by four distinct temporal components corresponding to putative physical mechanisms underlying scattered light changes. Region-of-interest analysis revealed image areas which were dominated by one or more of the different time-course components, some of which were also optimally recorded at different tissue depths. Two slow optical components appear to correspond to hemodynamic responses to metabolic demand associated with activation. Two fast optical components paralleled electrical evoked responses.

Continuous image and electrophysiological recording with real-time processing and control

Collecting continuous video together with multichannel electrophysiological data and other experimental modalities requires high bandwidth and storage capacities, as well as accurate synchronization to detect correlations between different recorded events. Often, experiments are highly complex, with many variables requiring immediate analysis and feedback during the course of the experiment. In addition, output channels require real-time control with high time resolution. We have explored several approaches to a system that can perform the above functions. The design of our system considered a number of issues, including time intervals between control and acquisition events, longest continuous recording period, data transfer bottleneck considerations, file archiving and format, and real-time display and processing. To demonstrate the system, we describe an experiment for characterizing rapid evoked scattered light changes in neural tissue, in vivo, using simultaneous electronic image acquisition and electrophysiological recording.

Changes in cat medullary neurone firing rates and synchrony following induction of respiratory long-term facilitation

1. Long-term facilitation is a respiratory memory expressed as an increase in motor output lasting more than an hour. This change is induced by repeated hypoxia, stimulation of carotid chemoreceptors, or electrical stimulation of the carotid sinus nerve or brainstem mid-line. The present work addressed the hypothesis that persistent changes in medullary respiratory neural networks contribute to long-term facilitation. 2. Carotid chemoreceptors were stimulated by close arterial injection of CO(2)-saturated saline solution. Phrenic nerve efferent activity and up to 30 single medullary neurones were recorded simultaneously in nucleus tractus solitarii (NTS) including the dorsal respiratory group (DRG), Botzinger-ventral respiratory group (Böt-VRG), and nucleus raphe obscurus of nine adult cats, anaesthetized, injected with a neuromuscular blocking agent, vagotomized and artificially ventilated. 3. The firing rates of 87 of 105 neurones (83 %) changed following induction of long-term facilitation. Nine of eleven DRG and Böt-VRG putative premotor inspiratory neurones had increased firing rates with long-term facilitation. Fourteen of twenty-one raphe obscurus neurones with control firing rates less than 4 Hz had significant long-term increases in activity. 4. Cross-correlogram analysis suggested that there were changes in effective connectivity of neuron pairs with long-term facilitation. Joint peristimulus time histograms and pattern detection methods used with 'gravity' analysis also detected changes in short time scale correlations associated with long-term facilitation. 5. The results suggest that changes in firing rates and synchrony of VRG and DRG premotor neurones and altered effective connectivity among other functionally antecedent elements of the medullary respiratory network contribute to the expression of long-term facilitation.

Improvements to the sensitivity of gravitational clustering for multiple neuron recordings

We outline two improvements to the technique of gravitational clustering for detection of neuronal synchrony, which are capable of improving the method's detection of weak synchrony with limited data. The advantages of the enhancements are illustrated using data with known levels of synchrony and different interspike interval distributions. The novel simulation method described can easily generate such test data. An important dependence of the sensitivity of gravitational clustering to the interspike interval distribution of the analysed spike trains is described.

Towards a network theory of cognition

For cognitive neuroscience to go forward a more explicit effort is needed to use neurophysiology to constrain how the brain produces human mental functions. This review begins with the suggestion that two fundamental features may be critical for this effort. The first is the connectivity of the brain, which occupies an intermediate position between complete redundant interconnections and independence. The term semniconnected is presented as a designation, which is an obvious derivation of the term semiconductors as used in engineering. The second is transient response plasticity where a given neuron or collection of neurons may show rapid changes in response characteristics depending on experience. Response plasticity is a ubiquitous property of the brain rather than a unique characteristic of "neurocognitive" regions. These two properties may be brought together when brain areas interact such that their aggregate function embodies cognition. Three examples are used to illustrate these general principles and to develop the idea that a particular region in isolation may not act as a reliable index for a particular cognitive function. Instead, the neural context in which an area is active may define the cognitive function. Neural context emphasizes that the particular spatiotemporal pattern of neural interactions may hold the key to bridge between brain and mind.

Repeated sequences of interspike intervals in baroresponsive respiratory related neuronal assemblies of the cat brain stem

Many neurons exhibit spontaneous activity in the absence of any specific experimental perturbation. Patterns of distributed synchrony embedded in such activity have been detected in the brain stem, suggesting that it represents more than "baseline" firing rates subject only to being regulated up or down. This work tested the hypothesis that nonrandom sequences of impulses recur in baroresponsive respiratory-related brain stem neurons that are elements of correlational neuronal assemblies. In 15 Dial-urethan anesthetized vagotomized adult cats, neuronal impulses were monitored with microelectrode arrays in the ventral respiratory group, nucleus tractus solitarius, and medullary raphe nuclei. Efferent phrenic nerve activity was recorded. Spike trains were analyzed with cycle-triggered histograms and tested for respiratory-modulated firing rates. Baroreceptors were stimulated by unilateral pressure changes in the carotid sinus or occlusion of the descending aorta; changes in firing rates were assessed with peristimulus time and cumulative sum histograms. Cross-correlation analysis was used to test for nonrandom temporal relationships between spike trains. Favored patterns of interspike interval sequences were detected in 31 of 58 single spike trains; 18 of the neurons with significant sequences also had short-time scale correlations with other simultaneously recorded cells. The number of distributed patterns exceeded that expected under the null hypothesis in 12 of 14 data sets composed of 4-11 simultaneously recorded spike trains. The data support the hypothesis that baroresponsive brain stem neurons operate in transiently configured coordinated assemblies and suggest that single neuron patterns may be fragments of distributed impulse sequences. The results further encourage the search for coding functions of spike patterns in the respiratory network.

Respiratory neuronal assemblies

This review describes results from in vivo experiments on brain stem network mechanisms that control breathing. Multi-array recording technology and computational methods were used to test predictions derived from simulations of respiratory network models. This highly efficient approach has the advantage that many simultaneously recorded neurons are subject to shared stimulus, history, and state-dependent conditions. Our results have provided evidence for concurrent or parallel network interactions in the generation and modulation of the respiratory motor pattern. Recent data suggest that baroreceptors, chemoreceptors, nociceptors, and airway cough receptors shape the respiratory motor pattern, at least in part, through a system of shared coordinated 'multifunctional' neurons distributed in the brain stem. The 'gravity method' for the analysis and representation of multi-neuron data has demonstrated respiratory phase-dependent impulse synchrony among neurons with no respiratory modulation of their individual firing rates. The detection of this emergent property motivated the development of pattern detection methods that subsequently identified repeated transient configurations of these 'correlational assemblies'. These results support the view that information can be 'coded' in the nervous system by spike timing relationships, in addition to firing rate changes that traditionally have been measured by neurophysiologists.

Respiratory neural activity during long-term facilitation

Intermittent hypoxia results in a long-term facilitation (LTF) of respiratory efferent activity. The studies reviewed here presented data from both anesthetized and decerebrate, paralyzed, vagotomized, artificially ventilated adult cats. Multiple arrays of tungsten microelectrodes were used to record the concurrent responses of brain stem neurons that contribute to respiratory motor pattern generation. Spike trains were analyzed with firing rate histograms, peristimulus time histograms, cycle triggered histograms, spike triggered averages with multiunit phrenic efferent activity, cross correlation histograms, joint peristimulus time histograms and the gravity method. These studies addressed several hypotheses. (1) There is parallel processing of input from carotid chemoreceptors to the brain stem. (2) Respiratory related midline neurons are involved in the induction and maintenance of LTF. (3) There is a change in effective connectivity of brain stem neurons with LTF. (4) Neural networks involved in the induction and maintenance of LTF have patterns of synchrony that recur with a frequency greater than expected by chance.

Transient configurations of baroresponsive respiratory-related brainstem neuronal assemblies in the cat

The regulation of gas exchange requires coordination of the respiratory and cardiovascular systems. Previous work suggested that medullary raphe neurones transform and transmit information from baroreceptors to neurones in the ventral respiratory group. This study tested the hypothesis that distributed brainstem neuronal assemblies are transiently reconfigured during the respiratory cycle and baroreceptor stimulation. Blood pressure was perturbed by intravenous injection of an alpha1-adrenergic receptor agonist, unilateral pressure changes in the carotid sinus, or occlusion of the descending aorta in 14 Dial-urethane anaesthetized, vagotomized, paralysed, artificially ventilated cats. Neurones were monitored simultaneously with microelectrode arrays in two or more of the following sites: n. raphe obscurus, n. raphe magnus, rostral and caudal ventrolateral medulla, and the nucleus tractus solitarii. Transient configurations of baroresponsive assemblies were detected with joint pericycle-triggered histograms, the gravitational representation, and related pattern detection methods. Data were also analysed with cycle-triggered histograms, peristimulus-time and cumulative sum histograms, cross-correlograms, spike-triggered averages of efferent phrenic activity, and joint impulse configuration scatter diagrams (snowflakes). Five to nine simultaneously recorded spike trains from control expiratory phases were compared with data from interleaved equal-duration time blocks from control inspiratory phases. In each of seven animals, significant impulse synchrony detected by gravity analysis was confined to one phase of the respiratory cycle. Repeated patterns of distributed synchrony confined to periods of altered baroreceptor activity were detected and involved neurones that individually did not change firing rates during stimulation. Snowflakes and logical cross-correlation analysis provided evidence for the cooperative actions of impulses in concurrently active parallel channels. In 12 of 17 pairs of neurones with at least one baroresponsive cell, joint pericycle-triggered histograms detected synchrony indicative of shared inputs or functional excitatory interactions that varied as a function of time in the respiratory cycle. Neurones in four of the pairs had no respiratory modulation of their individual firing rates. Data from eight other pairs were indicative of fluctuations in inhibition during the respiratory cycle. The results demonstrate repeated transient configurations of baroresponsive neuronal assemblies during the respiratory cycle, without concomitant firing rate changes in the constituent neurones, and suggest distributed network mechanisms for the modulation of baroreceptor-mediated reflexes.

Mapping cognition to the brain through neural interactions

Brain imaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), provide a unique opportunity to study the neurobiology of human memory. As these methods can measure most of the brain, it is possible to examine the operations of large-scale neural systems and their relation to cognition. Two neuroimaging studies, one concerning working memory and the other episodic memory retrieval, serve as examples of application of two analytic methods that are optimised for the quantification of neural systems, structural equation modelling, and partial least squares. Structural equation modelling was used to explore shifting prefrontal and limbic interactions from the right to the left hemisphere in a delayed match-to-sample task for faces. A feature of the functional network for short delays was strong right hemisphere interactions between hippocampus, inferior prefrontal, and anterior cingulate cortices. At longer delays, these same three areas were strongly linked, but in the left hemisphere, which was interpreted as reflecting change in task strategy from perceptual to elaborate encoding with increasing delay. The primary manipulation in the memory retrieval study was different levels of retrieval success. The partial least squares method was used to determine whether the image-wide pattern of covariances of Brodmann areas 10 and 45/47 in right prefrontal cortex (RPFC) and the left hippocampus (LGH) could be mapped on to retrieval levels. Area 10 and LGH showed an opposite pattern of functional connectivity with a large expanse of bilateral limbic cortices that was equivalent for all levels of retrieval as well as the baseline task. However, only during high retrieval was area 45/47 included in this pattern. The results suggest that activity in portions of the RPFC can reflect either memory retrieval mode or retrieval success depending on other brain regions to which it is functionally linked, and imply that regional activity must be evaluated within the neural context in which it occurs. The general hypothesis that learning and memory are emergent properties of large-scale neural network interactions is discussed, emphasising that a region can play a different role across many functions and that role is governed by its interactions with anatomically related regions.

Responses of simultaneously recorded respiratory-related medullary neurons to stimulation of multiple sensory modalities

This study addresses the hypothesis that multiple afferent systems share elements of a distributed brain stem network that modulates the respiratory motor pattern. Data were collected from 18 decerebrate, bilaterally vagotomized, paralyzed, artificially ventilated cats. Up to 28 neurons distributed in the rostral and caudal ventral respiratory group, nucleus tractus solitarius, and raphe obscurus were recorded simultaneously with microelectrode arrays. Phases of the respiratory cycle and inspiratory drive were assessed from integrated efferent phrenic nerve activity. Carotid chemoreceptors were stimulated by injection of CO2-saturated saline solution via the external carotid artery. Baroreceptors were stimulated by increased blood pressure secondary to inflation of an embolectomy catheter in the descending aorta. Cutaneous nociceptors were stimulated by pinching a footpad. Four hundred seventy-four neurons were tested for respiratory modulated firing rates and responses; 403 neurons were tested with stimulation of all 3 modalities. Chemoreceptor stimulation and pinch, perturbations that tend to increase respiratory drive, caused similar responses in 52 neurons; 28 responded oppositely. Chemoreceptor and baroreceptor stimulation resulted in similar primary responses in 45 neurons; 48 responded oppositely. Similar responses to baroreceptor stimulation and pinch were recorded for 38 neurons; opposite effects were measured in 26 neurons. Among simultaneously recorded neurons, distinct combinations of firing rate changes were evoked in response to stimulation of the different modalities. The results show a functional convergence of information from carotid chemoreceptors, baroreceptors, and cutaneous nociceptors on respiratory-modulated neurons distributed in the medulla. The data are consistent with the hypothesis that brain stem neurons have overlapping memberships in multifunctional groups that influence the respiratory motor pattern.

Multimodal medullary neurons and correlational linkages of the respiratory network

This study addresses the hypothesis that multiple sensory systems, each capable of reflexly altering breathing, jointly influence neurons of the brain stem respiratory network. Carotid chemoreceptors, baroreceptors, and foot pad nociceptors were stimulated sequentially in 33 Dial-urethan-anesthetized or decerebrate vagotomized adult cats. Neuronal impulses were monitored with microelectrode arrays in the rostral and caudal ventral respiratory group (VRG), nucleus tractus solitarius (NTS), and n. raphe obscurus. Efferent phrenic nerve activity was recorded. Spike trains of 889 neurons were analyzed with cycle-triggered histograms and tested for respiratory-modulated firing rates. Responses to stimulus protocols were assessed with peristimulus time and cumulative sum histograms. Cross-correlation analysis was used to test for nonrandom temporal relationships between spike trains. Spike-triggered averages of efferent phrenic activity and antidromic stimulation methods provided evidence for functional associations of bulbar neurons with phrenic motoneurons. Spike train cross-correlograms were calculated for 6,471 pairs of neurons. Significant correlogram features were detected for 425 pairs, including 189 primary central peaks or troughs, 156 offset peaks or troughs, and 80 pairs with multiple peaks and troughs. The results provide evidence that correlational medullary assemblies include neurons with overlapping memberships in groups responsive to different sets of sensory modalities. The data suggest and support several hypotheses concerning cooperative relationships that modulate the respiratory motor pattern. 1) Neurons responsive to a single tested modality promote or limit changes in firing rate of multimodal target neurons. 2) Multimodal neurons contribute to changes in firing rate of neurons responsive to a single tested modality. 3) Multimodal neurons may promote responses during stimulation of one modality and "limit" changes in firing rates during stimulation of another sensory modality. 4) Caudal VRG inspiratory neurons have inhibitory connections that provide negative feedback regulation of inspiratory drive and phase duration.

Medullary raphe neurones and baroreceptor modulation of the respiratory motor pattern in the cat

1. Perturbations of arterial blood pressure change medullary raphe neurone activity and the respiratory motor pattern. This study sought evidence for actions of baroresponsive raphe neurones on the medullary respiratory network. 2. Blood pressure was perturbed by intravenous injection of an alpha1-adrenergic receptor agonist, unilateral pressure changes in the carotid sinus, or occlusion of the descending aorta in thirty-six Dial-urethane-anaesthetized, vagotomized, paralysed, artificially ventilated cats. Neurones were monitored with microelectrode arrays in two or three of the following domains: nucleus raphe obscurus-nucleus raphe pallidus, nucleus raphe magnus, and rostral and caudal ventrolateral medulla. Data were analysed with cycle-triggered histograms, peristimulus time and cumulative sum histograms, cross-correlograms and spike-triggered averages of efferent phrenic nerve activity. 3. Prolongation of the expiratory phase and decreased peak integrated phrenic amplitude were most frequently observed. Of 707 neurones studied, 310 had altered firing rates during stimulation; changes in opposite directions were monitored simultaneously in fifty-six of eighty-seven data sets with at least two baroresponsive neurones. 4. Short time scale correlations were detected between neurones in 347 of 3388 pairs. Seventeen pairs of baroresponsive raphe neurones exhibited significant offset correlogram features indicative of paucisynaptic interactions. In correlated raphe-ventrolateral medullary neurone pairs with at least one baroresponsive neurone, six of seven ventrolateral medullary decrementing expiratory (E-Decr) neurones increased their firing rate during baroreceptor stimulation. Thirteen of fifteen ventrolateral medullary inspiratory neurones correlated with raphe cells decreased their firing rate during baroreceptor stimulation. 5. The results support the hypothesis that raphe neuronal assemblies transform and transmit information from baroreceptors to neurones in the ventral respiratory group. The inferred actions both limit and promote responses to sensory perturbations and match predictions from simulations of the respiratory network.