Metastability of active CA3 networks

Spectral convergence in tapping and physiological fluctuations: coupling and independence of 1/f noise in the central and autonomic nervous systems

When humans perform a response task or timing task repeatedly, fluctuations in measures of timing from one action to the next exhibit long-range correlations known as 1/f noise. The origins of 1/f noise in timing have been debated for over 20 years, with one common explanation serving as a default: humans are composed of physiological processes throughout the brain and body that operate over a wide range of timescales, and these processes combine to be expressed as a general source of 1/f noise. To test this explanation, the present study investigated the coupling vs. independence of 1/f noise in timing deviations, key-press durations, pupil dilations, and heartbeat intervals while tapping to an audiovisual metronome. All four dependent measures exhibited clear 1/f noise, regardless of whether tapping was synchronized or syncopated. 1/f spectra for timing deviations were found to match those for key-press durations on an individual basis, and 1/f spectra for pupil dilations matched those in heartbeat intervals. Results indicate a complex, multiscale relationship among 1/f noises arising from common sources, such as those arising from timing functions vs. those arising from autonomic nervous system (ANS) functions. Results also provide further evidence against the default hypothesis that 1/f noise in human timing is just the additive combination of processes throughout the brain and body. Our findings are better accommodated by theories of complexity matching that begin to formalize multiscale coordination as a foundation of human behavior.

Focus on desynchronization rather than excitability: a new strategy for intraencephalic electrical stimulation

Epilepsy is a severely debilitating brain disease, often associated with premature death, which has an urgent need for alternative methods of treatment. In fact, roughly 25% of patients with epilepsy do not have seizures satisfactorily controlled by pharmacological treatment, and 30% of these patients with treatment-refractory seizures are not even eligible for ablative surgery. Epilepsy is most readily identifiable by its seizures and/or paroxysmal events, mostly viewed as spontaneously recurrent and unpredictable, which are caused by stereotyped changes in neurological function associated with hyperexcitability and hypersynchronicity of the underlying neural networks. Treatment has strongly been based on the fixed goal of depressing neuronal activity, working under the veiled assumption that hyperexcitability would lead to synchronous neuronal activity and, therefore, to seizure. Over the last 20-30 years, the emergence of electrical (ES) of deep brain structures, a practicable option for treating patients with otherwise untreatable seizures, has broadened our understanding of anticonvulsant mechanisms that conceptually differ from those of pharmacological treatment. Conversely, the research on ES therapy applied to epilepsy is contributing significantly to untwine the phenomena of excitation from that of synchronization as potential target mechanisms for abolishing seizures and predicting paroxysmal events. This paper is, thus, an addendum to other reviews on the subject of ES therapy in epilepsy which focuses on the desynchronization effect ES has on epileptogenic neural networks rather than its effect on overall brain excitability.

Dopamine receptor activation reorganizes neuronal ensembles during hippocampal sharp waves in vitro

Hippocampal sharp wave (SW)/ripple complexes are thought to contribute to memory consolidation. Previous studies suggest that behavioral rewards facilitate SW occurrence in vivo. However, little is known about the precise mechanism underlying this enhancement. Here, we examined the effect of dopaminergic neuromodulation on spontaneously occurring SWs in acute hippocampal slices. Local field potentials were recorded from the CA1 region. A brief (1 min) treatment with dopamine led to a persistent increase in the event frequency and the magnitude of SWs. This effect lasted at least for our recording period of 45 min and did not occur in the presence of a dopamine D1/D5 receptor antagonist. Functional multineuron calcium imaging revealed that dopamine-induced SW augmentation was associated with an enriched repertoire of the firing patterns in SW events, whereas the overall tendency of individual neurons to participate in SWs and the mean number of cells participating in a single SW were maintained. Therefore, dopaminergic activation is likely to reorganize cell assemblies during SWs.

Astrocyte calcium signalling orchestrates neuronal synchronization in organotypic hippocampal slices

Astrocytes are thought to detect neuronal activity in the form of intracellular calcium elevations; thereby, astrocytes can regulate neuronal excitability and synaptic transmission. Little is known, however, about how the astrocyte calcium signal regulates the activity of neuronal populations. In this study, we addressed this issue using functional multineuron calcium imaging in hippocampal slice cultures. Under normal conditions, CA3 neuronal networks exhibited temporally correlated activity patterns, occasionally generating large synchronization among a subset of cells. The synchronized neuronal activity was correlated with astrocyte calcium events. Calcium buffering by an intracellular injection of a calcium chelator into multiple astrocytes reduced the synaptic strength of unitary transmission between pairs of surrounding pyramidal cells and caused desynchronization of the neuronal networks. Uncaging the calcium in the astrocytes increased the frequency of neuronal synchronization. These data suggest an essential role of the astrocyte calcium signal in the maintenance of basal neuronal function at the circuit level.

Interneuron firing precedes sequential activation of neuronal ensembles in hippocampal slices

Neuronal firing sequences that occur during behavioral tasks are precisely reactivated in the neocortex and the hippocampus during rest and sleep. These precise firing sequences are likely to reflect latent memory traces, and their reactivation is believed to be essential for memory consolidation and working memory maintenance. However, how the organized repeating patterns emerge through the coordinated interplay of distinct types of neurons remains unclear. In this study, we monitored ongoing spatiotemporal firing patterns using a multi-neuron calcium imaging technique and examined how the activity of individual neurons is associated with repeated ensembles in hippocampal slice cultures. To determine the cell types of the imaged neurons, we applied an optical synapse mapping method that identifies network connectivity among dozens of neurons. We observed that inhibitory interneurons exhibited an increase in their firing rates prior to the onset of repeating sequences, while the overall activity level of excitatory neurons remained unchanged. A specific repeating sequence emerged preferentially after the firing of a specific interneuron that was located close to the neuron first activated in the sequence. The times of repeating sequences could be more precisely predicted based on the activity patterns of inhibitory cells than excitatory cells. In line with these observations, stimulation of a single interneuron could trigger the emergence of repeating sequences. These findings provide a conceptual framework that interneurons serve as a key regulator of initiating sequential spike activity.

Cooperative integration and representation underlying bilateral network of fly motion-sensitive neurons

How is binocular motion information integrated in the bilateral network of wide-field motion-sensitive neurons, called lobula plate tangential cells (LPTCs), in the visual system of flies? It is possible to construct an accurate model of this network because a complete picture of synaptic interactions has been experimentally identified. We investigated the cooperative behavior of the network of horizontal LPTCs underlying the integration of binocular motion information and the information representation in the bilateral LPTC network through numerical simulations on the network model. First, we qualitatively reproduced rotational motion-sensitive response of the H2 cell previously reported in vivo experiments and ascertained that it could be accounted for by the cooperative behavior of the bilateral network mainly via interhemispheric electrical coupling. We demonstrated that the response properties of single H1 and Hu cells, unlike H2 cells, are not influenced by motion stimuli in the contralateral visual hemi-field, but that the correlations between these cell activities are enhanced by the rotational motion stimulus. We next examined the whole population activity by performing principal component analysis (PCA) on the population activities of simulated LPTCs. We showed that the two orthogonal patterns of correlated population activities given by the first two principal components represent the rotational and translational motions, respectively, and similar to the H2 cell, rotational motion produces a stronger response in the network than does translational motion. Furthermore, we found that these population-coding properties are strongly influenced by the interhemispheric electrical coupling. Finally, to test the generality of our conclusions, we used a more simplified model and verified that the numerical results are not specific to the network model we constructed.

Deep brain stimulation: are astrocytes a key driver behind the scene?

Despite its widespread use, the underlying mechanism of deep brain stimulation (DBS) remains unknown. Once thought to impart a "functional inactivation", there is now increasing evidence showing that DBS actually can both inhibit neurons and activate axons, generating a wide range of effects. This implies that the mechanisms that underlie DBS work not only locally but also at the network level. Therefore, not only may DBS induce membrane or synaptic plastic changes in neurons over a wide network, but it may also trigger cellular and molecular changes in other cells, especially astrocytes, where, together, the glial-neuronal interactions may explain effects that are not clearly rationalized by simple activation/inhibition theories alone. Recent studies suggest that (1) high-frequency stimulation (HFS) activates astrocytes and leads to the release of gliotransmitters that can regulate surrounding neurons at the synapse; (2) activated astrocytes modulate synaptic activity and increase axonal activation; (3) activated astrocytes can signal further astrocytes across large networks, contributing to observed network effects induced by DBS; (4) activated astrocytes can help explain the disparate effects of activation and inhibition induced by HFS at different sites; (5) astrocytes contribute to synaptic plasticity through long-term potentiation (LTP) and depression (LTD), possibly helping to mediate the long-term effects of DBS; and (6) DBS may increase delta-opioid receptor activity in astrcoytes to confer neuroprotection. Together, the plastic changes in these glial-neuronal interactions network-wide likely underlie the range of effects seen, from the variable temporal latencies to observed effect to global activation patterns. This article reviews recent research progress in the literature on how astrocytes play a key role in DBS efficacy.

Global actions of nicotine on the striatal microcircuit

The question to solve in the present work is: what is the predominant action induced by the activation of cholinergic-nicotinic receptors (nAChrs) in the striatal network given that nAChrs are expressed by several elements of the circuit: cortical terminals, dopamine terminals, and various striatal GABAergic interneurons. To answer this question some type of multicellular recording has to be used without losing single cell resolution. Here, we used calcium imaging and nicotine. It is known that in the presence of low micromolar N-Methyl-D-aspartate (NMDA), the striatal microcircuit exhibits neuronal activity consisting in the spontaneous synchronization of different neuron pools that interchange their activity following determined sequences. The striatal circuit also exhibits profuse spontaneous activity in pathological states (without NMDA) such as dopamine depletion. However, in this case, most pathological activity is mostly generated by the same neuron pool. Here, we show that both types of activity are inhibited during the application of nicotine. Nicotine actions were blocked by mecamylamine, a non-specific antagonist of nAChrs. Interestingly, inhibitory actions of nicotine were also blocked by the GABA_A-receptor antagonist bicuculline, in which case, the actions of nicotine on the circuit became excitatory and facilitated neuronal synchronization. We conclude that the predominant action of nicotine in the striatal microcircuit is indirect, via the activation of networks of inhibitory interneurons. This action inhibits striatal pathological activity in early Parkinsonian animals almost as potently as L-DOPA.

Multineuronal spike sequences repeat with millisecond precision

Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging (fMCI) technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and non-spiking neurons. Multineuronal spike sequences (MSSs) were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

Optimal balance of the striatal medium spiny neuron network

Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of 10 ~ 20% and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around 15% connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics - it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation.

Oxygen consumption rates during three different neuronal activity states in the hippocampal CA3 network

The brain is an organ with high metabolic rate. However, little is known about energy utilization during different activity states of neuronal networks. We addressed this issue in area CA3 of hippocampal slice cultures under well-defined recording conditions using a 20% O(2) gas mixture. We combined recordings of local field potential and interstitial partial oxygen pressure (pO(2)) during three different activity states, namely fast network oscillations in the gamma-frequency band (30 to 100 Hz), spontaneous network activity and absence of spiking (action potentials). Oxygen consumption rates were determined by pO(2) depth profiles with high spatial resolution and a mathematical model that considers convective transport, diffusion, and activity-dependent consumption of oxygen. We show that: (1) Relative oxygen consumption rate during cholinergic gamma oscillations was 2.2-fold and 5.3-fold higher compared with spontaneous activity and absence of spiking, respectively. (2) Gamma oscillations were associated with a similar large decrease in pO(2) as observed previously with a 95% O(2) gas mixture. (3) Sufficient oxygenation during fast network oscillations in vivo is ensured by the calculated critical radius of 30 to 40 μm around a capillary. We conclude that the structural and biophysical features of brain tissue permit variations in local oxygen consumption by a factor of about five.

Layer III neurons control synchronized waves in the immature cerebral cortex

Correlated spiking activity prevails in immature cortical networks and is believed to contribute to neuronal circuit maturation; however, its spatiotemporal organization is not fully understood. Using wide-field calcium imaging from acute whole-brain slices of rat pups on postnatal days 1-6, we found that correlated spikes were initiated in the anterior part of the lateral entorhinal cortex and propagated anteriorly to the frontal cortex and posteriorly to the medial entorhinal cortex, forming traveling waves that engaged almost the entire cortex. The waves were blocked by ionotropic glutamatergic receptor antagonists but not by GABAergic receptor antagonists. During wave events, glutamatergic and GABAergic synaptic inputs were balanced and induced UP state-like depolarization. Magnified monitoring with cellular resolution revealed that the layer III neurons were first activated when the waves were initiated. Consistent with this finding, layer III contained a larger number of neurons that were autonomously active, even under a blockade of synaptic transmission. During wave propagation, the layer III neurons constituted a leading front of the wave. The waves did not enter the parasubiculum; however, in some cases, they were reflected at the parasubicular border and propagated back in the opposite direction. During this reflection process, the layer III neurons in the medial entorhinal cortex maintained persistent activity. Thus, our data emphasize the role of layer III in early network behaviors and provide insight into the circuit mechanisms through which cerebral cortical networks maturate.

Novel modulatory effects of neurosteroids and benzodiazepines on excitatory and inhibitory neurons excitability: a multi-electrode array recording study

The balance between glutamate- and GABA-mediated neurotransmission in the brain is fundamental in the nervous system, but it is regulated by the “tonic” release of a variety of endogenous factors. One such important group of molecules are the neurosteroids (NSs) which, similarly to benzodiazepines (BDZs), enhance GABAergic neurotransmission. The purpose of our work was to investigate, at in vivo physiologically relevant concentrations, the effects of NSs and BDZs as GABA modulators on dissociated neocortical neuron networks grown in long-term culture. We used a multi-electrode array (MEA) recording technique and a novel analysis that was able to both identify the action potentials of engaged excitatory and inhibitory neurons and to detect drug-induced network up-states (burst). We found that the NSs tetrahydrodeoxycorticosterone (THDOC) and allopregnanolone (ALLO) applied at low nanomolar concentrations, produced different modulatory effects on the two neuronal clusters. Conversely, at high concentrations (1 μM), both NSs, decreased excitatory and inhibitory neuron cluster excitability; however, even several hours after wash-out, the excitability of inhibitory neurons continued to be depressed, leading to a network long-term depression (LTD). The BDZs clonazepam (CLZ) and midazolam (MDZ) also decreased the network excitability, but only MDZ caused LTD of inhibitory neuron cluster. To investigate the origin of the LTD after MDZ application, we tested finasteride (FIN), an inhibitor of endogenous NSs synthesis. FIN did not prevent the LTD induced by MDZ, but surprisingly induced it after application of CLZ. The significance and possible mechanisms underlying these LTD effects of NSs and BDZs are discussed. Taken together, our results not only demonstrate that ex vivo networks show a sensitivity to NSs and BDZs comparable to that expressed in vivo, but also provide a new global in vitro description that can help in understanding their activity in more complex systems.

Extracting functionally feedforward networks from a population of spiking neurons

Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABA(A) receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV/DNQX, consistent with fading feedforward activation. The observed alterations in FFCs provide experimental support for recent theoretical work linking power-law avalanches to the feedforward organization of functional connections in local neuronal circuits.

Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.

Unraveling the general organizing principles of connectivity in neural circuits is a crucial step towards understanding brain function. However, even the simpler task of assessing the global excitatory connectivity of a culture in vitro, where neurons form self-organized networks in absence of external stimuli, remains challenging. Neuronal cultures undergo spontaneous switching between episodes of synchronous bursting and quieter inter-burst periods. We introduce here a novel algorithm which aims at inferring the connectivity of neuronal cultures from calcium fluorescence recordings of their network dynamics. To achieve this goal, we develop a suitable generalization of Transfer Entropy, an information-theoretic measure of causal influences between time series. Unlike previous algorithmic approaches to reconstruction, Transfer Entropy is data-driven and does not rely on specific assumptions about neuronal firing statistics or network topology. We generate simulated calcium signals from networks with controlled ground-truth topology and purely excitatory interactions and show that, by restricting the analysis to inter-bursts periods, Transfer Entropy robustly achieves a good reconstruction performance for disparate network connectivities. Finally, we apply our method to real data and find evidence of non-random features in cultured networks, such as the existence of highly connected hub excitatory neurons and of an elevated (but not extreme) level of clustering.

Neuroligin-1 loss is associated with reduced tenacity of excitatory synapses

Neuroligins (Nlgns) are postsynaptic, integral membrane cell adhesion molecules that play important roles in the formation, validation, and maturation of synapses in the mammalian central nervous system. Given their prominent roles in the life cycle of synapses, it might be expected that the loss of neuroligin family members would affect the stability of synaptic organization, and ultimately, affect the tenacity and persistence of individual synaptic junctions. Here we examined whether and to what extent the loss of Nlgn-1 affects the dynamics of several key synaptic molecules and the constancy of their contents at individual synapses over time. Fluorescently tagged versions of the postsynaptic scaffold molecule PSD-95, the AMPA-type glutamate receptor subunit GluA2 and the presynaptic vesicle molecule SV2A were expressed in primary cortical cultures from Nlgn-1 KO mice and wild-type (WT) littermates, and live imaging was used to follow the constancy of their contents at individual synapses over periods of 8-12 hours. We found that the loss of Nlgn-1 was associated with larger fluctuations in the synaptic contents of these molecules and a poorer preservation of their contents at individual synapses. Furthermore, rates of synaptic turnover were somewhat greater in neurons from Nlgn-1 knockout mice. Finally, the increased GluA2 redistribution rates observed in neurons from Nlgn-1 knockout mice were negated by suppressing spontaneous network activity. These findings suggest that the loss of Nlgn-1 is associated with some use-dependent destabilization of excitatory synapse organization, and indicate that in the absence of Nlgn-1, the tenacity of excitatory synapses might be somewhat impaired.

Preinspiratory calcium rise in putative pre-Botzinger complex astrocytes

The neural inspiratory activity originates from a ventrolateral medullary region called the pre-Bötzinger complex (preBötC), yet the mechanism underlying respiratory rhythmogenesis is not completely understood. Recently, the role of not only neurons but astrocytes in the central respiratory control has attracted considerable attention. Here we report our discovery that an intracellular calcium rise in a subset of putative astrocytes precedes inspiratory neuronal firing in rhythmically active slices. Functional calcium imaging from hundreds of preBötC cells revealed that a subset of putative astrocytes exhibited rhythmic calcium elevations preceding inspiratory neuronal activity with a time lag of approximately 2 s. These preinspiratory putative astrocytes maintained their rhythmic activities even during the blockade of neuronal activity with tetrodotoxin, whereas the rhythm frequency was lowered and the intercellular phases of these rhythms were decoupled. In addition, optogenetic stimulation of preBötC putative astrocytes induced firing of inspiratory neurons. These findings raise the possibility that astrocytes in the preBötC are actively involved in respiratory rhythm generation in rhythmically active slices.

Bifurcations of emergent bursting in a neuronal network

Complex neuronal networks are an important tool to help explain paradoxical phenomena observed in biological recordings. Here we present a general approach to mathematically tackle a complex neuronal network so that we can fully understand the underlying mechanisms. Using a previously developed network model of the milk-ejection reflex in oxytocin cells, we show how we can reduce a complex model with many variables and complex network topologies to a tractable model with two variables, while retaining all key qualitative features of the original model. The approach enables us to uncover how emergent synchronous bursting can arise from a neuronal network which embodies known biological features. Surprisingly, the bursting mechanisms are similar to those found in other systems reported in the literature, and illustrate a generic way to exhibit emergent and multiple time scale oscillations at the membrane potential level and the firing rate level.

Muscarinic receptor activation disrupts hippocampal sharp wave-ripples

Cholinergic muscarinic innervations to the hippocampus play a role in learning and memory. Here we report that pharmacological activation of muscarinic receptors eliminates sharp wave-ripple events in the mouse hippocampal CA1 region in vivo and in vitro. This effect was associated with a decorrelation of excitatory synaptic inputs and a net increase in inhibitory conductances in pyramidal neurons. Multineuron calcium imaging revealed that muscarinic activation altered the spatiotemporal pattern of network activities. Thus, cholinergic input is likely to contribute to a neuromodulatory switch of hippocampal network states, as proposed in the "two-stage" model of learning processes.

Effects of axonal topology on the somatic modulation of synaptic outputs

Depolarization of the neuronal soma augments synaptic output onto postsynaptic neurons via long-range, axonal cable properties. Here, we report that the range of this somatic influence is spatially restricted by not only axonal path length but also a branching-dependent decrease in axon diameter. Cell-attached recordings of action potentials (APs) from multiple axon branches of a rat hippocampal CA3 pyramidal cell revealed that an AP was broadened following a 20 mV depolarization of the soma and reverted to a normal width during propagation down the axon. The narrowing of the AP depended on the distance traveled by the AP and on the number of axon branch points through which the AP passed. These findings were confirmed by optical imaging of AP-induced calcium elevations in presynaptic boutons, suggesting that the somatic membrane potential modifies synaptic outputs near the soma but not long-projection outputs. Consistent with this prediction, whole-cell recordings from synaptically connected neurons revealed that depolarization of presynaptic CA3 pyramidal cells facilitated synaptic transmission to nearby CA3 pyramidal cells, but not to distant pyramidal cells in CA3 or CA1. Therefore, axonal geometry enables the differential modulation of synaptic output depending on target location.

Modeling rhythmic patterns in the hippocampus

We investigate different dynamical regimes of the neuronal network in the CA3 area of the hippocampus. The proposed neuronal circuit includes two fast- and two slowly spiking cells which are interconnected by means of dynamical synapses. On the individual level, each neuron is modeled by FitzHugh-Nagumo equations. Three basic rhythmic patterns are observed: the gamma rhythm in which the fast neurons are uniformly spiking, the theta rhythm in which the individual spikes are separated by quiet epochs, and the theta-gamma rhythm with repeated patches of spikes. We analyze the influence of asymmetry of synaptic strengths on the synchronization in the network and demonstrate that strong asymmetry reduces the variety of available dynamical states. The model network exhibits multistability; this results in the occurrence of hysteresis in dependence on the conductances of individual connections. We show that switching between different rhythmic patterns in the network depends on the degree of synchronization between the slow cells.

Synchronized spike waves in immature dentate gyrus networks

At early developmental stages, immature neuronal networks of the neocortex and hippocampus spontaneously exhibit synchronously oscillating activities, which are believed to play roles in normal circuit maturation. The tissue development of the dentate gyrus (DG) in the hippocampal formation is exceptionally late compared with other brain regions and persists until postnatal periods. Using patch-clamp recording and functional multineuron calcium imaging, we found that the DG networks of postnatal day (P)3-7 mice spontaneously generated traveling waves of action potentials, which were initiated at the upper blade of the granule cell layer and propagated to the lower blade. The propagation was dependent on glutamatergic and electrical synapses, but not on GABAergic receptor activity. Remarkably, the DG waves were almost completely abolished in offspring born to female rats exposed to restraint stress during pregnancy. In the prenatally stressed offspring, DG granule cell dendrites developed normally until P3 and showed atrophy by P9. Thus, the DG waves may be required for the maturation of DG granule cells.

High-speed multineuron calcium imaging using Nipkow-type confocal microscopy

Conventional confocal and two-photon microscopy scan the field of view sequentially with single-point laser illumination. This raster-scanning method constrains video speeds to tens of frames per second, which are too slow to capture the temporal patterns of fast electrical events initiated by neurons. Nipkow-type spinning-disk confocal microscopy resolves this problem by the use of multiple laser beams. We describe experimental procedures for functional multineuron calcium imaging (fMCI) based on Nipkow-disk confocal microscopy, which enables us to monitor the activities of hundreds of neurons en masse at a cellular resolution at up to 2000 fps.

Interpyramid spike transmission stabilizes the sparseness of recurrent network activity

Cortical synaptic strengths vary substantially from synapse to synapse and exhibit a skewed distribution with a small fraction of synapses generating extremely large depolarizations. Using multiple whole-cell recordings from rat hippocampal CA3 pyramidal cells, we found that the amplitude of unitary excitatory postsynaptic conductances approximates a lognormal distribution and that in the presence of synaptic background noise, the strongest fraction of synapses could trigger action potentials in postsynaptic neurons even with single presynaptic action potentials, a phenomenon termed interpyramid spike transmission (IpST). The IpST probability reached 80%, depending on the network state. To examine how IpST impacts network dynamics, we simulated a recurrent neural network embedded with a few potent synapses. This network, unlike many classical neural networks, exhibited distinctive behaviors resembling cortical network activity in vivo. These behaviors included the following: 1) infrequent ongoing activity, 2) firing rates of individual neurons approximating a lognormal distribution, 3) asynchronous spikes among neurons, 4) net balance between excitation and inhibition, 5) network activity patterns that was robust against external perturbation, 6) responsiveness even to a single spike of a single excitatory neuron, and 7) precise firing sequences. Thus, IpST captures a surprising number of recent experimental findings in vivo. We propose that an unequally biased distribution with a few select strong synapses helps stabilize sparse neuronal activity, thereby reducing the total spiking cost, enhancing the circuit responsiveness, and ensuring reliable information transfer.

Use dependence of presynaptic tenacity

Recent studies indicate that synaptic vesicles (SVs) are continuously interchanged among nearby synapses at very significant rates. These dynamics and the lack of obvious barriers confining synaptic vesicles to specific synapses would seem to challenge the ability of synapses to maintain a constant amount of synaptic vesicles over prolonged time scales. Moreover, the extensive mobilization of synaptic vesicles associated with presynaptic activity might be expected to intensify this challenge. Here we examined the ability of individual presynaptic boutons of rat hippocampal neurons to maintain their synaptic vesicle content, and the degree to which this ability is affected by continuous activity. We found that the synaptic vesicle content of individual boutons belonging to the same axons gradually changed over several hours, and that these changes occurred independently of activity. Intermittent stimulation for 1 h accelerated rates of vesicle pool size change. Interestingly, however, following stimulation cessation, vesicle pool size change rates gradually converged with basal change rates. Over similar time scales, active zones (AZs) exhibited substantial remodeling; yet, unlike synaptic vesicles, AZ remodeling was not affected by the stimulation paradigms used here. These findings indicate that enhanced activity levels can increase synaptic vesicle redistribution among nearby synapses, but also highlight the presence of forces that act to restore particular set points in terms of SV contents, and support a role for active zones in preserving such set points. These findings also indicate, however, that neither AZ size nor SV content set points are particularly stable, questioning the long-term tenacity of presynaptic specializations.

Reliable optical detection of coherent neuronal activity in fast oscillating networks in vitro

Cognitive and behavioral functions depend on the activation of stable neuronal assemblies, i.e. distributed groups of co-active neurons within neuronal networks. It is therefore crucial to monitor distributed patterns of activity in real time with single-neuron resolution. Microelectrode recordings allow detection of coincidence between discharges of identified units at high temporal resolution, but are not able to reveal the full spatial pattern of activity in multi-cellular assemblies. Therefore, observation of such distributed sets of neurons is a stronghold of optical techniques, but the required resolution, sensitivity, and speed are still challenging current technology. Here, we report a new approach for monitoring neuronal assemblies, using memory-related network oscillations in rodent hippocampal circuits as a model. The cytosolic calcium-sensitive fluorescent protein GCaMP3.NES was expressed using recombinant adeno-associated viral (rAAV)-mediated gene transfer in CA3 pyramidal neurons of cultured mouse hippocampal slices. After 14-21 days in culture, field potential recordings revealed spontaneous occurrence of sharp wave-ripple network events during which a fraction of local neurons is coherently activated. Using a custom-built epi-fluorescence microscope we could monitor a field of view of 410 μm × 410 μm with single-neuron optical resolution (20× objective, 0.4 NA). We developed a highly sensitive and specific wavelet-based method of cell identification allowing simultaneous observation of more than 150 neurons at frame rates of up to 60 Hz. Our recording configuration and image analysis provide a tool to investigate cognition-related activity patterns in the hippocampus and other circuits.

Dopaminergic modulation of the striatal microcircuit: receptor-specific configuration of cell assemblies

Selection and inhibition of motor behaviors are related to the coordinated activity and compositional capabilities of striatal cell assemblies. Striatal network activity represents a main step in basal ganglia processing. The dopaminergic system differentially regulates distinct populations of striatal medium spiny neurons (MSNs) through the activation of D(1)- or D(2)-type receptors. Although postsynaptic and presynaptic actions of these receptors are clearly different in MSNs during cell-focused studies, their activation during network activity has shown inconsistent responses. Therefore, using electrophysiological techniques, functional multicell calcium imaging, and neuronal population analysis in rat corticostriatal slices, we describe the effect of selective dopaminergic receptor activation in the striatal network by observing cell assembly configurations. At the microcircuit level, during striatal network activity, the selective activation of either D(1)- or D(2)-type receptors is reflected as overall increases in neuronal synchronization. However, graph theory techniques applied to the transitions between network states revealed receptor-specific configurations of striatal cell assemblies: D(1) receptor activation generated closed trajectories with high recurrence and few alternate routes favoring the selection of specific sequences, whereas D(2) receptor activation created trajectories with low recurrence and more alternate pathways while promoting diverse transitions among neuronal pools. At the single-cell level, the activation of dopaminergic receptors enhanced the negative-slope conductance region (NSCR) in D(1)-type-responsive cells, whereas in neurons expressing D(2)-type receptors, the NSCR was decreased. Consequently, receptor-specific network dynamics most probably result from the interplay of postsynaptic and presynaptic dopaminergic actions.

Activity-dependent regulation of synaptic strength by PSD-95 in CA1 neurons

CaMKII and PSD-95 are the two most abundant postsynaptic proteins in the postsynaptic density (PSD). Overexpression of either can dramatically increase synaptic strength and saturate long-term potentiation (LTP). To do so, CaMKII must be activated, but the same is not true for PSD-95; expressing wild-type PSD-95 is sufficient. This raises the question of whether PSD-95's effects are simply an equilibrium process [increasing the number of AMPA receptor (AMPAR) slots] or whether activity is somehow involved. To examine this question, we blocked activity in cultured hippocampal slices with TTX and found that the effects of PSD-95 overexpression were greatly reduced. We next studied the type of receptors involved. The effects of PSD-95 were prevented by antagonists of group I metabotropic glutamate receptors (mGluRs) but not by antagonists of ionotropic glutamate receptors. The inhibition of PSD-95-induced strengthening was not simply a result of inhibition of PSD-95 synthesis. To understand the mechanisms involved, we tested the role of CaMKII. Overexpression of a CaMKII inhibitor, CN19, greatly reduced the effect of PSD-95. We conclude that PSD-95 cannot itself increase synaptic strength simply by increasing the number of AMPAR slots; rather, PSD-95's effects on synaptic strength require an activity-dependent process involving mGluR and CaMKII.

Hippocampal polysynaptic computation

Neural circuitry is a self-organizing arithmetic device that converts input to output and thereby remodels its computational algorithm to produce more desired output; however, experimental evidence regarding the mechanism by which information is modified and stored while propagating across polysynaptic networks is sparse. We used functional multineuron calcium imaging to monitor the spike outputs from thousands of CA1 neurons in response to the stimulation of two independent sites of the dentate gyrus in rat hippocampal networks ex vivo. Only pyramidal cells were analyzed based on post hoc immunostaining. Some CA1 pyramidal cells were observed to fire action potentials only when both sites were simultaneously stimulated (AND-like neurons), whereas other neurons fired in response to either site of stimulation but not to concurrent stimulation (XOR-like neurons). Both types of neurons were interlaced in the same network and altered their logical operation depending on the timing of paired stimulation. Repetitive paired stimulation for brief periods induced a persistent reorganization of AND and XOR operators, suggesting a flexibility in parallel distributed processing. We simulated these network functions in silico and found that synaptic modification of the CA3 recurrent excitation is pivotal to the shaping of logic plasticity. This work provides new insights into how microscopic synaptic properties are associated with the mesoscopic dynamics of complex microcircuits.

Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity

Rhythms at slow (<1 Hz) frequency of alternating Up and Down states occur during slow-wave sleep states, under deep anaesthesia and in cortical slices of mammals maintained in vitro. Such spontaneous oscillations result from the interplay between network reverberations nonlinearly sustained by a strong synaptic coupling and a fatigue mechanism inhibiting the neurons firing in an activity-dependent manner. Varying pharmacologically the excitability level of brain slices we exploit the network dynamics underlying slow rhythms, uncovering an intrinsic anticorrelation between Up and Down state durations. Besides, a non-monotonic change of Down state duration is also observed, which shrinks the distribution of the accessible frequencies of the slow rhythms. Attractor dynamics with activity-dependent self-inhibition predicts a similar trend even when the system excitability is reduced, because of a stability loss of Up and Down states. Hence, such cortical rhythms tend to display a maximal size of the distribution of Up/Down frequencies, envisaging the location of the system dynamics on a critical boundary of the parameter space. This would be an optimal solution for the system in order to display a wide spectrum of dynamical regimes and timescales.

Classifying heterogeneity of spontaneous up-states: a method for revealing variations in firing probability, engaged neurons and Fano factor

The dynamics of spontaneous and sensory-evoked up-states have been recently compared, in multi-site recordings in vivo and found to have similarities and differences. Also in vitro, this is evident because we here describe a novel computational method to classify into statistically different states the spontaneous reverberating activity recorded from long-term (12-18 days-in vitro) cultured cortical neurons (from 60-site multi-electrode arrays, MEA). State classification was performed by spike number time histograms (SNTH, or other burst features) of excitatory and inhibitory neuron clusters and revealed that in novel identified states the number of engaged neurons or up-state duration can change. To improve the characterization of each state we also computed the firing spike histograms (FSH) which revealed a new facet of the firing probability of clusters. In exemplary functional experiments we show that: (i) up to 6-7 states can be safely categorized during several hours of recordings without observing spike rate changes, (ii) they disappear after a short pharmacological stimulation being replaced with novel states active and living up to 6-8 h, (iii) antagonists in the nM range can split the activity of a homogeneous network into the chronological coexistence of 2 states, one completely different and one not significantly different from control state. In conclusion, we believe that this novel procedure better characterizes the number of functional states of a network and opens up the possibility of predicting the elementary "vocabulary" used by small networks of neurons.

Asynchronously enhanced spiking activity of ischemic neuronal networks

Cerebral ischemia causes the depletion of oxygen and nutrition from brain tissues, and when persistent, results in irreversible damage to the cell function and survival. The cellular response to ischemic conditions and its mechanisms have been investigated widely in in vivo and in vitro experimental models, yet no study has addressed the response of a whole neuronal network to energy deprivation with the single-cell resolution. Observations at the level of network are necessary, because the activity of individual neurons is nonlinearly integrated through a network and thereby gives rise to unexpectedly complex dynamics. Here we used functional multineuron calcium imaging (fMCI), an optical recording technique with high temporal and spatial resolution, to visualize the activity of neuron populations in hippocampus CA1 region under ischemia-like conditions ex vivo. We found that, although neurons responded to oxygen and glucose deprivation with an increase in the event frequency, they maintained an asynchronous network state. This is in contrast with other well known pathological states, in which the network hyperexcitability is usually accompanied by an increase in synchrony. We suggest that under ischemic conditions, at least to some time point, the neuronal network maintains the excitatory and inhibitory balance as a whole, whether actively or as a consequence of the cellular response to energy deprivation.

Robust transient dynamics and brain functions

In the last few decades several concepts of dynamical systems theory (DST) have guided psychologists, cognitive scientists, and neuroscientists to rethink about sensory motor behavior and embodied cognition. A critical step in the progress of DST application to the brain (supported by modern methods of brain imaging and multi-electrode recording techniques) has been the transfer of its initial success in motor behavior to mental function, i.e., perception, emotion, and cognition. Open questions from research in genetics, ecology, brain sciences, etc., have changed DST itself and lead to the discovery of a new dynamical phenomenon, i.e., reproducible and robust transients that are at the same time sensitive to informational signals. The goal of this review is to describe a new mathematical framework - heteroclinic sequential dynamics - to understand self-organized activity in the brain that can explain certain aspects of robust itinerant behavior. Specifically, we discuss a hierarchy of coarse-grain models of mental dynamics in the form of kinetic equations of modes. These modes compete for resources at three levels: (i) within the same modality, (ii) among different modalities from the same family (like perception), and (iii) among modalities from different families (like emotion and cognition). The analysis of the conditions for robustness, i.e., the structural stability of transient (sequential) dynamics, give us the possibility to explain phenomena like the finite capacity of our sequential working memory - a vital cognitive function -, and to find specific dynamical signatures - different kinds of instabilities - of several brain functions and mental diseases.

Large-scale calcium waves traveling through astrocytic networks in vivo

Macroscopic changes in cerebral blood flow, such as those captured by functional imaging of the brain, require highly organized, large-scale dynamics of astrocytes, glial cells that interact with both neuronal and cerebrovascular networks. However, astrocyte activity has been studied mainly at the level of individual cells, and information regarding their collective behavior is lacking. In this work, we monitored calcium activity simultaneously from hundreds of mouse hippocampal astrocytes in vivo and found that almost all astrocytes participated en masse in regenerative waves that propagated from cell to cell (referred to here as "glissandi"). Glissandi emerged depending on the neuronal activity and accompanied a reduction in infraslow fluctuations of local field potentials and a decrease in the flow of red blood cells. This novel phenomenon was heretofore overlooked, probably because of the high vulnerability of astrocytes to light damage; glissandi occurred only when observed at much lower laser intensities than previously used.

Spike-train communities: finding groups of similar spike trains

Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.

Learning shapes spontaneous activity itinerating over memorized states

Learning is a process that helps create neural dynamical systems so that an appropriate output pattern is generated for a given input. Often, such a memory is considered to be included in one of the attractors in neural dynamical systems, depending on the initial neural state specified by an input. Neither neural activities observed in the absence of inputs nor changes caused in the neural activity when an input is provided were studied extensively in the past. However, recent experimental studies have reported existence of structured spontaneous neural activity and its changes when an input is provided. With this background, we propose that memory recall occurs when the spontaneous neural activity changes to an appropriate output activity upon the application of an input, and this phenomenon is known as bifurcation in the dynamical systems theory. We introduce a reinforcement-learning-based layered neural network model with two synaptic time scales; in this network, I/O relations are successively memorized when the difference between the time scales is appropriate. After the learning process is complete, the neural dynamics are shaped so that it changes appropriately with each input. As the number of memorized patterns is increased, the generated spontaneous neural activity after learning shows itineration over the previously learned output patterns. This theoretical finding also shows remarkable agreement with recent experimental reports, where spontaneous neural activity in the visual cortex without stimuli itinerate over evoked patterns by previously applied signals. Our results suggest that itinerant spontaneous activity can be a natural outcome of successive learning of several patterns, and it facilitates bifurcation of the network when an input is provided.

Nonlinear dynamics of emotion-cognition interaction: when emotion does not destroy cognition?

Emotion (i.e., spontaneous motivation and subsequent implementation of a behavior) and cognition (i.e., problem solving by information processing) are essential to how we, as humans, respond to changes in our environment. Recent studies in cognitive science suggest that emotion and cognition are subserved by different, although heavily integrated, neural systems. Understanding the time-varying relationship of emotion and cognition is a challenging goal with important implications for neuroscience. We formulate here the dynamical model of emotion-cognition interaction that is based on the following principles: (1) the temporal evolution of cognitive and emotion modes are captured by the incoming stimuli and competition within and among themselves (competition principle); (2) metastable states exist in the unified emotion-cognition phase space; and (3) the brain processes information with robust and reproducible transients through the sequence of metastable states. Such a model can take advantage of the often ignored temporal structure of the emotion-cognition interaction to provide a robust and generalizable method for understanding the relationship between brain activation and complex human behavior. The mathematical image of the robust and reproducible transient dynamics is a Stable Heteroclinic Sequence (SHS), and the Stable Heteroclinic Channels (SHCs). These have been hypothesized to be possible mechanisms that lead to the sequential transient behavior observed in networks. We investigate the modularity of SHCs, i.e., given a SHS and a SHC that is supported in one part of a network, we study conditions under which the SHC pertaining to the cognition will continue to function in the presence of interfering activity with other parts of the network, i.e., emotion.

Collective stability of networks of winner-take-all circuits

The neocortex has a remarkably uniform neuronal organization, suggesting that common principles of processing are employed throughout its extent. In particular, the patterns of connectivity observed in the superficial layers of the visual cortex are consistent with the recurrent excitation and inhibitory feedback required for cooperative-competitive circuits such as the soft winner-take-all (WTA). WTA circuits offer interesting computational properties such as selective amplification, signal restoration, and decision making. But these properties depend on the signal gain derived from positive feedback, and so there is a critical trade-off between providing feedback strong enough to support the sophisticated computations while maintaining overall circuit stability. The issue of stability is all the more intriguing when one considers that the WTAs are expected to be densely distributed through the superficial layers and that they are at least partially interconnected. We consider how to reason about stability in very large distributed networks of such circuits. We approach this problem by approximating the regular cortical architecture as many interconnected cooperative-competitive modules. We demonstrate that by properly understanding the behavior of this small computational module, one can reason over the stability and convergence of very large networks composed of these modules. We obtain parameter ranges in which the WTA circuit operates in a high-gain regime, is stable, and can be aggregated arbitrarily to form large, stable networks. We use nonlinear contraction theory to establish conditions for stability in the fully nonlinear case and verify these solutions using numerical simulations. The derived bounds allow modes of operation in which the WTA network is multistable and exhibits state-dependent persistent activities. Our approach is sufficiently general to reason systematically about the stability of any network, biological or technological, composed of networks of small modules that express competition through shared inhibition.

Investigating and modelling pituitary endocrine network function

Endocrine cells in the mammalian pituitary are arranged into three-dimensional homotypic networks that wire the gland and act to optimise hormone output by allowing the transmission of information between cell ensembles in a temporally precise manner. Despite this, the structure-function relationships that allow cells belonging to these networks to display coordinated activity remain relatively uncharacterised. This review discusses the recent technological advances that have allowed endocrine cell network structure and function to be probed and the mathematical models that can be used to analyse and present the resulting data. In particular, we focus on the mechanisms that allow endocrine cells to dynamically function as a population to drive hormone release as well as the experimental and theoretical methods that are used to track and model information flow through the network.

Dynamics of the Parkinsonian striatal microcircuit: entrainment into a dominant network state

Neuronal synchronization in basal ganglia circuits plays a key role in the encoding of movement, procedural memory storage and habit formation. Striatal dopamine (DA) depletion during Parkinsonism causes abnormal synchronization in corticobasal ganglia loops resulting in motor dysfunction. However, the dynamics of the striatal microcircuit underlying abnormal synchronization in Parkinsonism is poorly understood. Here we used targeted whole-cell recordings, calcium imaging allowing the recording from dozens of cells simultaneously and analytical approaches, to describe the striking alterations in network dynamics that the striatal microcircuit undergoes following DA depletion in a rat model of Parkinson disease (PD): In addition to a significant enhancement of basal neuronal activity frequent periods of spontaneous synchronization were observed. Multidimensional reduction techniques of vectorized network dynamics revealed that increased synchronization resulted from a dominant network state that absorbed most spontaneously active cells. Abnormal synchronous activity can be virtually abolished by glutamatergic antagonists, while blockade of GABAergic transmission facilitates the engagement of striatal cell assemblies in the dominant state. Finally, a dopaminergic receptor agonist was capable of uncoupling neurons from the dominant state. Abnormal synchronization and "locking" into a dominant state may represent the basic neuronal mechanism that underlies movement disorders at the microcircuit level.

Scale-free topology of the CA3 hippocampal network: a novel method to analyze functional neuronal assemblies

Cognitive mapping functions of the hippocampus critically depend on the recurrent network of the CA3 pyramidal cells. However, it is still not known in detail how network activity patterns emerge, or how they encode information. By using functional multineuron calcium imaging, we simultaneously recorded the activity of >100 neurons in the CA3 region of hippocampal slice cultures. We utilized a novel computational method to analyze the multichannel spike trains and to depict functional neuronal assemblies. By means of event synchronization and the correlation matrix analysis method, we found that: 1), the average functional neuronal cluster consists of 23 neurons, and neurons could be part of multiple assemblies; 2), the clustering strength, size, and mean distance among cells in neuronal assemblies follow a power-law-like distribution; 3), the clustering strength and size of neuronal assemblies are not correlated with the total number of neurons and their physical distance; and 4), the clustering distance of neuronal assemblies is weakly correlated with the total number of neurons and their physical distance. These findings suggest that the functional organization of the spontaneously firing CA3 hippocampal network is a scale-free structure in slice culture.

Conditional probability-based significance tests for sequential patterns in multineuronal spike trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

Spatial-temporal dynamics of chaotic behavior in cultured hippocampal networks

Using multiple nonlinear techniques, we revealed the existence of chaos in the spontaneous activity of neuronal networks in vitro. The spatial-temporal dynamics of these networks indicated that emergent transition between chaotic behavior and superburst occurred periodically in low-frequency oscillations. An analysis of network-wide activity indicated that chaos was synchronized among different sites. Moreover, we found that the degree of chaos increased as the number of active sites in the network increased during long-term development (over three months in vitro). The chaotic behavior of the dissociated networks had similar spatial-temporal characteristics (rapid transition, periodicity, and synchronization) as the intact brain; however, the degree of chaos depended on the number of active sites at the mesoscopic level. This work could provide insight into neural coding and neurocybernetics.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Circuit topology for synchronizing neurons in spontaneously active networks

Spike synchronization underlies information processing and storage in the brain. But how can neurons synchronize in a noisy network? By exploiting a high-speed (500-2,000 fps) multineuron imaging technique and a large-scale synapse mapping method, we directly compared spontaneous activity patterns and anatomical connectivity in hippocampal CA3 networks ex vivo. As compared to unconnected pairs, synaptically coupled neurons shared more common presynaptic neurons, received more correlated excitatory synaptic inputs, and emitted synchronized spikes with approximately 10(7) times higher probability. Importantly, common presynaptic parents per se synchronized more than unshared upstream neurons. Consistent with this, dynamic-clamp stimulation revealed that common inputs alone could not account for the realistic degree of synchronization unless presynaptic spikes synchronized among common parents. On a macroscopic scale, network activity was coordinated by a power-law scaling of synchronization, which engaged varying sets of densely interwired (thus highly synchronized) neuron groups. Thus, locally coherent activity converges on specific cell assemblies, thereby yielding complex ensemble dynamics. These segmentally synchronized pulse packets may serve as information modules that flow in associatively parallel network channels.