Persistent Synchronized Bursting Activity in Cortical Tissues With Low Magnesium Concentration: A Modeling Study

Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals

The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a Pavlovian-conditioned spiking neural network model that may help elucidate these mechanisms. Using "three-factor learning rule," we conditioned an initially random spiking neural network to discriminate a specific spatiotemporal stimulus - a sequence of two or three pulses delivered within ∼ 10 ms to two or three distinct neuronal subpopulations - from other pulse sequences differing by only a few milliseconds. Through conditioning, a feedforward structure emerges that encodes the target pattern's temporal information into specific topographic arrangements of stimulated subpopulations. In the readout phase, discrimination of different inputs is achieved by evaluating the shape and peak-shift characteristics of the spike density functions (SDFs) of input-triggered population bursts. The network's dynamic range - defined by the duration over which pulse sequences are processed accurately - is limited to around 10 ms, as determined by the duration of the input-triggered population burst. However, by introducing axonal conduction delays, we show that the network can generate "superbursts," producing a more complex and extended SDF lasting up to ∼ 30 ms, and potentially much longer. This extension effectively broadens the network's dynamic range for processing temporal sequences. We propose that such conditioning mechanisms may provide insight into the brain's ability to perceive and interpret complex spatiotemporal sensory information encountered in real-world contexts.

The spinal premotor network driving scratching flexor and extensor alternation

Rhythmic motor behaviors are generated by neural networks termed central pattern generators (CPGs). Although locomotor CPGs have been extensively characterized, it remains unknown how the neuronal populations composing them interact to generate adaptive rhythms. We explored the non-linear cooperation dynamics among the three main populations of ipsilaterally projecting spinal CPG neurons - V1, V2a, V2b neurons - in scratch reflex rhythmogenesis. Ablation of all three neuronal subtypes reduced the oscillation frequency. Activation of excitatory V2a neurons enhanced the oscillation frequency, while activating inhibitory V1 neurons caused atonia. These findings required the development of a novel neuromechanical model that consists of flexor and extensor modules coupled via inhibition, in which rhythm in each module is generated by self-bursting excitatory populations and accelerated by intra-module inhibition. Inter-module inhibition coordinates the phases of flexor and extensor activity and slows the oscillations, while facilitation mechanisms in excitatory neurons explain the V2a activation-driven increase in frequency.

Astrocytic Gap Junctions Contribute to Aberrant Neuronal Synchronization in a Mouse Model of MeCP2 Duplication Syndrome

Abnormal synchronous neuronal activity has been widely detected by brain imaging of autistic patients, but its underlying neural mechanism remains unclear. Compared with wild-type mice, our in vivo two-photon imaging showed that transgenic (Tg1) mice over-expressing human autism risk gene MeCP2 exhibited higher neuronal synchrony in the young but lower synchrony in the adult stage. Whole-cell recording of neuronal pairs in brain slices revealed that higher neuronal synchrony in young postnatal Tg1 mice was attributed mainly to more prevalent giant slow inward currents (SICs). Both in vivo and slice imaging further demonstrated more dynamic activity and higher synchrony in astrocytes from young Tg1 mice. Blocking astrocytic gap junctions markedly decreased the generation of SICs and overall cell synchrony in the Tg1 brain. Furthermore, the expression level of Cx43 protein and the coupling efficiency of astrocyte gap junctions remained unchanged in Tg1 mice. Thus, astrocytic gap junctions facilitate but do not act as a direct trigger for the abnormal neuronal synchrony in young Tg1 mice, revealing the potential role of the astrocyte network in the pathogenesis of MeCP2 duplication syndrome.

Interstitial ions: A key regulator of state-dependent neural activity?

Throughout the nervous system, ion gradients drive fundamental processes. Yet, the roles of interstitial ions in brain functioning is largely forgotten. Emerging literature is now revitalizing this area of neuroscience by showing that interstitial cations (K+, Ca2+ and Mg2+) are not static quantities but change dynamically across states such as sleep and locomotion. In turn, these state-dependent changes are capable of sculpting neuronal activity; for example, changing the local interstitial ion composition in the cortex is sufficient for modulating the prevalence of slow-frequency neuronal oscillations, or potentiating the gain of visually evoked responses. Disturbances in interstitial ionic homeostasis may also play a central role in the pathogenesis of central nervous system diseases. For example, impairments in K+ buffering occur in a number of neurodegenerative diseases, and abnormalities in neuronal activity in disease models disappear when interstitial K+ is normalized. Here we provide an overview of the roles of interstitial ions in physiology and pathology. We propose the brain uses interstitial ion signaling as a global mechanism to coordinate its complex activity patterns, and ion homeostasis failure contributes to central nervous system diseases affecting cognitive functions and behavior.

Induction of activity synchronization among primed hippocampal neurons out of random dynamics is key for trace memory formation and retrieval

Memory is thought to be encoded by sparsely distributed neuronal ensembles in memory‐related regions. However, it is unclear how memory‐eligible neurons react during learning to encode trace fear memory and how they retrieve a memory. We implemented a fiber‐optic confocal fluorescence endomicroscope to directly visualize calcium dynamics of hippocampal CA1 neurons in freely behaving mice subjected to trace fear conditioning. Here we report that the overall activity levels of CA1 neurons showed a right‐skewed lognormal distribution, with a small portion of highly active neurons (termed Primed Neurons) filling the long‐tail. Repetitive training induced Primed Neurons to shift from random activity to well‐tuned synchronization. The emergence of activity synchronization coincided with the appearance of mouse freezing behaviors. In recall, a partial synchronization among the same subset of Primed Neurons was induced from random dynamics, which also coincided with mouse freezing behaviors. Additionally, training‐induced synchronization facilitated robust calcium entry into Primed Neurons. In contrast, most CA1 neurons did not respond to tone and foot shock throughout the training and recall cycles. In conclusion, Primed Neurons are preferably recruited to encode trace fear memory and induction of activity synchronization among Primed Neurons out of random dynamics is critical for trace memory formation and retrieval.

Cell adhesion molecules and sleep

Cell adhesion molecules (CAMs) play essential roles in the central nervous system, where some families are involved in synaptic development and function. These synaptic adhesion molecules (SAMs) are involved in the regulation of synaptic plasticity, and the formation of neuronal networks. Recent findings from studies examining the consequences of sleep loss suggest that these molecules are candidates to act in sleep regulation. This review highlights the experimental data that lead to the identification of SAMs as potential sleep regulators, and discusses results supporting that specific SAMs are involved in different aspects of sleep regulation. Further, some potential mechanisms by which SAMs may act to regulate sleep are outlined, and the proposition that these molecules may serve as molecular machinery in the two sleep regulatory processes, the circadian and homeostatic components, is presented. Together, the data argue that SAMs regulate the neuronal plasticity that underlies sleep and wakefulness.

Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits.

Synchronization of action potentials during low-magnesium-induced bursting

The relationship between mono- and polysynaptic strength and action potential synchronization was explored using a reduced external Mg(2+) model. Single and dual whole cell patch-clamp recordings were performed in hippocampal cultures in three concentrations of external Mg(2+). In decreased Mg(2+) medium, the individual cells transitioned to spontaneous bursting behavior. In lowered Mg(2+) media the larger excitatory synaptic events were observed more frequently and fewer transmission failures occurred, suggesting strengthened synaptic transmission. The event synchronization was calculated for the neural action potentials of the cell pairs, and it increased in media where Mg(2+) concentration was lowered. Analysis of surrogate data where bursting was present, but no direct or indirect connections existed between the neurons, showed minimal action potential synchronization. This suggests the synchronization of action potentials is a product of the strengthening synaptic connections within neuronal networks.

Contribution of transcriptional and translational mechanisms to the recovery aspect of sleep regulation

Sleep parallels brain functioning and mental health. Neuronal activity during wakefulness leads to a subsequent increase in sleep intensity as measured using electroencephalographic slow-wave activity (SWA; index of neuronal synchrony in the low-frequency range). Wakefulness, and particularly prolonged wakefulness, also drives important changes in brain gene expression and changes in protein regulation. The role of these two cellular mechanisms in sleep-wake regulation has typically been studied independently, and their exact contribution to SWA remains poorly defined. In this review, we highlight that many transcriptional pathways driven by sleep deprivation are associated to protein regulation. We first describe the relationship between cytokines, clock genes, and markers of sleep need with an emphasis on transcriptional processes. Observations regarding the role of protein metabolism in sleep-wake regulation are then depicted while presenting interconnections between transcriptional and translational responses driven by sleep loss. Lastly, a manner by which this integrated response can feed back on neuronal network activity to determine sleep intensity is proposed. Overall, the literature supports that a complex cross-talk between transcriptional and translational regulation during prolonged wakefulness drives the changes in sleep intensity as a function of the sleep/wake history.

Autaptic connections and synaptic depression constrain and promote gamma oscillations

Computational models of gamma oscillations have helped increase our understanding of the mechanisms that shape these 40–80 Hz cortical rhythms. Evidence suggests that interneurons known as basket cells are responsible for the generation of gamma oscillations. However, current models of gamma oscillations lack the dynamic short term synaptic plasticity seen at basket cell-basket cell synapses as well as the large autaptic synapses basket cells are known to express. Hence, I sought to extend the Wang-Buzsáki model of gamma oscillations to include these features. I found that autapses increased the synchrony of basket cell membrane potentials across the network during neocortical gamma oscillations as well as allowed the network to oscillate over a broader range of depolarizing drive. I also found that including realistic synaptic depression filtered the output of the network. Depression restricted the network to oscillate in the 60–80 Hz range rather than the 40–120 Hz range seen in the standard model. This work shows the importance of including accurate synapses in any future model of gamma oscillations.

Estimating the modulatory effects of nanoparticles on neuronal circuits using computational upscaling

BACKGROUND

Beside the promising application potential of nanotechnologies in engineering, the use of nanomaterials in medicine is growing. New therapies employing innovative nanocarrier systems to increase specificity and efficacy of drug delivery schemes are already in clinical trials. However the influence of the nanoparticles themselves is still unknown in medical applications, especially for complex interactions in neural systems. The aim of this study was to investigate in vitro effects of coated silver nanoparticles (cAgNP) on the excitability of single neuronal cells and to integrate those findings into an in silico model to predict possible effects on neuronal circuits.

METHODS

We first performed patch clamp measurements to investigate the effects of nanosized silver particles, surrounded by an organic coating, on excitability of single cells. We then determined which parameters were altered by exposure to those nanoparticles using the Hodgkin-Huxley model of the sodium current. As a third step, we integrated those findings into a well-defined neuronal circuit of thalamocortical interactions to predict possible changes in network signaling due to the applied cAgNP, in silico.

RESULTS

We observed rapid suppression of sodium currents after exposure to cAgNP in our in vitro recordings. In numerical simulations of sodium currents we identified the parameters likely affected by cAgNP. We then examined the effects of such changes on the activity of networks. In silico network modeling indicated effects of local cAgNP application on firing patterns in all neurons in the circuit.

CONCLUSION

Our sodium current simulation shows that suppression of sodium currents by cAgNP results primarily by a reduction in the amplitude of the current. The network simulation shows that locally cAgNP-induced changes result in changes in network activity in the entire network, indicating that local application of cAgNP may influence the activity throughout the network.

Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework

The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.

Neuroligin-1 links neuronal activity to sleep-wake regulation

Maintaining wakefulness is associated with a progressive increase in the need for sleep. This phenomenon has been linked to changes in synaptic function. The synaptic adhesion molecule Neuroligin-1 (NLG1) controls the activity and synaptic localization of N-methyl-d-aspartate receptors, which activity is impaired by prolonged wakefulness. We here highlight that this pathway may underlie both the adverse effects of sleep loss on cognition and the subsequent changes in cortical synchrony. We found that the expression of specific Nlg1 transcript variants is changed by sleep deprivation in three mouse strains. These observations were associated with strain-specific changes in synaptic NLG1 protein content. Importantly, we showed that Nlg1 knockout mice are not able to sustain wakefulness and spend more time in nonrapid eye movement sleep than wild-type mice. These changes occurred with modifications in waking quality as exemplified by low theta/alpha activity during wakefulness and poor preference for social novelty, as well as altered delta synchrony during sleep. Finally, we identified a transcriptional pathway that could underlie the sleep/wake-dependent changes in Nlg1 expression and that involves clock transcription factors. We thus suggest that NLG1 is an element that contributes to the coupling of neuronal activity to sleep/wake regulation.

The motor cortex: a network tuned to 7-14 Hz

The neocortex or six layer cortex consists of at least 52 cytoarchitectonically distinct areas in humans, and similar areas can be distinguished in rodents. Each of these areas has a defining set of extrinsic connections, identifiable functional roles, a distinct laminar arrangement, etc. Thus, neocortex is extensively subdivided into areas of anatomical and functional specialization, but less is known about the specialization of cellular and network physiology across areas. The motor cortex appears to have a distinct propensity to oscillate in the 7–14 Hz frequency range. Augmenting responses, normal mu and beta oscillations, and abnormal oscillations or after discharges caused by enhancing excitation or suppressing inhibition are all expressed around this frequency range. The substrate for this activity may be an excitatory network that is unique to the motor cortex or that is more strongly suppressed in other areas, such as somatosensory cortex. Interestingly, augmenting responses are dependent on behavioral state. They are abolished during behavioral arousal. Here, I briefly review this evidence.

GABA(B) receptor-mediated presynaptic inhibition reverses inter-columnar covariability of synaptic actions by intracortical axons in the rat barrel cortex

Intracortical axons originating from pyramidal cells in layer 3 of the rat somatosensory cortex are shared between adjacent columns, and receive the presynaptic inhibition that is mediated by the GABA(B) receptor. Synaptic actions by intracortical axons of single layer 3 pyramidal cells covary between the two adjacent columns in response to stimulation of layer 3 of either column. We examined whether GABA(B) receptor-mediated presynaptic inhibition affects the covariability of synaptic actions by intracortical axons between adjacent columns in slice preparations of the rat barrel cortex. Paired stimulations of superficial layer 3 evoked first and second excitatory postsynaptic currents (EPSCs) of varying amplitudes, yielding varying paired-pulse depression of EPSCs in layer 3 pyramidal cells that were located in the stimulated column, but not in its adjacent column. The amplitude of the second EPSC was inversely proportional to that of the first EPSC in layer 3 pyramidal cells in the stimulated column, yielding a negative correlation coefficient between the first and second EPSCs. Baclofen and CGP55845 attenuated paired-pulse depression and abolished the inverse relationship. Simultaneous recordings from two layer 3 pyramidal cells in the stimulated and adjacent columns revealed a positive correlation between the paired first EPSC amplitudes and a negative correlation between the paired second EPSC amplitudes, which, respectively, indicate the positive and negative covariability of synaptic actions by intracortical axons between the two adjacent columns. These results suggest that GABA(B) receptor-mediated presynaptic inhibition can reverse the positive covariability of inter-columnar synaptic actions, which may serve as a basis for inter-columnar desynchronisation.

Connexin36 gap junction blockade is ineffective at reducing seizure-like event activity in neocortical mouse slices

Despite much research, there remains controversy over the role of gap junctions in seizure processes. Many studies report anticonvulsant effects of gap junction blockade, but contradictory results have also been reported. The aim of this study was to clarify the role of connexin36 (Cx36) gap junctions in neocortical seizures. We used the mouse neocortical slice preparation to investigate the effect of pharmacological (mefloquine) and genetic (Cx36 knockout mice (Cx36KO)) manipulation of Cx36 gap junctions on two seizure models: low-magnesium artificial cerebrospinal fluid (ACSF) and aconitine perfusion in low-magnesium ACSF. Low-magnesium- (nominally zero) and aconitine- (230 nM) induced seizure-like event (SLE) population activity was recorded extracellularly. The results were consistent in showing that neither mefloquine (25 μM) nor genetic knockdown of Cx36 expression had anticonvulsant effects on SLE activity generated by either method. These findings call into question the widely held idea that open Cx36 gap junctions promote seizure activity.

Control of the firing patterns of vibrissa motoneurons by modulatory and phasic synaptic inputs: a modeling study

Vibrissa motoneurons (vMNs) generate rhythmic firing that controls whisker movements, even without cortical, cerebellar, or sensory inputs. vMNs receive serotonergic modulation from brain stem areas, which mainly increases their persistent sodium conductance (g(NaP)) and, possibly, phasic input from a putative central pattern generator (CPG). In response to serotonergic modulation or just-suprathreshold current steps, vMNs fire at low rates, below the firing frequency of exploratory whisking. In response to periodic inputs, vMNs exhibit nonlinear suprathreshold resonance in frequency ranges of exploratory whisking. To determine how firing patterns of vMNs are determined by their 1) intrinsic ionic conductances and 2) responses to periodic input from a putative CPG and to serotonergic modulation, we construct and analyze a single-compartment, conductance-based model of vMNs. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with g(NaP) and increases with M-potassium and h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced g(NaP) elevation may transfer the system from quiescence to a firing state that is highly locked to the CPG input. Thus we conclude that for vMNs, the CPG controls firing frequency and phase and enables bursting, whereas serotonergic modulation controls transitions from quiescence to firing unless the CPG input is sufficiently strong.

Mechanism for the universal pattern of activity in developing neuronal networks

Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. Here we use a modeling approach to explain this characteristic, but thus far unexplained, feature of developing networks. Because the correlation pattern is observed in networks with different structures and components, a satisfactory model needs to generate the right pattern of activity regardless of the details of network architecture or individual cell properties. We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. In this model, episode onset is highly sensitive to inputs. Thus noise resulting from random coincidences in the spike times of individual neurons led to the high variability at episode onset and to the observed correlation pattern. This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development.

Implications of synaptic biophysics for recurrent network dynamics and active memory

In cortical networks, synaptic excitation is mediated by AMPA- and NMDA-type receptors. NMDA differ from AMPA synaptic potentials with regard to peak current, time course, and a strong voltage-dependent nonlinearity. Here we illustrate based on empirical and computational findings that these specific biophysical properties may have profound implications for the dynamics of cortical networks, and via dynamics on cognitive functions like active memory. The discussion will be led along a minimal set of neural equations introduced to capture the essential dynamics of the various phenomena described. NMDA currents could establish cortical bistability and may provide the relatively constant synaptic drive needed to robustly maintain enhanced levels of activity during working memory epochs, freeing fast AMPA currents for other computational purposes. Perhaps more importantly, variations in NMDA synaptic input-due to their biophysical particularities-control the dynamical regime within which single neurons and networks reside. By provoking bursting, chaotic irregularity, and coherent oscillations their major effect may be on the temporal pattern of spiking activity, rather than on average firing rate. During active memory, neurons may thus be pushed into a spiking regime that harbors complex temporal structure, potentially optimal for the encoding and processing of temporal sequence information. These observations provide a qualitatively different view on the role of synaptic excitation in neocortical dynamics than entailed by many more abstract models. In this sense, this article is a plead for taking the specific biophysics of real neurons and synapses seriously when trying to account for the neurobiology of cognition.

Neural mechanisms of interstimulus interval-dependent responses in the primary auditory cortex of awake cats

Background

Primary auditory cortex (AI) neurons show qualitatively distinct response features to successive acoustic signals depending on the inter-stimulus intervals (ISI). Such ISI-dependent AI responses are believed to underlie, at least partially, categorical perception of click trains (elemental vs. fused quality) and stop consonant-vowel syllables (eg.,/da/-/ta/continuum).

Methods

Single unit recordings were conducted on 116 AI neurons in awake cats. Rectangular clicks were presented either alone (single click paradigm) or in a train fashion with variable ISI (2–480 ms) (click-train paradigm). Response features of AI neurons were quantified as a function of ISI: one measure was related to the degree of stimulus locking (temporal modulation transfer function [tMTF]) and another measure was based on firing rate (rate modulation transfer function [rMTF]). An additional modeling study was performed to gain insight into neurophysiological bases of the observed responses.

Results

In the click-train paradigm, the majority of the AI neurons ("synchronization type"; n = 72) showed stimulus-locking responses at long ISIs. The shorter cutoff ISI for stimulus-locking responses was on average ~30 ms and was level tolerant in accordance with the perceptual boundary of click trains and of consonant-vowel syllables. The shape of tMTF of those neurons was either band-pass or low-pass. The single click paradigm revealed, at maximum, four response periods in the following order: 1st excitation, 1st suppression, 2nd excitation then 2nd suppression. The 1st excitation and 1st suppression was found exclusively in the synchronization type, implying that the temporal interplay between excitation and suppression underlies stimulus-locking responses. Among these neurons, those showing the 2nd suppression had band-pass tMTF whereas those with low-pass tMTF never showed the 2nd suppression, implying that tMTF shape is mediated through the 2nd suppression. The recovery time course of excitability suggested the involvement of short-term plasticity. The observed phenomena were well captured by a single cell model which incorporated AMPA, GABA_A, NMDA and GABA_B receptors as well as short-term plasticity of thalamocortical synaptic connections.

Conclusion

Overall, it was suggested that ISI-dependent responses of the majority of AI neurons are configured through the temporal interplay of excitation and suppression (inhibition) along with short-term plasticity.

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

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

The synergistic inhibitory actions of oxcarbazepine on voltage-gated sodium and potassium currents in differentiated NG108-15 neuronal cells and model neurons

Oxcarbazepine (OXC), one of the newer anti-epileptic drugs, has been demonstrating its efficacy on wide-spectrum neuropsychiatric disorders. However, the ionic mechanism of OXC actions in neurons remains incompletely understood. With the aid of patch-clamp technology, we first investigated the effects of OXC on ion currents in NG108-15 neuronal cells differentiated with cyclic AMP. We found OXC (0.3-30 microm) caused a reversible reduction in the amplitude of voltage-gated Na+ current (INa). The IC50 value required for the inhibition of INa by OXC was 3.1 microm. OXC (3 microm) could shift the steady-state inactivation of INa to a more negative membrane potential by approximately -9 mV with no effect on the slope of the inactivation curve, and produce a significant prolongation in the recovery of INa inactivation. Additionally, OXC was effective in suppressing persistent INa (INa(P)) elicited by long ramp pulses. The blockade of INa by OXC does not simply reduce current magnitude, but alters current kinetics. Moreover, OXC could suppress the amplitude of delayed rectifier K+ current (IK(DR)), with no effect on M-type K+ current (IK(M)). In current-clamp configuration, OXC could reduce the amplitude of action potentials and prolong action-potential duration. Furthermore, the simulations, based on hippocampal pyramidal neurons (Pinsky-Rinzel model) and a network of the Hodgkin-Huxley model, were analysed to investigate the effect of OXC on action potentials. Taken together, our results suggest that the synergistic blocking effects on INa and IK(DR) may contribute to the underlying mechanisms through which OXC affects neuronal function in vivo.

Calcium and synaptic dynamics underlying reverberatory activity in neuronal networks

Persistent activity is postulated to drive neural network plasticity and learning. To investigate its underlying cellular mechanisms, we developed a biophysically tractable model that explains the emergence, sustenance and eventual termination of short-term persistent activity. Using the model, we reproduced the features of reverberating activity that were observed in small (50-100 cells) networks of cultured hippocampal neurons, such as the appearance of polysynaptic current clusters, the typical inter-cluster intervals, the typical duration of reverberation, and the response to changes in extra-cellular ionic composition. The model relies on action potential-triggered residual pre-synaptic calcium, which we suggest plays an important role in sustaining reverberations. We show that reverberatory activity is maintained by enhanced asynchronous transmitter release from pre-synaptic terminals, which in itself depends on the dynamics of residual pre-synaptic calcium. Hence, asynchronous release, rather than being a 'synaptic noise', can play an important role in network dynamics. Additionally, we found that a fast timescale synaptic depression is responsible for oscillatory network activation during reverberations, whereas the onset of a slow timescale depression leads to the termination of reverberation. The simplicity of our model enabled a number of predictions that were confirmed by additional analyses of experimental manipulations.

Characterization of synchronized bursts in cultured hippocampal neuronal networks with learning training on microelectrode arrays

Spontaneous synchronized bursts seem to play a key role in brain functions such as learning and memory. Still controversial is the characterization of spontaneous synchronized bursts in neuronal networks after learning training, whether depression or promotion. By taking advantages of the main features of the microelectrode array (MEA) technology (i.e. multisite recordings, stable and long-term coupling with the biological preparation), we analyzed changes of spontaneous synchronized bursts in cultured hippocampal neuronal networks after learning training. And for this purpose, a learning model at networking level on MEA system was constructed, and analysis of spontaneous synchronized burst activity modulation was presented. Preliminary results show that, the number of burst was increased by 154%, burst duration was increased by 35%, and the number of spikes per burst was increased by 124%, while interburst interval decreased by 44% with learning. In particular, correlation and synchrony of neuronal activities in networks were enhanced by 51% and 36%, respectively, with learning. In contrast, dynamic properties of neuronal networks were not changed much when the network was under "non-learning" condition. These results indicate that firing, association and synchrony of spontaneous bursts in neuronal networks were promoted by learning. Furthermore, from these observations, we are encouraged to think of a more engineered system based on in vitro hippocampal neurons, as a novel sensitive system for electrophysiological evaluations.

Area-specific resonance of excitatory networks in neocortex: control by outward currents

During disinhibition or low [Mg++](o) buffer, 7-14 Hz ( approximately 10 Hz) oscillations are generated by excitatory networks of interconnected pyramidal cells in motor (agranular) cortex but are absent in barrel (granular) cortex. Here we studied if the inability of barrel cortex to produce approximately 10 Hz oscillations during these conditions is because barrel cortex networks lack the necessary cellular mechanisms or, alternatively, because those mechanisms are inhibited by outward currents. The results show that blockers of slowly inactivating voltage-dependent K+ currents unmask approximately 10 Hz oscillations in barrel cortex, and this occurs in unison with the unmasking of intrinsic inward Ca++ currents that are kept suppressed by the outward currents. Moreover, the approximately 10 Hz oscillations unmasked in barrel cortex occur independently in upper and lower layers indicating that the approximately 10 Hz oscillation mechanisms are kept suppressed in multiple networks. The results reveal that the propensity of distinct excitatory networks of neocortex to generate epileptiform oscillatory activities is controlled by outward currents.

Gap junctions destroy persistent states in excitatory networks

Gap junctions between excitatory neurons are shown to disrupt the persistent state. The asynchronous state of the network loses stability via a Hopf bifurcation and then the active state is destroyed via a homoclinic bifurcation with a stationary state. A partial differential equation (PDE) is developed to analyze the Hopf and the homoclinic bifurcations. The simplified dynamics are compared to a biophysical model where similar behavior is observed. In the low noise case, the dynamics of the PDE is shown to be very complicated and includes possible chaotic behavior. The onset of synchrony is studied by the application of averaging to obtain a simple criterion for destabilization of the asynchronous persistent state.

Resonance (approximately 10 Hz) of excitatory networks in motor cortex: effects of voltage-dependent ion channel blockers

The motor cortex generates synchronous network oscillations at frequencies between 7 and 14 Hz during disinhibition or low [Mg2+]o buffers, but the underlying mechanisms are poorly understood. These oscillations, termed here approximately 10 Hz oscillations, are generated by a purely excitatory network of interconnected pyramidal cells because they are robust in the absence of GABAergic transmission. It is likely that specific voltage-dependent currents expressed in those cells contribute to the generation of approximately 10 Hz oscillations. We tested the effects of different drugs known to suppress certain voltage-dependent currents. The results revealed that drugs that suppress the low-threshold calcium current and the hyperpolarization-activated cation current are not critically involved in the generation of approximately 10 Hz oscillations. Interestingly, drugs known to suppress the persistent sodium current abolished approximately 10 Hz oscillations. Furthermore, blockers of K+ channels had significant effects on the oscillations. In particular, blockers of the M-current abolished the oscillations. Also, blockers of both non-inactivating and slowly inactivating voltage-dependent K+ currents abolished approximately 10 Hz oscillations. The results indicate that specific voltage-dependent non-inactivating K+ currents, such as the M-current, and persistent sodium currents are critically involved in generating approximately 10 Hz oscillations of excitatory motor cortex networks.

Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons

Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. Here we report that highly irregular firing can be induced through a combination of NMDA- and dopamine D1 receptor agonists applied to adult PFC neurons in vitro. The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, while neurons with clearly mono- or bimodal distributions fired much more regularly. Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. The nonlinearity of NMDA conductances was crucial for inducing this regime. NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.