Membrane Potential Oscillations Produced by Excitatory Amino Acids in Isolated Spinal Neurons of the Lamprey Lampetra fluviatilis

Membrane potential (MP) oscillations produced by excitatory amino acids (EAA) have been studied in branching neurons isolated by an enzymatic-mechanical method from the lamprey spinal cord. It was shown that (1) all studied EAA (glutamate, kainate, NMDA, aspartate, and quisqualate) evoke an ion current and a short-term reversible depolarization in studied cells; (2) EAA added to perfusion solution may produce MP oscillations, with kinetic parameters and duration of the oscillation depending on the amino acid used (the most effective are kainate and NMDA, the least effective, quisqualate); (3) oscillations can be irregular (of the type of a synaptic noise or of a long-term plateau of depolarization with action potentials—AP) or regular, with frequency of 0.5–1.5 Hz. Amplitude of both oscillation types depends on MP level, frequency is more steady for each cell and less depends on MP. In 68 out of 128 studied cells, oscillations could be evoked, which indicates that a significant part of lamprey spinal neurons have intrinsic capability for MP oscillations and probably pacemaker properties. The functional role of oscillations can be different. They can take cells out from the profound inhibition state, synchronize activity of rhythm generation neurons and/or be the base for trigger signals (AP firing) sent by locomotor neuronal circuits to trunk muscles.     

In Vitro Dose-Dependent Inhibition of the Intracellular Spontaneous Calcium Oscillations in Developing Hippocampal Neurons by Ketamine

Spatial and temporal abnormalities in the frequency and amplitude of the cytosolic calcium oscillations can impact the normal physiological functions of neuronal cells. Recent studies have shown that ketamine can affect the growth and development and even induce the apoptotic death of neurons. This study used isolated developing hippocampal neurons as its study subjects to observe the effect of ketamine on the intracellular calcium oscillations in developing hippocampal neurons and to further explore its underlying mechanism using Fluo-4-loaded laser scanning confocal microscopy. Using a semi-quantitative method to analyze the spontaneous calcium oscillatory activities, a typical type of calcium oscillation was observed in developing hippocampal neurons. In addition, the administration of NMDA (N-Methyl-D-aspartate) at a concentration of 100 µM increased the calcium oscillation amplitude. The administration of MK801 at a concentration of 40 µM inhibited the amplitude and frequency of the calcium oscillations. Our results demonstrated that an increase in the ketamine concentration, starting from 30 µM, gradually decreased the neuronal calcium oscillation amplitude. The inhibition of the calcium oscillation frequency by 300 µM ketamine was statistically significant, and the neuronal calcium oscillations were completely eliminated with the administration of 3,000 µM Ketamine. The administration of 100, 300, and 1,000 µM NMDA to the 1 mM ketamine-pretreated hippocampal neurons restored the frequency and amplitude of the calcium oscillations in a dose-dependent manner. In fact, a concentration of 1,000 µM NMDA completely reversed the decrease in the calcium oscillation frequency and amplitude that was induced by 1 mM ketamine. This study revealed that ketamine can inhibit the frequency and amplitude of the calcium oscillations in developing hippocampal neurons though the NMDAR (NMDA receptor) in a dose-dependent manner, which might highlight a possible underlying mechanism of ketamine toxicity on the rat hippocampal neurons during development.     

Pharmacologically induced calcium oscillations protect neurons from increases in cytosolic calcium after trauma

Increases in cytosolic calcium ([Ca(2+)](i)) following mechanical injury are often considered a major contributing factor to the cellular sequelae in traumatic brain injury (TBI). However, very little is known on how developmental changes may affect the calcium signaling in mechanically injured neurons. One key feature in the developing brain that may directly impact its sensitivity to stretch is the reduced inhibition which results in spontaneous [Ca(2+)](i) oscillations. In this study, we examined the mechanism of stretch-induced [Ca(2+)](i) transients in 18-days in vitro (DIV) neurons exhibiting bicuculline-induced [Ca(2+)](i) oscillations. We used an in vitro model of mechanical trauma to apply a defined uniaxial strain to cultured cortical neurons and used increases in [Ca(2+)](i) as a measure of the neuronal response to the stretch insult. We found that stretch-induced increases in [Ca(2+)](i) in 18-DIV neurons were inhibited by pretreatment with either the NMDA receptor antagonist, APV [D(-)-2-Amino-5-phosphonopentanoic acid], or by depolymerizing the actin cytoskeleton prior to stretch. Blocking synaptic NMDA receptors prior to stretch significantly attenuated most of the [Ca(2+)](i) transient. In comparison, cultures with pharmacologically induced [Ca(2+)](i) oscillations showed a substantially reduced [Ca(2+)](i) peak after stretch. We provide evidence showing that a contributing factor to this mechanical desensitization from induced [Ca(2+)](i) oscillations is the PKC-mediated uncoupling of NMDA receptors (NMDARs) from spectrin, an actin-associated protein, thereby rendering neurons insensitive to stretch. These results provide novel insights into how the [Ca(2+)](i) response to stretch is initiated, and how reduced inhibition - a feature of the developing brain - may affect the sensitivity of the immature brain to trauma.     

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.     

The generation of phase differences and frequency changes in a network model of inferior olive subthreshold oscillations

It is commonly accepted that the Inferior Olive (IO) provides a timing signal to the cerebellum. Stable subthreshold oscillations in the IO can facilitate accurate timing by phase-locking spikes to the peaks of the oscillation. Several theoretical models accounting for the synchronized subthreshold oscillations have been proposed, however, two experimental observations remain an enigma. The first is the observation of frequent alterations in the frequency of the oscillations. The second is the observation of constant phase differences between simultaneously recorded neurons. In order to account for these two observations we constructed a canonical network model based on anatomical and physiological data from the IO. The constructed network is characterized by clustering of neurons with similar conductance densities, and by electrical coupling between neurons. Neurons inside a cluster are densely connected with weak strengths, while neurons belonging to different clusters are sparsely connected with stronger connections. We found that this type of network can robustly display stable subthreshold oscillations. The overall frequency of the network changes with the strength of the inter-cluster connections, and phase differences occur between neurons of different clusters. Moreover, the phase differences provide a mechanistic explanation for the experimentally observed propagating waves of activity in the IO. We conclude that the architecture of the network of electrically coupled neurons in combination with modulation of the inter-cluster coupling strengths can account for the experimentally observed frequency changes and the phase differences.     

Computational Models Describing Possible Mechanisms for Generation of Excessive Beta Oscillations in Parkinson’s Disease

In Parkinson’s disease, an increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with difficulty in movement initiation. An important role in the generation of these oscillations is thought to be played by the motor cortex and by a network composed of the subthalamic nucleus (STN) and the external segment of globus pallidus (GPe). Several alternative models have been proposed to describe the mechanisms for generation of the Parkinsonian beta oscillations. However, a recent experimental study of Tachibana and colleagues yielded results which are challenging for all published computational models of beta generation. That study investigated how the presence of beta oscillations in a primate model of Parkinson’s disease is affected by blocking different connections of the STN-GPe circuit. Due to a large number of experimental conditions, the study provides strong constraints that any mechanistic model of beta generation should satisfy. In this paper we present two models consistent with the data of Tachibana et al. The first model assumes that Parkinsonian beta oscillation are generated in the cortex and the STN-GPe circuits resonates at this frequency. The second model additionally assumes that the feedback from STN-GPe circuit to cortex is important for maintaining the oscillations in the network. Predictions are made about experimental evidence that is required to differentiate between the two models, both of which are able to reproduce firing rates, oscillation frequency and effects of lesions carried out by Tachibana and colleagues. Furthermore, an analysis of the models reveals how the amplitude and frequency of the generated oscillations depend on parameters.     

Mechanism of melatonin-induced oscillations in the peroxidase-oxidase reaction

Melatonin induces oscillations in the peroxidase-oxidase (PO) reaction catalyzed by horseradish peroxidase. We present here studies of the effect of pH, enzyme concentration, and concentration of melatonin on the oscillation frequency. We also present a mechanistic model to explain the experimentally observed changes in oscillation frequency. Using the data obtained here we are able to predict that oscillations will also occur in the PO reaction catalyzed by myeloperoxidase. Myeloperoxidase is an important protein in activated neutrophils and we provide evidence that the oscillations of NAD(P)H, superoxide and hydrogen peroxide in these cells may involve this enzyme. Thus, our experimental system can be considered a model system for the nonrespiratory oxygen metabolism in activated neutrophils and other similar cells participating in the defence against invading pathogens.     

Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation Is State-Dependent

Cortical oscillations play a fundamental role in organizing large-scale functional brain networks. Noninvasive brain stimulation with temporally patterned waveforms such as repetitive transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) have been proposed to modulate these oscillations. Thus, these stimulation modalities represent promising new approaches for the treatment of psychiatric illnesses in which these oscillations are impaired. However, the mechanism by which periodic brain stimulation alters endogenous oscillation dynamics is debated and appears to depend on brain state. Here, we demonstrate with a static model and a neural oscillator model that recurrent excitation in the thalamo-cortical circuit, together with recruitment of cortico-cortical connections, can explain the enhancement of oscillations by brain stimulation as a function of brain state. We then performed concurrent invasive recording and stimulation of the human cortical surface to elucidate the response of cortical oscillations to periodic stimulation and support the findings from the computational models. We found that (1) stimulation enhanced the targeted oscillation power, (2) this enhancement outlasted stimulation, and (3) the effect of stimulation depended on behavioral state. Together, our results show successful target engagement of oscillations by periodic brain stimulation and highlight the role of nonlinear interaction between endogenous network oscillations and stimulation. These mechanistic insights will contribute to the design of adaptive, more targeted stimulation paradigms.     

Stochastic Simulation of Delay-Induced Circadian Rhythms in Drosophila

Circadian rhythms are ubiquitous in all eukaryotes and some prokaryotes. Several computational models with or without time delays have been developed for circadian rhythms. Exact stochastic simulations have been carried out for several models without time delays, but no exact stochastic simulation has been done for models with delays. In this paper, we proposed a detailed and a reduced stochastic model with delays for circadian rhythms in Drosophila based on two deterministic models of Smolen et al. and employed exact stochastic simulation to simulate circadian oscillations. Our simulations showed that both models can produce sustained oscillations and that the oscillation is robust to noise in the sense that there is very little variability in oscillation period although there are significant random fluctuations in oscillation peeks. Moreover, although average time delays are essential to simulation of oscillation, random changes in time delays within certain range around fixed average time delay cause little variability in the oscillation period. Our simulation results also showed that both models are robust to parameter variations and that oscillation can be entrained by light/dark circles. Our simulations further demonstrated that within a reasonable range around the experimental result, the rates that dclock and per promoters switch back and forth between activated and repressed sites have little impact on oscillation period.     

How Spike Synchronization Among Olfactory Neurons Can Contribute to Sensory Discrimination

Recent studies in honeybees have demonstrated that, when odor-evoked action potentials in antennal lobe neurons are pharmacologically desynchronized, the bees are impaired in their ability to discriminate chemically similar odor stimuli. Using a reduced computational model of the honeybee antennal lobe, we show how changes in spike-synchronization properties alone, independent of changes in overall spike-discharge rate or differences in activity levels among responsive neurons, can produce changes in associative learning similar to those observed experimentally.     

Computational aspects of feedback in neural circuits

It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning) to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks.     

Dependency analysis of frequency and strength of gamma oscillations on input difference between excitatory and inhibitory neurons

It has been found that gamma oscillations and the oscillation frequencies are regulated by the properties of external stimuli in many biology experimental researches. To unveil the underlying mechanism, firstly, we reproduced the experimental observations in an excitatory/inhibitory (E/I) neuronal network that the oscillation became stronger and moved to a higher frequency band (gamma band) with the increasing of the input difference between E/I neurons. Secondly, we found that gamma oscillation was induced by the unbalance between positive and negative synaptic currents, which was caused by the input difference between E/I neurons. When this input difference became greater, there would be a stronger gamma oscillation (i.e., a higher peak power in the power spectrum of the population activity of neurons). Further investigation revealed that the frequency dependency of gamma oscillation on the input difference between E/I neurons could be explained by the well-known mechanisms of inter-neuron-gamma (ING) and pyramidal-interneuron-gamma (PING). Finally, we derived mathematical analysis to verify the mechanism of frequency regulations and the results were consistent with the simulation results. The results of this paper provide a possible mechanism for the external stimuli-regulated gamma oscillations.     

A single spiking neuron that can represent interval timing: analysis,plasticity and multi-stability

The ability to represent interval timing is crucial for many common behaviors, such as knowing whether to stop when the light turns from green to yellow. Neural representations of interval timing have been reported in the rat primary visual cortex and we have previously presented a computational framework describing how they can be learned by a network of neurons. Recent experimental and theoretical results in entorhinal cortex have shown that single neurons can exhibit persistent activity, previously thought to be generated by a network of neurons. Motivated by these single neuron results, we propose a single spiking neuron model that can learn to compute and represent interval timing. We show that a simple model, reduced analytically to a single dynamical equation, captures the average behavior of the complete high dimensional spiking model very well. Variants of this model can be used to produce bi-stable or multi-stable persistent activity. We also propose a plasticity rule by which this model can learn to represent different intervals and different levels of persistent activity.     

When shared concept cells support associations: Theory of overlapping memory engrams

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction c_min of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction c_max of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds.     

Generation of locomotion rhythms without inhibition in vertebrates: the search for pacemaker neurons

Locomotion rhythms are thought to be generated by neurons in the central-pattern-generator (CPG) circuit in the spinal cord. Synaptic connections in the CPG and pacemaker properties in certain CPG neurons, both may contribute to generation of the rhythms. In the half-center model proposed by Graham Brown a century ago, reciprocal inhibition plays a critical role. However, in all vertebrate preparations examined, rhythmic motor bursts can be induced when inhibition is blocked in the spinal cord. Without inhibition, neuronal pacemaker properties may become more important in generation of the rhythms. Pacemaker properties have been found in motoneurons and some premotor interneurons in different vertebrates and they can be dependent on N-Methyl-d-aspartate (NMDA) receptors (NMDAR) or rely on other ionic currents like persistent inward currents. In the swimming circuit of the hatchling Xenopus tadpole, there is substantial evidence that emergent network properties can give rise to swimming rhythms. During fictive swimming, excitatory interneurons (dINs) in the caudal hindbrain fire earliest on each swimming cycle and their spikes drive the firing of other CPG neurons. Regenerative dIN firing itself relies on reciprocal inhibition and background excitation. We now find that the activation of NMDARs can change dINs from firing singly at rest to current injection to firing repetitively at swimming frequencies. When action potentials are blocked, some intrinsic membrane potential oscillations at about 10 Hz are revealed, which may underlie repetitive dIN firing during NMDAR activation. In confirmation of this, dIN repetitive firing persists in NMDA when synaptic transmission is blocked by Cd(2+). When inhibition is blocked, only dINs and motoneurons are functional in the spinal circuit. We propose that the conditional intrinsic NMDAR-dependent pacemaker firing of dINs can drive the production of swimming-like rhythms without the participation of inhibitory neurotransmission.     

Synaptic NMDA Receptor-Dependent Ca2+ Entry Drives Membrane Potential and Ca2+ Oscillations in Spinal Ventral Horn Neurons

During vertebrate locomotion, spinal neurons act as oscillators when initiated by glutamate release from descending systems. Activation of NMDA receptors initiates Ca²⁺-mediated intrinsic membrane potential oscillations in central pattern generator (CPG) neurons. NMDA receptor-dependent intrinsic oscillations require Ca²⁺-dependent K⁺ (K_Ca2) channels for burst termination. However, the location of Ca²⁺ entry mediating K_Ca2 channel activation, and type of Ca²⁺ channel – which includes NMDA receptors and voltage-gated Ca²⁺ channels (VGCCs) – remains elusive. NMDA receptor-dependent Ca²⁺ entry necessitates presynaptic release of glutamate, implying a location at active synapses within dendrites, whereas VGCC-dependent Ca²⁺ entry is not similarly constrained. Where Ca²⁺ enters relative to K_Ca2 channels is crucial to information processing of synaptic inputs necessary to coordinate locomotion. We demonstrate that Ca²⁺ permeating NMDA receptors is the dominant source of Ca²⁺ during NMDA-dependent oscillations in lamprey spinal neurons. This Ca²⁺ entry is synaptically located, NMDA receptor-dependent, and sufficient to activate K_Ca2 channels at excitatory interneuron synapses onto other CPG neurons. Selective blockade of VGCCs reduces whole-cell Ca²⁺ entry but leaves membrane potential and Ca²⁺ oscillations unaffected. Furthermore, repetitive oscillations are prevented by fast, but not slow, Ca²⁺ chelation. Taken together, these results demonstrate that K_Ca2 channels are closely located to NMDA receptor-dependent Ca²⁺ entry. The close spatial relationship between NMDA receptors and K_Ca2 channels provides an intrinsic mechanism whereby synaptic excitation both excites and subsequently inhibits ventral horn neurons of the spinal motor system. This places the components necessary for oscillation generation, and hence locomotion, at glutamatergic synapses.     

A model for Ca2+ oscillations stimulated by the type 5 metabotropic glutamate receptor: an unusual mechanism based on repetitive, reversible phosphorylation of the receptor

In parallel with experimental investigations, the molecular mechanisms responsible for Ca²⁺ oscillations have been much investigated with computational models. In the vast majority of cell-types, these oscillations rely on the biphasic regulation of the inositol 1,4,5-trisphosphate (InsP₃) receptor by cytosolic Ca²⁺. However, when Ca²⁺ oscillations are initiated by agonist stimulation of the type 5 metabotropic glutamate (mGlu5) receptor, oscillatory behaviour is tightly controlled by repetitive cycles of receptor phosphorylation/dephosphorylation leading to the periodic activation/deactivation of the G protein-activated signalling cascade downstream of this G protein-coupled receptor. We present a minimal model for mGlu5 receptor-induced Ca²⁺ oscillations, taking into account receptor phosphorylation by a protein kinase C isoenzyme sensitive to diacylglycerol but not to Ca²⁺. Depending on the density of receptors and the level of stimulation, the model reproduces Ca²⁺ oscillations based on either a ‘dynamic uncoupling’ mechanism or InsP₃ receptor dynamics. When based on the former mechanism, Ca²⁺ oscillation frequency is insensitive to the level of stimulation, while the level of receptor expression is a determinant of oscillation frequency. When investigating the conditions for the occurrence of oscillations, the model predicts that dynamic uncoupling likely relies on a steep relationship between the activity of PKC and the amount of phosphorylated mGlu5 receptor. Finally, we use the model to simulate the adaptation of the signalling pathway during periods of prolonged stimulation associated with receptor desensitization/internalization. The model suggests that the existence of both oscillatory mechanisms could allow for a significant lengthening of the repetitive Ca²⁺ responses under these conditions.     

Relationships among Parvalbumin-Immunoreactive Neuron Density,Phase-Locked Gamma Oscillations,and Autistic/Schizophrenic Symptoms in PDGFR-β Knock-Out and Control Mice

Cognitive deficits and negative symptoms are important therapeutic targets for schizophrenia and autism disorders. Although reduction of phase-locked gamma oscillation has been suggested to be a result of reduced parvalbumin-immunoreactive (putatively, GABAergic) neurons, no direct correlations between these have been established in these disorders. In the present study, we investigated such relationships during pharmacological treatment with a newly synthesized drug, T-817MA, which displays neuroprotective and neurotrophic effects. In this study, we used platelet-derived growth factor receptor-β gene knockout (PDGFR-β KO) mice as an animal model of schizophrenia and autism. These mutant mice display a reduction in social behaviors; deficits in prepulse inhibition (PPI); reduced levels of parvalbumin-immunoreactive neurons in the medical prefrontal cortex, hippocampus, amygdala, and superior colliculus; and a deficit in of auditory phase-locked gamma oscillations. We found that oral administration of T-817MA ameliorated all these symptoms in the PDGFR-β KO mice. Furthermore, phase-locked gamma oscillations were significantly correlated with the density of parvalbumin-immunoreactive neurons, which was, in turn, correlated with PPI and behavioral parameters. These findings suggest that recovery of parvalbumin-immunoreactive neurons by pharmacological intervention relieved the reduction of phase-locked gamma oscillations and, consequently, ameliorated PPI and social behavioral deficits. Thus, our findings suggest that phase-locked gamma oscillations could be a useful physiological biomarker for abnormality of parvalbumin-immunoreactive neurons that may induce cognitive deficits and negative symptoms of schizophrenia and autism, as well as of effective pharmacological interventions in both humans and experimental animals.     

A new regime for highly robust gamma oscillation with co-exist of accurate and weak synchronization in excitatory–inhibitory networks

A great number of biological experiments show that gamma oscillation occurs in many brain areas after the presentation of stimulus. The neural systems in these brain areas are highly heterogeneous. Specifically, the neurons and synapses in these neural systems are diversified; the external inputs and parameters of these neurons and synapses are heterogeneous. How the gamma oscillation generated in such highly heterogeneous networks remains a challenging problem. Aiming at this problem, a highly heterogeneous complex network model that takes account of many aspects of real neural circuits was constructed. The network model consists of excitatory neurons and fast spiking interneurons, has three types of synapses (GABA_A, AMPA, and NMDA), and has highly heterogeneous external drive currents. We found a new regime for robust gamma oscillation, i.e. the oscillation in inhibitory neurons is rather accurate but the oscillation in excitatory neurons is weak, in such highly heterogeneous neural networks. We also found that the mechanism of the oscillation is a mixture of interneuron gamma (ING) and pyramidal-interneuron gamma (PING). We explained the mixture ING and PING mechanism in a consistent-way by a compound post-synaptic current, which has a slowly rising-excitatory stage and a sharp decreasing-inhibitory stage.     

TTX-Resistant NMDA Receptor-Mediated Membrane Potential Oscillations in Neonatal Mouse Hb9 Interneurons

Conditional neuronal membrane potential oscillations have been identified as a potential mechanism to help support or generate rhythmogenesis in neural circuits. A genetically identified population of ventromedial interneurons, called Hb9, in the mouse spinal cord has been shown to generate TTX-resistant membrane potential oscillations in the presence of NMDA, serotonin and dopamine, but these oscillatory properties are not well characterized. Hb9 interneurons are rhythmically active during fictive locomotor-like behavior. In this study, we report that exogenous N-Methyl-D-Aspartic acid (NMDA) application is sufficient to produce membrane potential oscillations in Hb9 interneurons. In contrast, exogenous serotonin and dopamine application, alone or in combination, are not sufficient. The properties of NMDA-induced oscillations vary among the Hb9 interneuron population; their frequency and amplitude increase with increasing NMDA concentration. NMDA does not modulate the T-type calcium current (I_Ca(T)), which is thought to be important in generating locomotor-like activity, in Hb9 neurons. These results suggest that NMDA receptor activation is sufficient for the generation of TTX-resistant NMDA-induced membrane potential oscillations in Hb9 interneurons.