Noise enhances subthreshold oscillations in injured primary sensory neurons

Noise can play a constructive role in the detection of weak signals in various kinds of peripheral receptors and neurons. What the mechanism underlying the effect of noise is remains unclear. Here, the perforated patch-clamp technique was used on isolated cells from chronic compression of the dorsal root ganglion (DRG) model. Our data provided new insight indicating that, under conditions without external signals, noise can enhance subthreshold oscillations, which was observed in a certain type of neurons with high-frequency (20-100 Hz) intrinsic resonance from injured DRG neurons. The occurrence of subthreshold oscillation considerably decreased the threshold potential for generating repetitive firing. The above effects of noise can be abolished by blocking the persistent sodium current (I(Na, P)). Utilizing a mathematical neuron model we further simulated the effect of noise on subthreshold oscillation and firing, and also found that noise can enhance the electrical activity through autonomous stochastic resonance. Accordingly, we propose a new concept of the effects of noise on neural intrinsic activity, which suggests that noise may be an important factor for modulating the excitability of neurons and generation of chronic pain signals.     

Resonance and selective communication via bursts in neurons having subthreshold oscillations

Revealing the role of bursts of action potentials is an important step toward understanding how the neurons communicate. The dominant point of view is that bursts are needed to increase the reliability of communication between neurons [Trends Neurosci. 20 (1997) 38]. In this paper we present an alternative but complementary hypothesis. We consider the effect of a short burst on a model postsynaptic cell having damped oscillation of its membrane potential. The oscillation frequency (eigenfrequency) plays a crucial role. Due to the subthreshold membrane resonance and frequency preference, the responses (i.e. voltage oscillations) of such a cell are amplified when the intra-burst frequency equals the cell's eigenfrequency. Responses are negligible, however, if the intra-burst frequency is twice the eigenfrequency. Thus, the same burst could be effective for one cell and ineffective for another depending on their eigenfrequencies. This theoretical observation suggests that, in addition to coping with unreliable synapses, bursts of action potentials may provide effective mechanisms for selective communication between neurons.     

From neural oscillations to variational problems in the visual cortex.

Aim of this study is to provide a formal link between connectionist neural models and variational psycophysical ones. We show that the solution of phase difference equation of weakly connected neural oscillators gamma-converges as the dimension of the grid tends to 0, to the gradient flow relative to the Mumford-Shah functional in a Riemannian space. The Riemannian metric is directly induced by the pattern of neural connections. Next, we embed the energy functional in the specific geometry of the functional space of the primary visual cortex, that is described in terms of a subRiemannian Heisenberg space. Namely, we introduce the Mumford-Shah functional with the Heisenberg metric and discuss the applicability of our main gamma-convergence result to subRiemannian spaces.     

Effect of temporal synergism of neural oscillations on photorefractoriness in Japanese quail (Coturnix coturnix japonica)

Circadian rhythms in many metabolic functions including neural (transmitters) and hormonal secretion appear to change with physiological condition. It is also reported that seasonal changes in photoperiodism/reproduction and other metabolic conditions may result from a temporal interaction of circadian neural oscillations that change seasonally. To test this hypothesis, the present study was designed to study the effect of temporal synergism of two neural oscillations (serotonin and dopamine) on relative photorefractoriness of Japanese quail.Serotonin and dopamine precursor drugs (5-HTP, 5-hydroxytryptophan and L-DOPA, L-dihydroxyphenylalanine) were administered (intraperitonially 5 mg/100 g body weight) at six different time intervals of 0, 4, 8, 12, 16 and 20 hr in sexually mature quail (>12 weeks old). The birds of control group received two daily injections of normal saline. The treatment was given for 13 days in continuous condition of light and then the quail were shifted to intermediate daylength (LD 13.5:10.5 for experiment 1) and short daylength (LD 8:16 for experiment 2). Six weeks following treatment, birds in intermediate daylength showed regressed cloacal gland and testicular activity except in 12-hr group, which exhibited gonadostimulatory condition. But birds of all the groups in short daylength showed complete regression of cloacal gland after 4 weeks of the treatment. In experiment 3, reproductively quiescent relative photorefractory quail maintained under intermediate daylength (LD 13.5:10.5) received 13 daily injections of 5-HTP and L-DOPA at the interval of 12 hr. At 6 weeks post-treatment, it was observed that unlike cloacal gland of control quail, which remained regressed, that of 12-hr quail showed significant development.These findings indicate that 12-hr temporal interaction of 5-HTP and L-DOPA administration maintained reproductive system in stimulated condition and prevented reproductive regression in photorefractory quail, but did not prevent the onset of scotosensitivity. It is concluded that the 12-hr temporal relationship of circadian serotonergic and dopaminergic oscillations not only eliminates photorefractoriness but may also re-establish photosensitivity in relative photorefractory quail. These findings suggest the regulatory role of neural oscillations and their temporal interaction in the regulation of neuroendocrine-gonadal axis with special reference to photosensitivity/refractoriness.     

Neuropeptide S-mediated facilitation of synaptic transmission enforces subthreshold theta oscillations within the lateral amygdala

The neuropeptide S (NPS) receptor system modulates neuronal circuit activity in the amygdala in conjunction with fear, anxiety and the expression and extinction of previously acquired fear memories. Using in vitro brain slice preparations of transgenic GAD67-GFP (Δneo) mice, we investigated the effects of NPS on neural activity in the lateral amygdala as a key region for the formation and extinction of fear memories. We are able to demonstrate that NPS augments excitatory glutamatergic synaptic input onto both projection neurons and interneurons of the lateral amygdala, resulting in enhanced spike activity of both types of cells. These effects were at least in part mediated by presynaptic mechanisms. In turn, inhibition of projection neurons by local interneurons was augmented by NPS, and subthreshold oscillations were strengthened, leading to their shift into the theta frequency range. These data suggest that the multifaceted effects of NPS on amygdaloid circuitry may shape behavior-related network activity patterns in the amygdala and reflect the peptide's potent activity in various forms of affective behavior and emotional memory.     

Linking neural activity and molecular oscillations in the SCN

Neurons in the suprachiasmatic nucleus (SCN) function as part of a central timing circuit that drives daily changes in our behaviour and underlying physiology. A hallmark feature of SCN neuronal populations is that they are mostly electrically silent during the night, start to fire action potentials near dawn and then continue to generate action potentials with a slow and steady pace all day long. Sets of currents are responsible for this daily rhythm, with the strongest evidence for persistent Na(+) currents, L-type Ca(2+) currents, hyperpolarization-activated currents (I(H)), large-conductance Ca(2+) activated K(+) (BK) currents and fast delayed rectifier (FDR) K(+) currents. These rhythms in electrical activity are crucial for the function of the circadian timing system, including the expression of clock genes, and decline with ageing and disease. This article reviews our current understanding of the ionic and molecular mechanisms that drive the rhythmic firing patterns in the SCN.     

Propagation effects of current and conductance noise in a model neuron with subthreshold oscillations

We have examined the effects of current and conductance noise in a single-neuron model which can generate a variety of physiologically important impulse patterns. Current noise enters the membrane equation directly while conductance noise is propagated through the activation variables. Additive Gaussian white noise which is implemented as conductance noise appears in the voltage equations as an additive and a multiplicative term. Moreover, the originally white noise is turned into colored noise. The noise correlation time is a function of the system's control parameters which may explain the different effects of current and conductance noise in different dynamic states. We have found the most significant, qualitative differences between different noise implementations in a pacemaker-like, tonic firing regime at the transition to chaotic burst discharges. This reflects a dynamic state of high physiological relevance.     

Slow oscillations in neural networks with facilitating synapses

The synchronous oscillatory activity characterizing many neurons in a network is often considered to be a mechanism for representing, binding, conveying, and organizing information. A number of models have been proposed to explain high-frequency oscillations, but the mechanisms that underlie slow oscillations are still unclear. Here, we show by means of analytical solutions and simulations that facilitating excitatory (E _f) synapses onto interneurons in a neural network play a fundamental role, not only in shaping the frequency of slow oscillations, but also in determining the form of the up and down states observed in electrophysiological measurements. Short time constants and strong E _f synapse-connectivity were found to induce rapid alternations between up and down states, whereas long time constants and weak E _f synapse connectivity prolonged the time between up states and increased the up state duration. These results suggest a novel role for facilitating excitatory synapses onto interneurons in controlling the form and frequency of slow oscillations in neuronal circuits.     

BDNF boosts spike fidelity in chaotic neural oscillations

Oscillatory activity and its nonlinear dynamics are of fundamental importance for information processing in the central nervous system. Here we show that in aperiodic oscillations, brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family, enhances the accuracy of action potentials in terms of spike reliability and temporal precision. Cultured hippocampal neurons displayed irregular oscillations of membrane potential in response to sinusoidal 20-Hz somatic current injection, yielding wobbly orbits in the phase space, i.e., a strange attractor. Brief application of BDNF suppressed this unpredictable dynamics and stabilized membrane potential fluctuations, leading to rhythmical firing. Even in complex oscillations induced by external stimuli of 40 Hz (gamma) on a 5-Hz (theta) carrier, BDNF-treated neurons generated more precisely timed spikes, i.e., phase-locked firing, coupled with theta-phase precession. These phenomena were sensitive to K252a, an inhibitor of tyrosine receptor kinases and appeared attributable to BDNF-evoked Na(+) current. The data are the first indication of pharmacological control of endogenous chaos. BDNF diminishes the ambiguity of spike time jitter and thereby might assure neural encoding, such as spike timing-dependent synaptic plasticity.     

Conductance versus current noise in a neuronal model for noisy subthreshold oscillations and related spike generation

Biological systems are notoriously noisy. Noise, therefore, also plays an important role in many models of neural impulse generation. Noise is not only introduced for more realistic simulations but also to account for cooperative effects between noisy and nonlinear dynamics. Often, this is achieved by a simple noise term in the membrane equation (current noise). However, there are ongoing discussions whether such current noise is justified or whether rather conductance noise should be introduced because it is closer to the natural origin of noise. Therefore, we have compared the effects of current and conductance noise in a neuronal model for subthreshold oscillations and action potential generation. We did not see any significant differences in the model behavior with respect to voltage traces, tuning curves of interspike intervals, interval distributions or frequency responses when the noise strength is adjusted. These findings indicate that simple current noise can give reasonable results in neuronal simulations with regard to physiological relevant noise effects.     

On the activities in a continuous neural network

In this work an attempt is made to study the activities in a continuous neural system. The neural model considered here is a two dimensional continuous version of an earlier discrete model investigated in a series of papers [5–8]. The variations of the normalized firing rates in the present model are described by a nonlinear integro-partial differential equation. The conditions for the existence and uniqueness of the solutions of the describing equation subject to an initial condition are established and the steady-state solutions are investigated for inputs which are constant with respect to time. Depending on the parameters which are related to the self-inhibition and adaptation properties of the neural network, some of the oscillatory and stability properties of the solutions of the describing equation are discussed.     

Techniques for temporal detection of neural sensitivity to external stimulation

We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning.     

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.     

Global rhythmic activities in hippocampal neural fields and neural coding

Global oscillations of the neural field represent some of the most interesting expressions of the hippocampal activity, being related also to learning and memory. To study oscillatory activities of the CA3 field in theta range, a model of this sub-field of Hippocampus has been formulated. The model describes the firing activity of CA3 neuronal populations within the frame of a kinetic theory of neural systems and it has been used for computer simulations. The results show that the propagation of activities induced in the neural field by hippocampal afferents occurs only in narrow time windows confined by inhibitory barrages, whose time-course follows the theta rhythm. Moreover, during each period of a theta wave, the entire CA3 field bears a firing activity with peculiar space-time patterns, a sort of specific imprint, which can induce effects with similar patterns on brain regions driven by the hippocampal formation. The simulation has also demonstrated the ability of medial septum to influence the global activity of the CA3 pyramidal population through the control of the population of inhibitory interneurons. At last, the possible involvement of global population oscillations in neural coding has been discussed.     

Population oscillations in a discrete model of neural networks of the brain

A discrete model of biological neural networks is used to find out how synchronized firing of neurons emerges in a randomly connected neural population. The objective is to understand the mechanisms underlying brain waves and to find and characterize conditions which support spontaneous switching from disordered to rhythmic population activity as in case of an epileptic seizure. The model is kept as simple as possible to achieve on one hand a fast performance of computer simulations of networks with up to 10,000 neurons and to keep on the other hand an overview of parameter dependences. Dynamics of the model can be classified into different regimes: random fluctuations, rhythmic oscillations and silence. When the ratio of the inhibitory/excitatory connectivity is raised the system crosses from the fluctuating regime through the rhythmic oscillating region to the silence regime. Close to the boundary between the fluctuating and the oscillating regimes the network shows spontaneous bursting of high amplitude rhythmic oscillations, which is characteristic of epileptiform behavior. The simulation results are in agreement with recent theories saying that focal epilepsy after injury of the brain could result from axonal sprouting of GABAergic neurons in the injured region.     

Competition between Delta and the Abruptex domain of Notch

Background  

Extracellular domains of the Notch family of signalling receptors contain many EGF repeat domains, as do their major ligands. Some EGF repeats are modified by O-fucosylation, and most have no identified role in ligand binding.     

Specific subunits of voltage-gated sodium channel underlying the subthreshold membrane potential oscillations: Potential therapeutic targets for neuropathic pain

The treatment of neuropathic pain remains a major challenge to pain clinicians. Certain nociceptive and non-nociceptive dorsal root ganglion (DRG) neurons may develop abnormal spontaneous activities following peripheral nerve injury, which is believed to be a major contributor to chronic pain. Subthreshold membrane potential oscillation (SMPO) observed in injured DRG neurons was reported to be involved in the generation of abnormal spontaneous activity. Tetrodotoxin-sensitive sodium (Na⁺) channels were testified to be involved in the generation of SMPO, but their specific subunits have not been clarified. We hypothesize that the subunits of voltage-gated sodium channel, Na_v1.3 and Na_v1.6, are involved in the generation of SMPO. An attempt to test this hypothesis may lead to a new therapeutic strategy for neuropathic pain.     

Existence and stability criteria for phase-locked modes in ring neural networks based on the spike time resetting curve method

We developed a systematic and consistent mathematical approach to predicting 1:1 phase-locked modes in ring neural networks of spiking neurons based on the open loop spike time resetting curve (STRC) and its almost equivalent counterpart—the phase resetting curve (PRC). The open loop STRCs/PRCs were obtained by injecting into an isolated model neuron a triangular shaped time-dependent stimulus current closely resembling an actual synaptic input. Among other advantages, the STRC eliminates the confusion regarding the undefined phase for stimuli driving the neuron outside of the unperturbed limit cycle. We derived both open loop PRC and STRC-based existence and stability criteria for 1:1 phase-locked modes developed in ring networks of spiking neurons. Our predictions were in good agreement with the closed loop numerical simulations. Intuitive graphical methods for predicting phase-locked modes were also developed both for half-centers and for larger ring networks.