Spiking Behavior Research Articles

Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study

BackgroundRecent genome-wide association studies have identified a large number of genetic risk factors for schizophrenia (SCZ) featuring ion channels and calcium transporters. For some of these risk factors, independent prior investigations have examined the effects of genetic alterations on the cellular electrical excitability and calcium homeostasis. In the present proof-of-concept study, we harnessed these experimental results for modeling of computational properties on layer V cortical pyramidal cells and identified possible common alterations in behavior across SCZ-related genes. MethodsWe applied a biophysically detailed multicompartmental model to study the excitability of a layer V pyramidal cell. We reviewed the literature on functional genomics for variants of genes associated with SCZ and used changes in neuron model parameters to represent the effects of these variants. ResultsWe present and apply a framework for examining the effects of subtle single nucleotide polymorphisms in ion channel and calcium transporter-encoding genes on neuron excitability. Our analysis indicates that most of the considered SCZ-related genetic variants affect the spiking behavior and intracellular calcium dynamics resulting from summation of inputs across the dendritic tree. ConclusionSOur results suggest that alteration in the ability of a single neuron to integrate the inputs and scale its excitability may constitute a fundamental mechanistic contributor to mental disease, alongside the previously proposed deficits in synaptic communication and network behavior.

Sensory-Evoked Spiking Behavior Emerges via an Experience-Dependent Plasticity Mechanism

The ability to generate action potentials (spikes) in response to synaptic input determines whether a neuron participates in information processing. How a developing neuron becomes an active participant in a circuit or whether this process is activity dependent is not known, especially as spike-dependent plasticity mechanisms would not be available to non-spiking neurons. Here we use the optic tectum of awake Xenopus laevis tadpoles to determine how a neuron becomes able to generate sensory-driven spikes in vivo. At the onset of vision, many tectal neurons do not exhibit visual spiking behavior, despite being intrinsically excitable and receiving visuotopically organized synaptic inputs. However, a brief period of visual stimulation can drive these neurons to start generating stimulus-driven spikes. This conversion relies upon a selective increase in glutamatergic input and requires depolarizing GABAergic transmission and NMDA receptor activation. This permissive form of experience-dependent plasticity enables a neuron to start contributing to circuit function.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation.

Towards Spike based Models of Visual Attention in the Brain

A numerical solution of Hodgkin Huxley equations is presented to simulate the spiking behavior of a biological neuron. The solution is illustrated by building a graphical chart interface to finely tune the behavior of the neuron under different stimulations. In addition, a Multi-Agent System (MAS) has been developed to simulate the Visual Attention Network Model of the brain. Tasks are assigned to the agents according to the Attention Network Theory, developed by neuroscientists. A sequential communication model based on simple objects has been constructed, aiming to show the relations and the workflow between the different visual attention networks. Each agent is being used as an analogy to a role or function of the visual attention systems in the brain. Some experimental results based on this model have been presented in an earlier paper. The two approaches are at the moment not integrated. The long term goal is to develop an integrated parallel layered object model of the visual attention process, as a tool for simulating neuron interactions described by Hodgkin Huxley's equations or the Leaky-Integrate-and-Fire model.

Influence of trapping and de-trapping charge in MOS-like structures with single and twofold SiOx films as active layers

Single and twofold non-stoichiometric silicon oxide (SiOx) films are considered as an active layer in metal oxide semiconductor (MOS)-like structures. Such films were obtained by hot filament chemical vapor deposition (HFCVD) technique in the range of temperatures from 900°C to 1150°C. They were subjected to a thermal annealing at 1100°C. In order to know their optical and electrical properties, their transmittance spectra, absorption coefficients, absorbance (FTIR) spectra, photoluminescence (PL) spectra, current–voltage (I–V) curves and capacitance–voltage (C–V) ones were obtained. The optical energy gap of twofold SiOx films as a function of the growth temperature (Tg), suffers non-linear variations in the range from 2.25 to 1.8eV when we make combinations of different Tǵs in both films; besides, such films exhibit compositional changes when Tg is varied and a restructuration (phase separation) takes place because of thermal annealing. Light emission in single and twofold SiOx films is ascribed to both silicon nanocrystals (Si-ncs) and some kinds of defects, which are produced during the growth process and thermal annealing. In the case of twofold films it is observed that the effect of thermal annealing induces emission in the orange-near infrared range and in the violet-blue bands but without the presence of the green–yellow band. MOS-like structures using single and twofold films, show different electrical phenomena such as the influence of trapping and de-trapping charge. The influence of Si-ncs and defects gives place to spike and stair-like behaviour in I–V curves. Measurements of the electrical I–V and C–V curves are obtained under dark conditions.

Explaining Dopamine Deficiency of Spiking Neurons Based on Quantum Superposition Theory

In all mammalian brain billions of neurons exist and these neurons are working together through synapses and spike responses. Through numerous models presented to describe neuronal response and how the membrane potential would vary due to different tasks, still a big question stand still in the field of neuroscience. Why a neuron can produce different types of spikes under same conditions? Here we build up a new model of spiking neuron based on Izhikevich model which can generate all possible neuronal spikes and we try to answer this question by putting the concept of quantum superposition into account. We showed that unknown and random behavior of neuronal spikes through time can be described by this quantum fact that a neuron can be in many states at a same time, therefore any spike patterns or combination of spike patterns is of interest. Based on neuronal spikes patterns, sometimes we observed an unknown pattern can be explained by this phenomena that a neuron can exist partially in a physical state but when it’s response to an stimuli is recorded the result would be only one of the possible states or a linear combination of some states.

Modeling neural activity with cumulative damage distributions.

Neurons transmit information as action potentials or spikes. Due to the inherent randomness of the inter-spike intervals (ISIs), probabilistic models are often used for their description. Cumulative damage (CD) distributions are a family of probabilistic models that has been widely considered for describing time-related cumulative processes. This family allows us to consider certain deterministic principles for modeling ISIs from a probabilistic viewpoint and to link its parameters to values with biological interpretation. The CD family includes the Birnbaum-Saunders and inverse Gaussian distributions, which possess distinctive properties and theoretical arguments useful for ISI description. We expand the use of CD distributions to the modeling of neural spiking behavior, mainly by testing the suitability of the Birnbaum-Saunders distribution, which has not been studied in the setting of neural activity. We validate this expansion with original experimental and simulated electrophysiological data.

Frequency-domain order parameters for the burst and spike synchronization transitions of bursting neurons

We are interested in characterization of synchronization transitions of bursting neurons in the frequency domain. Instantaneous population firing rate (IPFR) [Formula: see text], which is directly obtained from the raster plot of neural spikes, is often used as a realistic collective quantity describing population activities in both the computational and the experimental neuroscience. For the case of spiking neurons, a realistic time-domain order parameter, based on [Formula: see text], was introduced in our recent work to characterize the spike synchronization transition. Unlike the case of spiking neurons, the IPFR [Formula: see text] of bursting neurons exhibits population behaviors with both the slow bursting and the fast spiking timescales. For our aim, we decompose the IPFR [Formula: see text] into the instantaneous population bursting rate [Formula: see text] (describing the bursting behavior) and the instantaneous population spike rate [Formula: see text] (describing the spiking behavior) via frequency filtering, and extend the realistic order parameter to the case of bursting neurons. Thus, we develop the frequency-domain bursting and spiking order parameters which are just the bursting and spiking "coherence factors" [Formula: see text] and [Formula: see text] of the bursting and spiking peaks in the power spectral densities of [Formula: see text] and [Formula: see text] (i.e., "signal to noise" ratio of the spectral peak height and its relative width). Through calculation of [Formula: see text] and [Formula: see text], we obtain the bursting and spiking thresholds beyond which the burst and spike synchronizations break up, respectively. Consequently, it is shown in explicit examples that the frequency-domain bursting and spiking order parameters may be usefully used for characterization of the bursting and the spiking transitions, respectively.

Ablation of oxide materials and production of nanopowders by ytterbium fiber laser

In this paper, laser radiation action on Nd3+:Y2O3 targets having the absorption 13–1.7 × 103 cm−1 is studied. During the experiment, we used the ytterbium fiber laser, which can operate both in continuous and pulse mode with the pulse duration time 0.1–1.9 ms. Laser radiation power was 200–700 W, and power density was (0.2–1.2) × 106 W/cm2. We found out the spike behavior of laser plume luminescence, large statistical variability of delay time of laser plume appearance, and spallation of front surface of the target. Aiming to explain these facts, we developed the numeric model of radiation impact on substance with non-uniform absorption coefficient. We found out the optimal laser oblation mode for nanopowder production. At laser power of 700 W, we received Nd3+:Y2O3 nanopowder with productivity up to 27 g/h. Average size of nanoparticles is 13.6 nm, and their phase is monoclinic.

Emergent spike patterns in neuronal populations.

This numerical study documents and analyzes emergent spiking behavior in local neuronal populations. Emphasis is given to a phenomenon we call clustering, by which we refer to a tendency of random groups of neurons large and small to spontaneously coordinate their spiking activity in some fashion. Using a sparsely connected network of integrate-and-fire neurons, we demonstrate that spike clustering occurs ubiquitously in both high firing and low firing regimes. As a practical tool for quantifying such spike patterns, we propose a simple scheme with two parameters, one setting the temporal scale and the other the amount of deviation from the mean to be regarded as significant. Viewing population activity as a sequence of events, meaning relatively brief durations of elevated spiking, separated by inter-event times, we observe that background activity tends to give rise to extremely broad distributions of event sizes and inter-event times, while driving a system imposes a certain regularity on its inter-event times, producing a rhythm consistent with broad-band gamma oscillations. We note also that event sizes and inter-event times decorrelate very quickly. Dynamical analyses supported by numerical evidence are offered.

Neonatal NMDA receptor blockade disrupts spike timing and glutamatergic synapses in fast spiking interneurons in a NMDA receptor hypofunction model of schizophrenia.

The dysfunction of parvalbumin-positive, fast-spiking interneurons (FSI) is considered a primary contributor to the pathophysiology of schizophrenia (SZ), but deficits in FSI physiology have not been explicitly characterized. We show for the first time, that a widely-employed model of schizophrenia minimizes first spike latency and increases GluN2B-mediated current in neocortical FSIs. The reduction in FSI first-spike latency coincides with reduced expression of the Kv1.1 potassium channel subunit which provides a biophysical explanation for the abnormal spiking behavior. Similarly, the increase in NMDA current coincides with enhanced expression of the GluN2B NMDA receptor subunit, specifically in FSIs. In this study mice were treated with the NMDA receptor antagonist, MK-801, during the first week of life. During adolescence, we detected reduced spike latency and increased GluN2B-mediated NMDA current in FSIs, which suggests transient disruption of NMDA signaling during neonatal development exerts lasting changes in the cellular and synaptic physiology of neocortical FSIs. Overall, we propose these physiological disturbances represent a general impairment to the physiological maturation of FSIs which may contribute to schizophrenia-like behaviors produced by this model.

Research Update: Reactively sputtered nanometer-thin ZrN film as a diffusion barrier between Al and boron layers for radiation detector applications

In this paper, optimization of the process flow for PureB detectors is investigated. Diffusion barrier layers between a boron layer and the aluminum interconnect can be used to enhance the performance and visual appearance of radiation detectors. Few nanometers-thin Zirconium Nitride (ZrN) layer deposited by reactive sputtering in a mixture of Ar/N2, is identified as a reliable diffusion barrier with better fabrication process compatibility than others. The barrier properties of this layer have been tested for different boron layers deposited at low and high temperatures with extensive optical microscopy analyses, electron beam induced current, SEM, and electrical measurements. This study demonstrated that spiking behavior of pure Al on Si can be prevented by the thin ZrN layer thus improving the performance of the radiation detectors fabricated using boron layer.

Emergent spiking in non-ideal memristor networks

Memristors have uses as artificial synapses and perform well in this role in simulations with artificial spiking neurons. Our experiments show that memristor networks natively spike and can exhibit emergent oscillations and bursting spikes. Networks of near-ideal memristors exhibit behaviour similar to a single memristor and combine in circuits like resistors do. Spiking is more likely when filamentary memristors are used or the circuits have a higher degree of compositional complexity (i.e. a larger number of anti-series or anti-parallel interactions). 3-memristor circuits with the same memristor polarity (low compositional complexity) are stabilised and do not show spiking behaviour. 3-memristor circuits with anti-series and/or anti-parallel compositions show richer and more complex dynamics than 2-memristor spiking circuits. We show that the complexity of these dynamics can be quantified by calculating (using partial auto-correlation functions) the minimum order auto-regression function that could fit it. We propose that these oscillations and spikes may have similar phenomena to brainwaves and neural spike trains and suggest that these behaviours can be used to perform neuromorphic computation.

Induced Coherence Resonance in an Electrochemical System

Applying noise to an excitable electrochemical systems near homoclinic orbits may lead to the emergence of coherent dynamics. A noise driver-control was developed for this purpose, using a neural network reference model assisted by a Kalman filter. The model is constructed to predict the occurrence of spiking behavior and its long-term prediction describes the statistics of the spike train. The strategy involving the use of the Kalman filter allows for the on-line correction of model inaccuracies. This adaptive protocol is coupled with a stochastic search algorithm, allowing the pinpointing of the optimal noise amplitude that induces maximum coherence resonance in the system. The protocol was tested in an experimental Fe-potassium sulfate system, exhibiting a robust response even in the presence of electrochemical drift.

Combining machine learning and simulations of a morphologically realistic model to study modulation of neuronal activity in cerebellar nuclei during absence epilepsy

Epileptic absence seizures are characterized by synchronized oscillatory activity in the cerebral cortex that can be recorded as so-called spike-and-wave discharges (SWDs) by electroencephalogram. Although the cerebral cortex and the directly connected thalamus are paramount to this particular form of epilepsy, several other parts of the mammalian brain are likely to influence this oscillatory activity. We have recently shown that some of the cerebellar nuclei (CN) neurons, which form the main output of the cerebellum, show synchronized oscillatory activity during episodes of cortical SWDs in two independent mouse models of absence epilepsy [1]. The CN neurons that show this significant correlation with the SWDs are deemed to “participate” in the seizure activity and are therefore used in our current study designed to unravel the potential causes of such oscillatory firing patterns. Initially, we set out to study if different types of CN neurons are more prone to show modulated firing patterns during seizure activity. We applied Growing Neural Gas (GNG) [2], an unsupervised clustering algorithm, on the interictal activity, i.e., firing patterns recorded in between seizures, using the measures CV, log-interval entropy, permutation entropy and firing rate. Three main groups of neurons were found by the clustering algorithm, in which the neurons were predominantly participating in the seizures. These can be seen on Figure ​Figure11 as the green, yellow and pale blue crosses (+). Moreover, these three clusters have the highest CV (and therefore more irregular) and higher log-interval entropy (more unpredictable). Further, we used a Gaussian Process Regression model [3] to predict the extent of participation of the neurons in the seizure activity, based on two measures: Z-score of mean power at seizure frequency (6-9Hz) (FFT based Z-score) and modulation frequency. These characterize the extent to which CN neurons phase-lock their spiking activity to the spikes in the EEG during seizures. We achieved a good prediction rate (r = 0.56, p < 0.05 for FFT based Z-score; r = 0.45, p < 0.05 for modulation frequency) using this method. Also, we are using a compartmental model of a CN neuron with realistic morphology [4] to investigate the input conditions that can generate interictal activity found in the participating neurons. Our results indicate that bursting in the Purkinje cell or mossy fiber input can cause behavior that is similar to the interictal activity found in participating neurons. The black dot in Figure ​Figure11 shows the output from the CN neuron model, provided with a bursting Purkinje cell input, when it is subjected to clustering with the experimental data. Desynchronization of the burst occurrence in the input did not alter the position of the data point drastically[0]. Currently, we are in the process of applying an evolutionary algorithm to explore in detail the input conditions that can that lead to the spiking behavior that is associated with seizures. Figure 1 shows 2D projection, using principal component analysis, of the clusters formed as a result of GNG clustering of the CN neuron interictal activity using CV, Log-Interval Entropy, Firing rate and Permutation Entropy. The crosses (+) indicate cells that ...

Estimating the biophysical properties of neurons with intracellular calcium dynamics

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

A simple model of cortical culture growth: burst property dependence on network composition and activity.

This paper describes large-scale simulations of growth, network formation, and behavior in cultures of dissociated cortical cells. A neuron model that incorporates synaptic facilitation/depression and neurite outgrowth/retraction was used to construct virtual cultures of 10,000 cells whose spiking behavior and evolution were investigated in closed-loop simulations. This approach allows us to perform detailed analysis of the effects of model parameters on burst shape and timing, their changes, and the interrelationship among these behaviors, gross network structure, and model parameters. We examined the effects of two parameters--network composition (fraction of excitatory cells) and neuron excitability (activity level corresponding to neurite outgrowth equilibrium)--on network structure and behavior. Our results suggest that much of the burst shape and timing observed in vitro can be explained by a model that includes only closed-loop neurite outgrowth and dynamic synapses; features such as LTP/LTD, random connectivity, long-distance connections, and detailed neurite topology are not necessary.

Learning intrinsic excitability in medium spiny neurons

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

One-dimensional map-based neuron model: A logistic modification

A one-dimensional map is proposed for modeling some of the neuronal activities, including different spiking and bursting behaviors. The model is obtained by applying some modifications on the well-known Logistic map and is named the Modified and Confined Logistic (MCL) model. Map-based neuron models are known as phenomenological models and recently, they are widely applied in modeling tasks due to their computational efficacy. Most of discrete map-based models involve two variables representing the slow-fast prototype. There are also some one-dimensional maps, which can replicate some of the neuronal activities. However, the existence of four bifurcation parameters in the MCL model gives rise to reproduction of spiking behavior with control over the frequency of the spikes, and imitation of chaotic and regular bursting responses concurrently. It is also shown that the proposed model has the potential to reproduce more realistic bursting activity by adding a second variable. Moreover the MCL model is able to replicate considerable number of experimentally observed neuronal responses introduced in Izhikevich (2004) [23]. Some analytical and numerical analyses of the MCL model dynamics are presented to explain the emersion of complex dynamics from this one-dimensional map.

Conditional Deletions of Epilepsy-Associated KCNQ2 and KCNQ3 Channels from Cerebral Cortex Cause Differential Effects on Neuronal Excitability

KCNQ2 and KCNQ3 potassium channels have emerged as central regulators of pyramidal neuron excitability and spiking behavior. However, despite an abundance of evidence demonstrating that KCNQ2/3 heteromers underlie critical potassium conductances, it is unknown whether KCNQ2, KCNQ3, or both are obligatory for maintaining normal pyramidal neuron excitability. Here, we demonstrate that conditional deletion of Kcnq2 from cerebral cortical pyramidal neurons in mice results in abnormal electrocorticogram activity and early death, whereas similar deletion of Kcnq3 does not. At the cellular level, Kcnq2-null, but not Kcnq3-null, CA1 pyramidal neurons show increased excitability manifested as a decreased medium afterhyperpolarization and a longer-lasting afterdepolarization. As a result, these Kcnq2-deficient neurons are hyperexcitable, responding to current injections with an increased number and frequency of action potentials. Biochemically, the Kcnq2 deficiency secondarily results in a substantial loss of KCNQ3 and KCNQ5 protein levels, whereas loss of Kcnq3 only leads to a modest reduction of other KCNQ channels. Consistent with this finding, KCNQ allosteric activators can still markedly dampen neuronal excitability in Kcnq3-null pyramidal neurons, but have only weak effects in Kcnq2-null pyramidal neurons. Together, our data reveal the indispensable function of KCNQ2 channels at both the cellular and systems levels, and demonstrate that pyramidal neurons have near normal excitability in the absence of KCNQ3 channels.