Muhammet Uzuntarla

Controlling the spontaneous spiking regularity via channel blocking on Newman-Watts networks of Hodgkin-Huxley neurons

We investigate the regularity of spontaneous spiking activity on Newman-Watts small-world networks consisting of biophysically realistic Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise and fraction of blocked voltage-gated sodium and potassium ion channels embedded in neuronal membranes. We show that there exists an optimal fraction of shortcut links between physically distant neurons, as well as an optimal intensity of intrinsic noise, which warrant an optimally ordered spontaneous spiking activity. This doubly coherence resonance-like phenomenon depends significantly on, and can be controlled via, the fraction of closed sodium and potassium ion channels, whereby the impacts can be understood via the analysis of the firing rate function as well as the deterministic system dynamics. Potential biological implications of our findings for information propagation across neural networks are also discussed.

Weak signal propagation through noisy feedforward neuronal networks.

We determine under which conditions the propagation of weak periodic signals through a feedforward Hodgkin–Huxley neuronal network is optimal. We find that successive neuronal layers are able to amplify weak signals introduced to the neurons forming the first layer only above a certain intensity of intrinsic noise. Furthermore, we show that as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the feedforward neuronal network, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted.

Double inverse stochastic resonance with dynamic synapses

We investigate the behavior of a model neuron that receives a biophysically realistic noisy postsynaptic current based on uncorrelated spiking activity from a large number of afferents. We show that, with static synapses, such noise can give rise to inverse stochastic resonance (ISR) as a function of the presynaptic firing rate. We compare this to the case with dynamic synapses that feature short-term synaptic plasticity and show that the interval of presynaptic firing rate over which ISR exists can be extended or diminished. We consider both short-term depression and facilitation. Interestingly, we find that a double inverse stochastic resonance (DISR), with two distinct wells centered at different presynaptic firing rates, can appear.

Impact of synaptic noise and conductance state on spontaneous cortical firing

Cortical neurons in-vivo operate in a continuum of overall conductance states, depending on the average level of background synaptic input throughout the dendritic tree. We compare how variability, or fluctuations, in this input affects the statistics of the resulting ‘spontaneous’ or ‘background’ firing activity, between two extremes of the mean input corresponding to a low-conductance (LC) and a high-conductance (HC) state. In the HC state, we show that both firing rate and regularity increase with increasing variability. In the LC state, firing rate also increases with input variability, but in contrast to the HC state, firing regularity first decreases and then increases with an increase in the variability. At high levels of input variability, firing regularity in both states converge to similar values.

Inverse stochastic resonance in networks of spiking neurons

Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron’s intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

Propagation of firing rate in a noisy feedforward biological neural network

In this study, we investigate the input firing rate propagation in a feedforward biological neural network composed of multiple layers. Dynamical behaviour of neurons in the network are modeled by using stochastic Hodgkin-Huxley equations which considers the probabilistic nature of ion channels embedded in neuronal membranes. Thus, firing rate propagation is studied in a biophysically more realistic manner by including ion channel noise which is ignored in previous studies. Input rate information in the network is provided by varying the cell size in the first layer. We show that the efficent transmission of input firing rate through the network can be achieved via the synchronization mechanism within the neurons in layers. We also show that this synchronization araise from the synaptic current variance increase and provided by adjusting the cell size or the intrinsic channel noise strength in layers to an optimal value.

Effects of dynamic synapses on noise-delayed response latency of a single neuron

The noise-delayed decay (NDD) phenomenon emerges when the first-spike latency of a periodically forced stochastic neuron exhibits a maximum for a particular range of noise intensity. Here, we investigate the latency response dynamics of a single Hodgkin-Huxley neuron that is subject to both a suprathreshold periodic stimulus and a background activity arriving through dynamic synapses. We study the first-spike latency response as a function of the presynaptic firing rate f. This constitutes a more realistic scenario than previous works, since f provides a suitable biophysically realistic parameter to control the level of activity in actual neural systems. We first report on the emergence of classical NDD behavior as a function of f for the limit of static synapses. Second, we show that when short-term depression and facilitation mechanisms are included at the synapses, different NDD features can be found due to their modulatory effect on synaptic current fluctuations. For example, an intriguing double NDD (DNDD) behavior occurs for different sets of relevant synaptic parameters. Moreover, depending on the balance between synaptic depression and synaptic facilitation, single NDD or DNDD can prevail, in such a way that synaptic facilitation favors the emergence of DNDD whereas synaptic depression favors the existence of single NDD. Here we report the existence of the DNDD effect in the response latency dynamics of a neuron.

Inverse stochastic resonance induced by ion channel noise

Recent work has considered the inhibitory effects of noise on neuronal activity, particularly on rhythmic firing. For example, Paydarfar et al. [1] studied the influence of noise on neuronal pacemakers in an in vitro preparation of the squid giant axon, and found that small noisy currents induce an on-off switching behavior between two nearby regimes: repetitive firing and quiescence. They also showed that the timings of on-off switching of the pacemaker depend on the intensity and spectral properties of noisy current. Tuckwell et al. [2,3] further investigated the inhibitory effect of noise in a single Hodgkin-Huxley neuron. These authors show that in a model neuron subject to stochastic external additive noise, the average firing rate exhibits a minimum as the noise amplitude is varied. The authors called this phenomenon Inverse Stochastic Resonance (ISR), in contrast to the well-known phenomenon of stochastic resonance. In these modelling studies [2,3], noise was incorporated by adding an external noisy current. Here, we consider the ISR phenomenon in the Hodgkin-Huxley neuron with a more biophysically realistic model of noise: that resulting from the stochastic nature of voltage-gated ion channels embedded in neuronal membranes. The intensity of channel noise is related to the total number of channels for fixed channel density, i.e. when the number of ion channels (or the cell size) is small, the stochasticity of channels imparts strong noise intensity to the neuron’s dynamics. Our results show that the ISR phenomenon is also present in the case of ion channel noise. We clarify the mechanism that underlies ISR and show that the most surprising feature – the increase in average firing rate as the noise decreases (membrane area increases) – is a consequence of the dynamical structure of the model and the averaging procedure (Figure ​(Figure1).1). We also discuss the relative contributions of the different channel subunits to transitions from the spiking to the rest state, and vice versa, in the noisy case. Figure 1 (A) Inverse stochastic resonance: average firing rate versus membrane area for different values of constant input current I0. Effective channel noise intensity is smaller for larger membrane area. Right: sample traces from random initial conditions used ...

Vibrational resonance in a scale-free network with different coupling schemes

Abstract We investigate the phenomenon of vibrational resonance (VR) in neural populations, whereby weak low-frequency signals below the excitability threshold can be detected with the help of additional high-frequency driving. The considered dynamical elements consist of excitable FitzHugh–Nagumo neurons connected by electrical gap junctions and chemical synapses. The VR performance of these populations is studied in unweighted and weighted scale-free networks. We find that although the characteristic network features – coupling strength and average degree – do not dramatically affect the signal detection quality in unweighted electrically coupled neural populations, they have a strong influence on the required energy level of the high-frequency driving force. On the other hand, we observe that unweighted chemically coupled populations exhibit the opposite behavior, and the VR performance is significantly affected by these network features whereas the required energy remains on a comparable level. Furthermore, we show that the observed VR performance for unweighted networks can be either enhanced or worsened by degree-dependent coupling weights depending on the amount of heterogeneity.

Synchronization induced termination in neuronal networks

Vital functions which take place in the brain are accomplished through synchronized neural activities between interconnected neuronal populations. In healthy and unhealthy nerve system, rhythms in different frequencies can be recognized as they are responsible for neural synchronization. Despite this, in the case of global synchronization where oscillation power is the maximum, as observed in epileptic seizures, neural activity can terminate. In this study, effects of neural network characteristics synchronization emerges from on activity termination are computationally investigated. Stochastic Hodgkin-Huxley (H-H) equations are used in the simulations. The results show that synchronization increases when neurons have high synaptic conductance and due to this, strong synaptic current neurons exposed to terminates the firings.