Ali Calim

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.

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.

Simulation of Parkinsonian Basal nuclei with network motifs

Nowadays, neurodegenerative diseases which affect human life quite negatively with motor, cognitive and psychiatric disorders are becoming widespread. One of the most common neurodegenerative disorder is Parkinsons disease. Recent electrophysiological experiments have shown that Basal Ganglia, a special region in the midbrain, is related to Parkinsonism. Beta frequency oscillations, which are important symptoms of Parkinsons disease, emerge intensively in Globus Pallidus and Subtalamus nuclei. In this study, anatomical connections of Globus Pallidus and Subtalamus are constructed computationally, and the cellular properties that give rise to emergence of beta oscillations are investigated.

Effects of subthreshold excitation characteristics on vibrational resonance in weighted scale-free network

In this paper, the phenomena of Vibrational Resonance is investigated in an excitable system which consists of FitzHugh-Nagumo neurons with electrical coupling. Weak signal detection performance of excitable system is examined in scale-free network (unweighted or weighted) topology. The simulation results show that; weighting the scale-free network, average connectivity degree, amplitude and frequency of weak signal play an active role to determine the data carrying performance of neurons based on Vibrational Resonance. It is determined that, the amount of required energy for creating resonance peaks of excitable system is decreased significantly by choosing the correct value of weight control parameter in a weighted network especially.

Effects of synaptic time delay on vibrational resonance in neuronal networks

In the phenomenon of vibrational resonance, the excitable system is under the influence of two periodic forces: a low-frequency (signal), a high-frequency (carrier). In this study, the effects of synaptic time delay on the vibrational resonance were investigated in two coupled FitzHugh-Nagumo neurons with electrical or chemical coupling. It is seen that, for both types of coupling by appropriate choice of synaptic time delay can be had a curative effect to transmission between two neurons at certain values of synaptic conductivity.

Effects of synaptic heterogenity on vibrational resonance in biological neural networks

In this study, vibrational resonance phenomena is investigated for topologies of scale-free network in excitable neural system. Effect of heterogeneity which emerges from weightening synaptic conductivity in neural network on performance of weak signal detection is studied. FitzHugh-Nagumo neuron model with electrical coupling is used as excitable system. In the result of numerical simulations; it is seen that the state of the scale-free network being unweighted or weighted, synaptic conductivity and average connectivity degree play a crucial role for determining performance of information coding of neuron population based on vibrational resonance.

Effects of synaptic reliability on the regularity of neuronal firing

In this study, the regularity of neuronal firing is investigated. We perform independent simulations to find out effects of synaptic transmission reliability in the background activity on the firing regularity of neurons, using Integrate and Fire (IF) neuron model. The obtained results demonstrate that synaptic transmission reliability changes firing regularity influencing background activity intensity. Moreover, it is investigated how the excitatory and inhibitory synaptic connections effect the regularity of neuronal firing.

The role of short-term synaptic depression and adaptive threshold on cortical irregular spike activity

In this study, the biophysical factors underlying the irregular spike patterns produced by neurons in the neocortex, whose cause and function are not fully understood yet, are investigated. In the experimental studies in the literature, it has been proposed that neocortical neurons are subject to high background activity. Thereby, the postsynaptic cortical neuron, used in the study, is modeled as a single-compartment neuron which receives random inputs from a large number of excitatory presynaptic neurons. Furthermore, synaptic transmission lines in the model are designed to include the short-term synaptic depression mechanism. In order to examine the regularity of spike trains in postsynaptic neuron having fix and adaptive threshold, the coefficient of variation of interspikes intervals are computed. The obtained results show that short-term synaptic depression and adaptive threshold mechanisms might be candidate mechanisms explaining the irregular firings in cortical neurons.

Vibrational resonance in feed-forward-loop neuronal network motifs

A neuron carries out its functions in networks receiving contacts from roughly 104 presynaptic neurons. Such a dense connectivity profile for a single neuron may give rise to enormous complex neuronal topologies, which might be very difficult to understand the underlying mechanisms of neurological functions and diseases. Extensive experimental data from neuroanatomical studies have uncovered that neural networks include some recurring topologies of microcircuits, known as network motifs which serve as characteristic building blocks of complex networks [1,2]. Therefore, it is widely assumed that a clear explanation on dynamical and functional features of these network motifs can be considered as the first step to understand large networks. Following this motivation, in this study, we investigate the Vibrational Resonance (VR) phenomenon in a triple-neuron feed-forward-loop (FFL) which is one of the most significant brain network motifs, shown in Figure ​Figure1.1. VR is a physical phenomenon found in nonlinear systems where a weak signal can be detected and processed by the system with the assistance of another high frequency signal. It is very similar to the well-known stochastic resonance phenomenon, where the role of noise is replaced in VR by a high frequency signal. In recent years, there exists a growing interest in applications of VR to neuroscience because two frequency signals are pervasive in neural systems, i.e. bursting neurons exhibit two widely different time scales, simultaneous arrival of vocal signals having distinct frequencies to auditory neurons. Due to the significance of its potential in neural signal processing, the VR phenomenon has already been studied with computational models of neurons and their networks [3,4]. However, a population of excitatory and inhibitory neurons has not been considered, yet. Here, we study the VR dynamics depending on whether the neurons in the FFL motifs are excitatory or inhibitory considering eight possible structural configurations of the considered microcircuit (Table ​(Table1).1). Such an approach provides us to test the functional influences of structural configuration on VR dynamics in neural populations. Figure 1 Schematic illustration of the considered feed-forward-loop motif. LF and HF refers to low and high frequency signals, respectively. Neuron 1 and 3 is considered as input and output neurons, respectively. Table 1 Eight possible FFL types. We found that the weak signal transmission in the network via the VR mechanism is possible depending on both the coupling strength between neurons and the network structure. For instance, when a weak coupling strength is present between connected neurons, weak signal injected to the input neuron can be transmitted to the output with only T8-FFL motif. In this case, signal transmission is impossible for other types of network motifs. For intermediate coupling strengths, signal transmission performance is high for all motifs and motif type does not change very much VR dynamics. Finally, for strong coupling strengths, the best VR performance is obtained with T1-FFL motif where all the neurons in the network are excitatory. We also clarify the mechanisms that underlies the differences in performance of network motifs.