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, 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.Comment: 6 two-column pages, 5 figures; accepted for publication in Europhysics Letter

Chimera states in hybrid coupled neuron populations

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant parameters for their emergence. Additionally, we show that, when the relative population of chemical synapses increases further, a new intriguing chaotic dynamical behavior appears above the region for chimera states. This is characterized by the coexistence of two distinct synchronized states with different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We also discuss about the computational implications of such state. (c) 2020 Elsevier Ltd. All rights reserved.MU acknowledges Bulent Ecevit University Research Foundation, Turkey under Project No. BAP2018-39971044-01. JJT acknowledges the Spanish Ministry for Science and Technology and the "Agencia Espanola de Investigacion, Spain'' (AEI) for financial support under grant FIS2017-84256-P (FEDER funds). AC acknowledges financial support from the Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214/A International Research Fellowship Program, and the hospitality of the Institute Carlos I for Theoretical and Computational Physics at University of Granada

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

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

İleri yönlü biyolojik sinir sinir ağlarında bilgi iletimi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sinir sisteminde bilginin işlenimi farklı nöronal bölgeler tarafından yapılmaktadır. Bu durum, nöronal aktivitenin bir nöron topluluğundan başka bir nöron topluluğuna iletildiğini göstermektedir. Hesaplamalı yaklaşımlar nöronal aktivitenin farklı bilgi işleme birimleri arasındaki propagasyonunun nasıl gerçekleştiğini anlamada etkili araçlar sunmaktadır. Bu bağlamda ileri yönlü sinir ağları modeli sinir sisteminde bilgi iletiminin incelenmesinde basit bir platformdur. Bu çalışma, nöronların biyofiziksel açıdan daha gerçekçi modelleri kullanılarak ileri yönlü ağda nöronal aktivite propagasyonunun daha iyi bir biçimde anlaşılmasını hedeflemektedir. Ağdaki her bir nöron, membran üzerinde gömülü bulunan iyon kanallarının stokastik davranışlarını da içerecek şekilde hücre büyüklüklerine bağlı olarak modellenmiştir. Çalışmada ilk olarak, zayıf periyodik sinyallerin ileri yönlü ağda iletimindeki optimum koşulların belirlenmesi ele alınmıştır. Nöron iç dinamiklerinden kaynaklanan gürültü şiddeti belirli bir seviyenin üzerinde olduğunda, ilk katmana uygulanan zayıf sinyalin sonraki katmanlar tarafından yükseltilerek iletilebildiği tespit edilmiştir. Bunun yanında, sistemdeki gürültü şiddeti ve sinyal frekansı uygun bir biçimde ayarlandığında, katmanlar arasındaki sinaptik bağlantıların olası tüm bağlantı sayısının %4’ ü kadar olmasının etkili bir zayıf sinyal iletimi için yeterli olduğu gösterilmiştir. Çalışmada ayrıca oransal kodlama bağlamında, katmanlarda farklı gürültü rejimleri oluşturularak ateşleme oranlarının ileri yönlü ağdaki propagasyonu araştırılmıştır. Tüm gürültü rejimlerinde, ateşleme oranının ağdaki iletiminin katmanlarda nöronların sergilediği senkronizasyon mekanizması ile gerçekleşebileceği ortaya konmuştur. Sözü edilen mekanizmanın ileri yönlü ağda ortaya çıkabilmesinin giriş katmanı ateşleme oranı seviyesine, ağdaki sinaptik bağlantı yoğunluğuna, sinaptik akım istatistiklerine ve nöron iç dinamiklerinden kaynaklanan gürültü şiddetine bağlı olduğu da gösterilmiştir. Tespit edilen sonuçlar literatürdeki deneysel sonuçlarla tutarlılık göstermekte ve ileri yönlü ağda bilgi işlemenin nasıl gerçekleştiğinin anlaşılmasına katkı sağlamaktadır.Information processing in the nervous system involves multiple stages of neuronal networks, where neuronal activity progress from one sub-population to another. Computational approaches provide useful tools to understand the underlying mechanisms of the activity propagation through multiple processing stages. A feedforward sequence of layers of neurons provides a simple platform for analyzing the propagation of neuronal activity in the nervous system. The present work aims at a better understanding of neuronal activity propagation in Feedforward Networks (FFN) by including a more biophysically realistic model of individual neurons on the network, where the stochastic behavior of voltage-gated ion channels embedded in neuronal membranes is modeled depending on the cell size. First, it is determined under which conditions the propagation of weak periodic signals through a FFN is optimal. It is found 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, as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the FFN, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted. Next, in the context of rate coding, firing rate propagation is studied in FFN by considering the different noise regimes in layers. For all regimes, the stable propagation of input firing rate through the network can be achieved via the synchronization mechanism within the neurons in layers. It is also shown that the development of this mechanism in the network depends on the input rate, interlayerlink density, synaptic current statistics and intrinsic noise intensity in layers. Achieved results are consistent with experimental results, given in the literature, and advance our understanding of how information is processed in FFN

Inverse stochastic resonance induced by synaptic background activity with unreliable synapses

Inverse stochastic resonance (ISR) is a recently pronounced phenomenon that is the minimum occurrence in mean firing rate of a rhythmically firing neuron as noise level varies. Here, by using a realistic modeling approach for the noise, we investigate the ISR with concrete biophysical mechanisms. It is shown that mean firing rate of a single neuron subjected to synaptic bombardment exhibits a minimum as the spike transmission probability varies. We also demonstrate that the occurrence of ISR strongly depends on the synaptic input regime, where it is most prominent in the balanced state of excitatory and inhibitory inputs. © 2013 Elsevier B.V

Firing dynamics in hybrid coupled populations of bistable neurons

We study the firing behavior of bistable neurons that are coupled by both electrical and chemical synapses, constituting a hybrid coupled population. We use stochastic Hodgkin–Huxley model neurons for the dynamics of individual bistable units within the population and the connectivity is implemented with a scale-free network topology which is a widely used network paradigm in theoretical studies of neural circuits. By analyzing the electrical activity of both whole population and individual neurons, it is shown that population mean firing rate exhibits a non-monotonic behavior in the space of electrical and chemical coupling strengths. We identify dynamical mechanisms that shape such non-monotonic spiking behavior and show that different types of population spiking patterns can emerge by fine-tuning the coupling strengths of electrical and chemical synapses. Furthermore, we map the transitions between observed dynamical states in the parameter space of interest depending on the level of individual neuron bistability, existence probability of a synapse type and intrinsic ion channel noise. © 2019 Elsevier B.V

Effects of the network structure and coupling strength on the noise-induced response delay of a neuronal network

The Hodgkin-Huxley (H-H) neuron model driven by stimuli just above threshold shows a noise-induced response delay with respect to time to the first spike for a certain range of noise strengths, an effect called "noise delayed decay" (NDD). We study the response time of a network of coupled H-H neurons, and investigate how the NDD can be affected by the connection topology of the network and the coupling strength. We show that the NDD effect exists for weak and intermediate coupling strengths, whereas it disappears for strong coupling strength regardless of the connection topology. We also show that although the network structure has very little effect on the NDD for a weak coupling strength, the network structure plays a key role for an intermediate coupling strength by decreasing the NDD effect with the increasing number of random shortcuts, and thus provides an additional operating regime, that is absent in the regular network, in which the neurons may also exploit a spike time code. © 2008 Elsevier B.V. All rights reserved