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Explosive, continuous and frustrated synchronization transition in spiking Hodgkin–Huxley neural networks: The role of topology and synaptic interaction
انتقال همزمان ، انفجاری ، مداوم و ناامید کننده در شبکه های عصبی هوچکین-هاکسلی اسپایک: نقش توپولوژی و تعامل سیناپسی-2020
Synchronization is an important collective phenomenon in interacting oscillatory agents. Many functional features of the brain are related to synchronization of neurons. The type of synchronization transition that may occur (explosive vs. continuous) has been the focus of intense attention in recent years, mostly in the context of phase oscillator models for which collective behavior is independent of the mean-value of natural frequency. However, synchronization properties of biologically-motivated neural models depend on the firing frequencies. In this study we report a systematic study of gammaband synchronization in spiking Hodgkin–Huxley neurons which interact via electrical or chemical synapses. We use various network models in order to define the connectivity matrix. We find that the underlying mechanisms and types of synchronization transitions in gamma-band differs from beta-band. In gamma-band, network regularity suppresses transition while randomness promotes a continuous transition. Heterogeneity in the underlying topology does not lead to any change in the order of transition, however, correlation between number of synapses and frequency of a neuron will lead to explosive synchronization in heterogeneous networks with electrical synapses. Furthermore, small-world networks modeling a fine balance between clustering and randomness (as in the cortex), lead to explosive synchronization with electrical synapses, but a smooth transition in the case of chemical synapses. We also find that hierarchical modular networks, such as the connectome, lead to frustrated transitions. We explain our results based on various properties of the network, paying particular attention to the competition between clustering and long-range synapses.
Keywords: Synchronization | Hodgkin–Huxley neuron | Phase transition | Electrical and chemical synapses | Complex networks
Channel noise effects on neural synchronization
تأثیر نویز کانال بر هماهنگی عصبی-2020
Synchronization in neural networks is believed to be linked to cognitive processes, while abnormal synchronization has been associated with disorders such as epilepsy and schizophrenia. We examine the synchronization of small Hodgkin–Huxley neuronal networks. The principal features of Hodgkin–Huxley neurons are protein channels in the neural membrane that transition between open and closed states with voltage dependent rate constants. The standard assumption of infinitely many channels neglects the fact that real neurons have finitely many channels, which leads to fluctuations in the membrane voltage and modifies neuronal spike times. These fluctuations are referred to as channel noise. We demonstrate that regardless of channel noise magnitude, neurons in the network reach a steady state synchronization level dependent only on the number of neurons in the network, equivalent to the steady state level of uncoupled Poisson neurons. The channel noise only affects the time to reach the steady state synchronization level.
Keywords: Synchronization | Hodgkin–Huxley | Channel noise | Neural network
A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1
یک کلاس متمایز از پشت سر گذاشتن نورون ها با همگام سازی گاما قوی و انتخاب محرک در میمون V1-2020
Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron types to sensory processing and network synchronization in primate visual cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell type (ii30% of neurons) that has narrow-waveform (NW) action potentials and high spontaneous discharge rates and fires in high-frequency bursts. These neurons are more stimulus selective and phase locked to 30- to 80-Hz gamma oscillations than other neuron types. Unlike other neuron types, their gamma-phase locking is highly predictive of orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. We did not find a similar class of NW bursting neurons in L2-L4 of mouse V1. Given its properties, this class of NW bursting neurons should be pivotal for the encoding and transmission of stimulus information.
Quasi-pinning synchronization and stabilization of fractional order BAM neural networks with delays and discontinuous neuron activations
هماهنگ سازی شبه پین و تثبیت شبکه های عصبی BAM مرتبه کسری با تاخیر و فعال سازی نورون ناپیوسته-2020
This manuscript concerns quasi-pinning synchronization and β-exponential pinning stabilization for a class of fractional order BAM neural networks with time-varying delays and discontinuous neuron acti- vations (FBAMNNDDAs). Firstly, under the framework of Filippov solution and fractional-order differential inclusions analysis for the initial value problem of FBAMNNDDAs is presented. Secondly, two kinds of novel pinning controllers according to pinning control technique are designed. By means of fractional or- der Lyapunov method and designed pinning control strategy, the sufficient criteria is given first to ensure the quasi-synchronization for the dynamic behavior of FBAMNNDDAs. Furthermore, the error bound of pinning synchronization is explicitly evaluated. Thirdly, via Kakutani s fixed point theorem of set-valued map analysis, Razumikhin condition, and a nonlinear pinning controller, the existence and β-exponential stabilization of FBAMNNDDAs equilibrium point is obtained in the voice of linear matrix inequality (LMI) technique. Fourthly, based on as well as Mittag-Leffler function and growth condition, the global existence of a solution in the Filippov sense of such system is guaranteed with detailed proof. At last, a numerical example with computer simulations are performed to illustrate the effectiveness of proposed theoretical consequences.
Keywords: Quasi-synchronization | β-Exponential stabilization | Discontinuous BAM-type neural networks | Fractional order | Time-varying delays | Filippov’s solutions | Pinning control
Astrocyte-induced intermittent synchronization of neurons in a minimal network
هماهنگ سازی متناوب نورون ها در یک شبکه کمینه با آستروسیت ها-2020
We investigate the impact of mixed coupling on synchronization in a minimal multiplex neuro-astrocytal ensemble, inspired by physiological systems. We find that calcium activity in astrocytes can effectively mediate neural interactions and control cooperative dynamics of neurons. In particular, astrocytes can in- duce the intermittent synchronization of a pair synaptically coupled fast spiking Hodgkin-Huxley neurons on the slow timescale of calcium oscillations.
Keywords: Synchronization | Multiple timescales | Neuron-astrocyte network | Hodgkin-Huxley neuron | Ullah-Jung astrocyte
Modeling dopaminergic modulation of clustered gamma rhythms
مدل سازی مدولاسیون دوپامینرژیک ریتم گاما خوشه ای-2020
Gamma rhythm (20–100 Hz) plays a key role in numerous cognitive tasks: working mem- ory, sensory processing and in routing of information across neural circuits. In compar- ison with lower frequency oscillations in the brain, gamma-rhythm associated firing of the individual neurons is sparse and the activity is locally distributed in the cortex. Such “weak”gamma rhythm results from synchronous firing of pyramidal neurons in an inter- play with the local inhibitory interneurons in a "pyramidal-interneuron gamma" or PING. Experimental evidence shows that individual pyramidal neurons during such oscillations tend to fire at rates below gamma, with the population showing clear gamma oscillations and synchrony. One possible way to describe such features is that this gamma oscilla- tion is generated within local synchronous neuronal clusters. The number of such syn- chronous clusters defines the overall coherence of the rhythm and its spatial structure. The number of clusters in turn depends on the properties of the synaptic coupling and the intrinsic properties of the constituent neurons. We previously showed that a slow spike frequency adaptation current in the pyramidal neurons can effectively control cluster numbers. These slow adaptation currents are modulated by endogenous brain neuromod- ulators such as dopamine, whose level is in turn related to cognitive task requirements. Hence we postulate that dopaminergic modulation can effectively control the clustering of weak gamma and its coherence. In this paper we study how dopaminergic modulation of the network and cell properties impacts the cluster formation process in a PING network model.
Keywords: Gamma oscillations | Spike frequency adaptation | Cluster syncronization | Dopamine modulation | Multiple timer scales
Long-range memory effects in a magnetized Hindmarsh-Rose neural network
اثرات حافظه با برد طولانی در یک شبکه عصبی مغناطیسی Hindmarsh-Rose-2020
We consider a model network of diffusively coupled Hindmarsh-Rose neurons to study both analytically and numerically, long-range memory effects on the modulational instabil- ity phenomenon, chaotic, synchronous and chimera states within the network. The multi- ple scale method is used to reduce the generic model into a discrete nonlinear Schrödinger equation. The latter is explored in the linear stability analysis and the instability criterion along with the critical amplitude are derived. The analytical results predict that strong lo- cal coupling, high electromagnetic induction and strong long-range interactions may sup- port the formation of highly localized excitations in neural networks. Through numeri- cal simulations, the largest Lyapunov exponents are computed for studying chaos, the synchronization factor and the strong of incoherence are recorded for studying, respec- tively synchronous and chimera states in the network. We find the appropriate domains of space parameters where these rich activities could be observed. As a result, quasi-periodic synchronous patterns, chaotic chimera and synchronous states, strange chaotic and non- chaotic attractors are found to be the main features of membrane potential coupled with memristive current during long-range memory activities of neural networks. Our results suggest that a combination of long-range activity and memory effects in neural networks may produces a rich variety of membrane potential patterns which are involved in infor- mation processing, odors recognition and discrimination and various diseases in the brain.
Keywords: Modulational instability | Chaos | Synchronization | Chimera states
Synchronization of boundary coupled Hindmarsh–Rose neuron network
هماهنگ سازی شبکه سیار نورون Hindmarsh-Rose سیار -2020
In this work, we present a new mathematical model of a boundary coupled neuron network described by the partly diffusive Hindmarsh–Rose equations. We prove the global absorbing property of the solution semiflow and then the main result on the asymptotic synchronization of this neuron network at a uniform exponential rate provided that the boundary coupling strength and the stimulating signal exceed a quantified threshold in terms of the parameters.
Keywords: Neuron network | Synchronization with boundary | coupling | Hindmarsh–Rose equations | Absorbing semiflow
Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce
داده های بزرگ : آموزش و راهنمایی در مورد اطلاعات و همگام سازی فرایند برای الگوریتم های تحلیلی با MapReduce-2018
We live in a world were data are generated from a myriad of sources, and it is really cheap to collect and storage such data. However, the real benefit is not related to the data itself, but with the algorithms that are capable of processing such data in a tolerable elapse time, and to extract valuable knowledge from it. Therefore, the use of Big Data Analytics tools provide very significant advantages to both industry and academia. The MapReduce programming framework can be stressed as the main paradigm related with such tools. It is mainly identified by carrying out a distributed execution for the sake of providing a high degree of scalability, together with a fault tolerant scheme. In every MapReduce algorithm, first local models are learned with a subset of the original data within the so called Map tasks. Then, the Reduce task is devoted to fuse the partial outputs generated by each Map. The ways of designing such fusion of information/models may have a strong impact in the quality of the final system. In this work, we will enumerate and analyze two alternative methodologies that may be found both in the spe cialized literature and in standard Machine Learning libraries for Big Data. Our main objective is to provide an introduction of the characteristics of these methodologies, as well as giving some guidelines for the design of novel algorithms in this field of research. Finally, a short experimental study will allow us to contrast the scalability issues for each type of process fusion in MapReduce for Big Data Analytics.
Keywords: Big Data Analytics ، MapReduce ، Information fusion ، Spark ، Machine learning
Privacy-preserving fusion of IoT and big data for e-health
همگام سازی حفظ حریم شخصی اینترنت اشیا و داده های بزرگ برای سلامتی الکترونیکی-2018
In this paper, we propose a privacy-preserving e-health system, which is a fusion of Internet-of-things (IoT), big data and cloud storage. The medical IoT network monitors patient’s physiological data, which are aggregated to electronic health record (EHR). The medical big data that contains a large amount of EHRs are outsourced to cloud platform. In the proposed system, the patient distributes an IoT group key to the medical nodes in an authenticated way without interaction round. The IoT messages are encrypted using the IoT group key and transmitted to the patient, which can be batch authenticated by the patient. The encrypted EHRs are shared among patient and different data users in a fine-grained access control manner. A novel keyword match based policy update mechanism is designed to enable flexible access policy updating without privacy leakage. Extensive comparison and simulation results demonstrate that the algorithms in the proposed system are efficient. Comprehensive analysis is provided to prove its security.
Keywords: Internet-of-things ، Big data ، Privacy-preserving ، Cloud storage ، Access control ، Keyword match based policy update