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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
What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons
آنچه الکتروفیزیولوژی در مورد بیماری آلزایمر به ما می گوید: پنجره ای برای هماهنگ سازی و اتصال نورون های مغز-2020
Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer’s disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies.
Keywords: The Alzheimer’s Association International | Society to Advance Alzheimer’s Research | and Treatment (ISTAART) | Alzheimer’s disease (AD) | Electroencephalography and | magnetoencephalography (EEG and MEG) | Resting-state condition | Event-related potentials and magnetic fields | Preclinical and clinical research
Optimal energy management for a grid connected PV-battery system
مدیریت بهینه انرژی برای سیستم باتری PV متصل به شبکه-2020
The increase demand for electricity and the non-renewable nature of fossil energy makes the move towards renewable energies required. However, the common problem of renewable sources, which is the intermittence, is overcome by the hybridization of complementary sources. Thus, whenever the load demand is not fully covered by the primary source, the second one will absolutely support it. Furthermore, the production, the interaction with the grid and the storage system must be managed by the grid-connected hybrid renewable energy system, which is the main objective of this paper. Indeed, we propose a new system of a grid-connected PV-battery, which can manage its energy flows via an optimal management algorithm. The DC bus source connection topology in our proposed hybrid architecture tackles the synchronization issues between sources when the load is powered. We consider in this work that choosing a battery discharge and charge limiting power provides an extension of the battery life. On the other hand, we simulated the dynamic behavior of the architecture’s various components according to their mathematical modeling. Following this, an energy management algorithm was proposed, and simulated using MATLAB/SIMULINK to serve the load. The results have shown that the load was served in all cases, taking into account the electrical behavior of the inhabitants as well as the weather changes on a typical day. Indeed, the load was served either by instant solar production between sunrise and sunset, or the recovery from sunset to 10pm, which could be a stored or injected energy without exceeding the 1000W per hour
Keywords: Renewable energy | PV-battery | Hybrid renewable system | Energy management | Hybrid architecture
Intranasal oxytocin enhances EEG mu rhythm desynchronization during execution and observation of social action: An exploratory study
اکسی توسین داخل رحمی باعث می شود که EEG mu رطوبت زدایی در حین اجرا و مشاهده اقدامات اجتماعی تقویت شود: یک مطالعه اکتشافی-2020
Intranasal administration of oxytocin (OT) has been found to facilitate prosocial behaviors, emotion recognition and cooperation between individuals. Recent electroencephalography (EEG) investigations have reported enhanced mu rhythm (alpha: 8–13 Hz; beta: 15–25 Hz) desynchronization during the observation of biological motion and stimuli probing social synchrony after the administration of intranasal OT. This hormone may therefore target a network of cortical circuits involved in higher cognitive functions, including the mirror neuron system (MNS). Here, in a double-blind, placebo-controlled, between-subjects exploratory study, we investigated whether intranasal OT modulates the cortical activity from sensorimotor areas during the observation and the execution of social and non-social grasping actions. Participants underwent EEG testing after receiving a single dose (24 IU) of either intranasal OT or placebo. Results revealed an enhancement of alpha - but not beta - desynchronization during observation and execution of social grasps, especially over central and parietal electrodes, in participants who received OT (OT group). No differences between the social and non-social condition were found in the control group (CTRL group). Moreover, we found a significant difference over the cortical central-parietal region between the OT and CTRL group only within the social condition. These results suggest a possible action of intranasal OT on sensorimotor circuits involved in social perception and action understanding, which might contribute to facilitate the prosocial effects typically reported by behavioral studies.
Keywords: Oxytocin | ERD | Mirror neuron system | Grasping actions | Electroencephalogram
FPGA Realization of Fractional Order Neuron
تحقق FPGA نورون مرتبه فراکسیون-2020
In this paper fractional Hindmarsh Rose (HR) neuron, which mimics several behaviors of a real biological neuron is implemented on field programmable gate array (FPGA). The re- sults show several differences in the dynamic characteristics of integer and fractional order Hindmarsh Rose neuron models. The integer order model shows only one type of firing characteristics when the parameters of model remains same. The fractional order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied. The firing frequency increases when the order of the frac- tional operator decreases. The fractional order is therefore key in determining the firing characteristics of biological neurons. To implement this neuron model first the digital re- alization of different fractional operator approximations are obtained, then the fractional integrator is used to obtain the low power and low cost hardware realization of fractional HR neuron. The fractional neuron model has been implemented on a low voltage and low power circuit and then compared with its integer counter part. The hardware is used to demonstrate the different dynamical behaviors of fractional HR neuron for different type of approximations obtained for fractional operator in this paper. A coupled network of frac- tional order HR neurons is also implemented. The results also show that synchronization between neurons increases as long as coupling factor keeps on increasing.
Keywords: Computational neuroscience | Fractional Hindmarsh Rose neuron (HR) | Fractional calculus | Fractional-operator | Field-programmable-gate-arrays (FPGA) | Synchronization
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
Chimera States mediated by nonlocally attractive-repulsive coupling in FitzHugh–Nagumo neural networks
حالت Chimera با واسطه زوج جذب-دفع در شبکه های عصبی FitzHugh-Nagumo-2020
The spontaneous occurrence of heterogeneous behaviors in homogeneous systems is an intriguing phenomenon. Recently, a remarkable heterogeneous behavior, called “chimera states”, which consists of spatially coherent and incoherent domains, has been studied in a great variety of systems including physical, chemical, biological, or optical. In this paper, chimera states in FitzHugh–Nagumo (FHN) neural networks are investigated. The identical FHN neurons are assigned in a ring and nonlocally coupled by attractive and repulsive couplings. We show that, the chimera states can be induced by the cooperation of nonlocally attractive and repulsive interactions between these neurons. Moreover, depending on the strength and range of attractive or repulsive couplings, the neural networks display different spatiotemporal behaviors, including chimera states, multi-cluster (MC) chimera states, traveling waves, traveling coherent states, solitary states, bursting synchronizations, and synchronizations. These results suggest that attractive and repulsive couplings may play a crucial role in mediating dynamic behavior of neural networks, and these results could be useful in understanding and predicting the rich dynamics of neural networks.
Keywords: Attractive and repulsive coupling | Neural network | Chimera state
Synchronization of Hindmarsh Rose Neurons
هماهنگ سازی از نورون های Hindmarsh Rose-2020
Modeling and implementation of biological neurons are key to the fundamental understanding of neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal models play a significant role in neural signal processing as it is very difficult to identify the actual interaction between neurons in living brain. Therefore, the synchronization study of these neuronal architectures has received extensive attention from researchers. Higher biological accuracy of these neuronal units demands more computational overhead and requires more hardware resources for implementation. This paper presents a two coupled hardware implementation of Hindmarsh Rose neuron model which is mathematically simpler model and yet mimics several behaviors of a real biological neuron. These neurons are synchronized using an exponential function. The coupled system shows several behaviors depending upon the parameters of HR model and coupling function. An approximation of coupling function is also provided to reduce the hardware cost. Both simulations and a low cost hardware implementations of exponential synaptic coupling function and its approximation are carried out for comparison. Hardware implementation on field programmable gate array (FPGA) of approximated coupling function shows that the coupled network produces different dynamical behaviors with acceptable error. Hardware implementation shows that the approximated coupling function has significantly lower implementation cost. A spiking neural network based on HR neuron is also shown as a practical application of this coupled HR neural networks. The spiking network successfully encodes and decodes a time varying input.
Keywords: Computational neuroscience | Hindmarsh Rose neuron (HR) | Digital | Spiking Neural Networks (SNNs) | Field programmable gate arrays (FPGAs) | Nengo
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