Fig 1 - uploaded by Lijuan Xia
Content may be subject to copyright.
(a) schematic diagram shows the relation between different neural subpopulation in the NMM model, (b) block diagram of the PID controller to suppress the epileptic seizures.
Contexts in source publication
Context 1
... to model a complex neurological phenomena. The NMM is based on a biologically plausible parametrization of the of the layered neocortex dynamic behavior IEEE ICASI 2018 In this model, a cortical sheet is represented by three interacting populations; the main subpopulation, the excitatory subpopulation and the inhibitory subpopulation as shown in Fig. 1. These subpopulations are connected and interacted with each other via a connectivity constants which represents the average synaptic constants. The transfer function of the NMM model is given in (1). í µí°ºí µí°º í µí±í µí±í µí±í µí±í µí±í µí± (í µí± í µí± ) = í µí°ºí µí°º í µí±í µí± (í µí± í µí± ) 1+í µí°ºí µí°º í µí±í µí± ...
Context 2
... the other hand, the fractional orders in case of FPID can be used to compensate for the large values of the components and limit the transfer function parameters as will be shown in the following analysis. From Fig. 1(b), the transfer function of the system in case of detecting a seizure is given by ...
Similar publications
In this paper, a family of explicit methods are presented based on the fractional backward differentiation formula of order three for the numerical solution of the fractional differential equations. The major part of the paper is focused on the study of the stability properties of the introduced methods. The intervals of stability for multistep met...
Citations
... There are various approaches to design stimulation control strategies based on mathematical models of epilepsy. Some researchers attempt to use methods similar to traditional control theory, employing closed-loop control strategies to regulate and achieve control [Chakravarthy et al., 2009;Soltan et al., 2018;Liu et al., 2013;Popovych et al., 2017]. The limitation of this approach lies in the differences between the model and the actual state of the brain. ...
The transiting mechanism of abnormal brain functional activities, such as the epileptic seizures, has not been fully elucidated. In this study, we employ a probabilistic neural network model to investigate the impact of negative regulation, including negative connections and negative inputs, on the dynamical transition behavior of network dynamics. It is observed that negative connections significantly influence the transition behavior of the network, intensifying the oscillation of discharge probability, corresponding to uneven discharge and epileptic states. Negative inputs, within a certain range, exhibited a similar impact on the dynamic state of the network as negative connections, enhancing network oscillations and resulting in higher fragility. However, larger negative inputs can led to the disappearance of oscillations in the discharge probability, indicating a maintenance of lower fragility. We speculate that negative regulation may be an indispensable factor in the occurrence of epileptic seizures, and future research should give it due consideration.
... In [21], a fractional proportional-integralderivative (PID) method was designed to control the abnormal activity of the brain in epilepsy. The study in [22] adopted an unscented Kalman filter (UKF) to estimate the variables of the membrane potential of the Pinsky-Rinzel (PR) model and considered a linear proportion-integration (PI) approach to control firing patterns. The PR model was presented in 1994, which is mentioned in reference [17], and it has been used in various research studies to control seizures. ...
This paper proposes an adaptive barrier function terminal sliding mode control method for partial seizure based on the Pinsky–Rinzel model. A terminal sliding mode control technique is designed to achieve the convergence of trajectories to the desired value in a finite time, while an adaptive barrier function is used to ensure that the outputs, which are independent of the disturbance boundary, converge to the predetermined zero location. The performance of the proposed approach is checked for the nonlinear two-compartmental Pinsky–Rinzel pyramidal neuron model. The obtained method of the finite time stability, in the presence of uncertainty and disturbance, is proven by the Lyapunov theory. The simulation results confirm the effectiveness of the proposed control scheme. Finite time convergence, robustness, chattering-free dynamics and near-zero error are the advantages of the proposed technique.
... The use of neural mass models akin to the Jansen-Rit model in feedback control frameworks is well documented. All the works in (Wang et al., 2016;Xia et al., 2019;Wei et al., 2019c,a,b;Soltan et al., 2018) use neural mass models, in the control theory sense, for the suppression of epileptic seizures. In what follows, we will demonstrate the effectiveness of our proposed control strategy on a seizure simulated by the classical Jansen-Rit neural mass model with standard parameter values. ...
Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people’s quality of life.
... The use of neural mass models akin to the Jansen-Rit model in feedback control frameworks is well documented. All the works in (Wang et al., 2016;Xia et al., 2019;Wei et al., 2019c,a,b;Soltan et al., 2018) use neural mass models, in the control theory sense, for the suppression of epileptic seizures. In what follows, we will demonstrate the effectiveness of our proposed control strategy on a seizure simulated by the classical Jansen-Rit neural mass model with standard parameter values. ...
Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Ultimately, advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people's quality of life.
... Fractional-order systems have found their application in diverse industry branches such as medicine [1,2], modeling and measurement of various signals [3][4][5], agriculture [6], car industry [7], etc. In case of the electrical engineering, the utilization of FO calculus covers circuits filtering the spectrum [8][9][10][11][12][13][14][15][16][17], FO oscillators [18][19][20][21][22][23] and other circuits with fractional-order characteristics [24][25][26][27][28][29], which then can be implemented and find their purpose in applications of above-mentioned industry areas. ...
A design of a fractional-order (FO) integrator is introduced for operation of resulting solution in the current mode (CM). The solution of the integrator is based on the utilization of RC structures, but in comparison to other RC structure based FO designs, the proposed integrator offers the electronic control of the order. Moreover, the control of the proposed integrator does not require multiple specific and accurate values of the control voltages/currents in comparison to the topologies based on the approximation of the FO Laplacian operator. The electronic control of a gain level (gain adjustment) of the proposed integrator is available. The paper offers the results of Cadence IC6 (spectre) simulations and more importantly experimental measurements to support the presented design. The proposed integrator can be used to build various FO circuits as demonstrated by the utilization of the integrator into a structure of a frequency filter in order to provide FO characteristics.
... Investigators in the first category tried to utilize closed-loop algorithms in order to control seizure like activity of the computational models. For example, classical proportional-integral-derivative (PID) algorithm [16,17] or simpler versions such as PD [18,19] and PI [12,20], state feedback method [21], optimal control approach [22,23] and sliding mode controller [24] are used to control seizure-like activity in neural mass models. In practice, the success of model-based closed-loop seizure control approach is highly dependent on the possibility to relate model states to the brain signals, which is in most cases a challenge. ...
High frequency electrical stimulation of brain is commonly used in research experiments and clinical trials as a modern tool for control of epileptic seizures. However, the mechanistic basis by which periodic external stimuli alter the brain state is not well understood. This study provides a computational insight into the mechanism of seizure suppression by high frequency stimulation (HFS). In particular, a modified version of the Jansen-Rit neural mass model is employed, in which EEG signals can be considered as the input. The proposed model reproduces seizure-like activity in the output during the ictal period of the input signal. By applying a control signal to the model, a wide range of stimulation amplitudes and frequencies are systematically explored. Simulation results reveal that HFS can effectively suppress the seizure-like activity. Our results suggest that HFS has the ability of shifting the operating state of neural populations away from a critical condition. Furthermore, a closed-loop control strategy is proposed in this paper. The main objective has been to considerably reduce the control effort needed for blocking abnormal activity of the brain. Such an energy reduction could be of practical importance, to reduce possible side effects and increase battery life for implanted neurostimulators.
... Fractional calculus explores the gap between integerorders in systems models. This improves the controllability for different applications such as control applications [1], biomedical applications [2], chaotic systems [3], and filters [4]. Several definitions for the fractional-order integral /derivative can be used such as Grünwald-Letnikov (GL), Caputo, and Riemann-Liouville (RL). ...
... Saving the values of w binomial coefficients (2) for a certain window length in look-up tables and implement (3) in was used in [11]. The values of w binomial coefficients (2) for a certain window length were saved in look-up tables, then the rest w binomial coefficients were approximated with a linear equation approximation and implement (2) in [12]. The values of w binomial coefficients in (2) for a certain window length were saved, then the rest of w binomial coefficients were approximated with a quadratic equation approximation in [13]. ...
... Advanced PID are also used in the medical sector, [11] proposed a fractional order PID controller and an integer order PID controller for supressing epileptic activities. Both controllers showed great results to stabilize the patient, but the fractional order PID controller is more suitable for implementation in FPGA because it uses less flip-flops. ...
This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller. Keywords: Adaptive Linearization MIT MRAPIDC Nonlinear Parameters Stability This is an open access article under the CC BY-SA license. 1. INTRODUCTION Adaptive control of uncertain processes has become more and more important in industry. Adaptive controllers differ from ordinary ones, because their parameters are variable, and there is a mechanism for adjusting these parameters online based on signals in the system [1]. The design of an adaptive PI controller to stabilize a mass damper-spring system under parameters' uncertainties was proposed in [2]. The designed adaptive PI controller adjusts to parameters' variations, and the output of the process follows the set points, regardless of the values of the parameters. But it does not guarantee stability when external disturbances and large variations of parameters occur. In [3], the design of a PID controller on MATLAB to maintain the level of liquid constant in a coupled-tank system (CTS) was proposed. The control parameters were found using the trial and error methodology and the results were analysed in MATLAB/Simulink environments. Proportional (P), proportional integral (PI), proportional derivative (PD) and proportional integral derivative (PID) controllers were applied on the process and their performances were compared to select the most suitable control solution. The PID controller showed superior results, but it did not guarantee stability to disturbances and variations of plant parameters. Adaptive controllers, as opposed to conventional constant gain controllers (PID controllers), are very effective in handling situations where the variations of parameters and environmental changes are very frequent with the application of model reference adaptive control scheme in a first order system [4].
... Advanced PID are also used in the medical sector, [11] proposed a fractional order PID controller and an integer order PID controller for supressing epileptic activities. Both controllers showed great results to stabilize the patient, but the fractional order PID controller is more suitable for implementation in FPGA because it uses less flip-flops. ...
This paper describes the design of an adaptive controller based on model reference adaptive PID control (MRAPIDC) to stabilize a two-tank process when large variations of parameters and external disturbances affect the closed-loop system. To achieve that, an innovative structure of the adaptive PID controller is defined, an additional PI is designed to make sure that the reference model produces stable output signals and three adaptive gains are included to guarantee stability and robustness of the closed-loop system. Then, the performance of the model reference adaptive PID controller on the behaviour of the closed-loop system is compared to a PI controller designed on MATLAB when both closed-loop systems are under various conditions. The results demonstrate that the MRAPIDC performs significantly better than the conventional PI controller.
... The phase shift algorithm could help us investigate several fundamental neuroscience mechanisms such as neural coherence [41] [42] and synchronization [43] [44]. A standard PID controller has been implemented for control/modification of abnormal activitiesin particular, for epilepsy [45] [46]. The final optical converter stage is determined by the required suppression/activation level as well as opsin expression, optoelectronics design and LED performance. ...
Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.
