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نتیجه جستجو - Izhikevich neuron

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 A closed-loop BMI system design based on the improved SJIT model and the network of Izhikevich neurons
طراحی سیستم BMI حلقه بسته بر اساس مدل SJIT بهبود یافته و شبکه نورون های Izhikevich-2020
Brain–machine interface (BMI) is a useful technology which creates a new way for disable people to communicate with the world, but experimenting with human brains is risky. Hence, a precise mathemat- ical model of the information transmission in the process of limb movement is necessary to be estab- lished. In this paper, firstly, we improve the classical single-joint information transmission (SJIT) model through introducing several neuron models, and the improved model is closer to the true single-joint movements. Secondly, a closed-loop system with a Wiener filter-based decoder, an auxiliary controller based on model predictive control (MPC) and a network of Izhikevich neurons is formulated based on the improved model, and the used network of Izhikevich neurons is more time efficient than the existing one. Finally, in this closed-loop system, the intracortical micro-stimulation (ICMS) technology is intro- duced to feedback the information from the MPC controller in real time. The auxiliary controller assist the brain to control artificial arm by changing the frequency of stimulation current. In this way, the com- putational complexity of the optimization problem proposed in this paper is greatly reduced, and the closed-loop BMI system designed in this paper can well track the desired trajectory.
Keywords: Brain–machine interface | Model improvement | Model predictive control | Closed-loop system design | Intracortical micro-stimulation
مقاله انگلیسی
2 Interaction of neuronal and network mechanisms on firing propagation in a feedforward network
تعامل مکانیسم های عصبی و شبکه بر انتشار سریع در یک شبکه feedforward-2020
The mammalian brain has enormously complex neuronal diversity and a highly modular structure. The propagation of information in the modular brain network can be modeled by a feedforward network (FFN). Although studies in this area have yielded many important results, neuronal diversity has rarely been considered. In the current work, we investigate the complex interactions between the intrinsic properties of neurons and the FFN structure in the propagation of spiking activity. Here, four typical types of cortical neurons reproduced by the Izhikevich neuron model are introduced. A homogeneous FFN composed of a single type of excitatory neuron (regular spiking, mixed model, or tonic bursting) can propagate spiking activity. However, an FFN with fast spiking neurons does not propagate spiking activity. By modifying the network structure and synaptic weights, the spiking propagation of the homogeneous FFNs can vary from synchronous transmission (with a high firing rate) to asynchronous transmission (with a low firing rate). Among the homogeneous FFNs, both the firing rate and the synchrony of the FFN with tonic bursting neurons are the highest, but those of the FFN with regular spiking neurons is lowest, even when implementing the same FFN structure. For the FFN with mixed neuronal types, interestingly, the spiking propagation is very sensitive to the composition of the four types of neurons. By introducing fast spiking neurons into the homogeneous FFN composed of excitatory neurons, spiking propagation can be modified from synchronous to asynchronous. Similarly, changing the proportion of any of the types of neuron affects the spiking propagation, even for very small changes. The underlying mechanism of these observed results has also been discussed.
Keywords: Feedforward network | Neuronal diversity | Spiking propagation | Izhikevich neuron
مقاله انگلیسی
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بازدید امروز: 4267 :::::::: بازدید دیروز: 3097 :::::::: بازدید کل: 38534 :::::::: افراد آنلاین: 50