• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.421-424
• /
• 2003

This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.

• Applied Microscopy
• /
• v.41 no.4
• /
• pp.249-255
• /
• 2011

Neurons utilize a large quantity of energy for their survival and function, and thereby require active mitochondrial function. Mitochondrial morphology shows dynamic changes, depending on the cellular condition, and mitochondrial dynamics are required for neuronal development and function. In this study, we found that the length of mitochondria in the distal axon is significantly shorter than that of mitochondria in dendrites or proximal axons of cerebral cortical neurons, and the reason for this difference is the local fission within the axon. We also found that suppression of Drp1, a key regulator of mitochondrial fission, resulted in significant elongation of mitochondria in axons. Collectively, these results suggest that local mitochondrial fission within the axon contributes to region-dependent mitochondrial length differences in the axons of cortical neurons.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.1
• /
• pp.32-39
• /
• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• Molecules and Cells
• /
• v.45 no.11
• /
• pp.855-867
• /
• 2022

For proper function of proteins, their subcellular localization needs to be monitored and regulated in response to the changes in cellular demands. In this regard, dysregulation in the nucleocytoplasmic transport (NCT) of proteins is closely associated with the pathogenesis of various neurodegenerative diseases. However, it remains unclear whether there exists an intrinsic regulatory pathway(s) that controls NCT of proteins either in a commonly shared manner or in a target-selectively different manner. To dissect between these possibilities, in the current study, we investigated the molecular mechanism regulating NCT of truncated ataxin-3 (ATXN3) proteins of which genetic mutation leads to a type of polyglutamine (polyQ) diseases, in comparison with that of TDP-43. In Drosophila dendritic arborization (da) neurons, we observed dynamic changes in the subcellular localization of truncated ATXN3 proteins between the nucleus and the cytosol during development. Moreover, ectopic neuronal toxicity was induced by truncated ATXN3 proteins upon their nuclear accumulation. Consistent with a previous study showing intracellular calcium-dependent NCT of TDP-43, NCT of ATXN3 was also regulated by intracellular calcium level and involves Importin α3 (Imp α3). Interestingly, NCT of ATXN3, but not TDP-43, was primarily mediated by CBP. We further showed that acetyltransferase activity of CBP is important for NCT of ATXN3, which may acetylate Imp α3 to regulate NCT of ATXN3. These findings demonstrate that CBP-dependent acetylation of Imp α3 is crucial for intracellular calcium-dependent NCT of ATXN3 proteins, different from that of TDP-43, in Drosophila neurons.

• The Korean Journal of Physiology and Pharmacology
• /
• v.3 no.4
• /
• pp.365-373
• /
• 1999

Somatostatin (SOM) is one of the major neuropeptides in dorsal root ganglion cells, but its role in spinal nociceptive process has not been well known. In present study we aimed to investigate the effect of SOM on the response of dorsal horn neurons to the various types of peripheral nociceptive stimuli in anesthetized cats. Using carbon-filament microelectrode, the single cell activities of wide dynamic range neurons were recorded from the lumbosacral enlargement after noxious mechanical (squeeze), thermal (radiant heat lamp) and cold (dry ice) stimulation to the receptive field. Sciatic nerve was stimulated electrically to evoke $A\;{\delta}-$ and C-nociceptive responses. SOM analogue, octreotide $(10\;{\mu}g/kg),$ was applied intravenously and the results were compared with those of morphine (2 mg/kg, MOR). Systemic SOM decreased the cellular responses to the noxious heat and the mechanical stimulation, but increased those to the cold stimulation. In the responses to the electric stimuli of sciatic nerve, $A\;{\delta}-nociceptive$ response was increased by SOM, while C-nociceptive response was decreased. On the other hand, MOR inhibited the dorsal horn cell responses to all the noxious stimuli. From the above results, it is concluded that SOM suppresses the transmission of nociceptive heat and mechanical stimuli, especially via C-fiber, while it facilitates those of nociceptive cold stimuli via $A\;{\delta}-fiber$.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1997.04a
• /
• pp.125-130
• /
• 1997

In this paper, it is presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C3x is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, andsuitable for implementation of robust control.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.10
• /
• pp.1231-1238
• /
• 1999

This paper presents an approach for the optimal heating load Identification using Diagonal Recurrent Neural Networks(DRNN). In this paper, the DRNN captures the dynamic nature of a system and since it is not fully connected, training is much faster than a fully connected recurrent neural network. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself. In this study, A dynamic backpropagation (DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. Delta-bar-delta learning method is an empirical method to adapt the learning rate gradually during the training period in order to improve accuracy in a short time. The simulation results based on experimental data show that the proposed model is superior to the other methods in most cases, in regard of not only learning speed but also identification accuracy.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.5
• /
• pp.60-66
• /
• 1997

This paper presents a fuzzy inference-based reinforcement learning algorithm of dynamci recurrent neural networks, which is very similar to the psychological learning method of higher animals. By useing the fuzzy inference technique the linguistic and concetional expressions have an effect on the controller's action indirectly, which is shown in human's behavior. The intervlas of fuzzy membership functions are found optimally by genetic algorithms. And using recurrent neural networks composed of dynamic neurons as action-generation networks, past state as well as current state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.939-942
• /
• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.

• Journal of Life Science
• /
• v.16 no.6
• /
• pp.955-959
• /
• 2006

A dynamic cycle of addition and removal of O-linked N-acetylglucosamine (O-GlcNAc) at serine and threonine residues is emerging as a key regulator of nuclear and cytoplasmic protein activity. In this work, immunocytochemistry was carried out to investigate the subcellular expression of GlcNAc kinase (NAGK, EC 2.7.1.59) that catalyzes the phosphorylation of GlcNAc to GlcNAc 6-phosphate. Immunostainings of cultured rat hippocampal neurons revealed patchy or punctate distribution of NAGK. When NAGK is doublestained with caveolin-1 or flotillin, markers for caveolae and lipid rafts, respectively, NAGK was co-localized with these markers. These results indicate that most, if not all, of the NAGK immunopunctae represent caveolae and lipid rafts, and suggest NAGK's role in these membrane microdomains.