• BMB Reports
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• v.42 no.11
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• pp.705-712
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• 2009

Our understanding of the relationships between genes, brains, and behaviors has changed a lot since the first behavioral mutants were isolated in the fly bottles of the Benzer lab at Caltech (1), but Drosophila is still an excellent model system for studying the neurobiology of behavior. Recent advances provide an unprecedented level of control over fly neural circuits. Efforts are underway to add to existing GAL4-driver lines that permit exogenous expression of genetic tools in small populations of neurons. Combining these driver lines with a variety of inducible UAS lines permits the visualization of neuronal morphology, connectivity, and activity. These driver lines also make it possible to specifically ablate, inhibit, or activate subsets of neurons and assess their roles in the generation of behavioral responses. Here, I will briefly review the extensive arsenal now available to drosophilists for investigating the neuronal control of behavior.

• Korean Journal of Biological Psychiatry
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• v.13 no.3
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• pp.137-143
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• 2006

One of the most widely recognized neurophysiologic endophenotypes for schizophrenia is deficient gating or inhibition of the P50 component of the auditory event-related potential(ERP). A deficit in P50 sensory gating refers to a dysfunction in the mechanism responsible for modulating the brain's sensitivity of filtering out irrelevant or background stimuli, perhaps as a result of dysfunction in inhibitory neural circuits. In this paper, we review the neuronal and genetic aspects as well as medication effects on P50 in schizophrenia.

• BMB Reports
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• v.50 no.3
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• pp.109-110
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• 2017

The nature of encoded information in neural circuits is determined by neuronal firing patterns and frequencies. This paper discusses the molecular identity and cellular mechanisms of spike-frequency adaptation in the central nervous system (CNS). Spike-frequency adaptation in thalamocortical (TC) and CA1 hippocampal neurons is mediated by the $Ca^{2+}$-activated $Cl^-$ channel (CACC) anoctamin-2 (ANO2). Knockdown of ANO2 in these neurons results in increased number of spikes, in conjunction with significantly reduced spike-frequency adaptation. No study has so far demonstrated that CACCs mediate afterhyperpolarization currents, which result in the modulation of neuronal spike patterns in the CNS. Our study therefore proposes a novel role for ANO2 in spike-frequency adaptation and transmission of information in the brain.

• Clinical and Experimental Pediatrics
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• v.48 no.2
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• pp.126-137
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• 2005

New insights in the complex metabolic pathways and its control mechanism for energy homeostasis have refined our understanding of the pathophysiology of obesity. It is now recognized that there are several additional regulatory mechanism such as peripheral signals including leptin, ghrelin, GLP-1 and PYY and cellular signals including uncoupling proteins and ${\beta}$ Adrenergic receptors, which contribute to the regulation of food intake and energy expenditure, respectively. In addition, the function of adipocyte as an endocrine organ in energy homeostasis has been recently emphasized. Recent findings suggest that elevated levels of adipokines, such as leptin, adiponectin, resistin and TNF-${\alpha}$, in addition to increased free fatty acid level could be related to the pathophysiology of insulin resistance in obesity. For effective treatments and prevention of obesity, further studies on the circuits of neural and endocrine interactions in the regulation of energy homeostasis are needed.

• Fisheries and Aquatic Sciences
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• v.10 no.3
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• pp.159-169
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• 2007

The complex 2-dimensional movements of fish during an annual migration circuit were generated and simulated by a chaotic model of fish movement, which was expanded from a small-scale movement model. Fish migration was modeled as a neural network including stimuli, central decision-making, and output responses as variables. The input stimuli included physical stimuli (temperature, salinity, turbidity, flow), biotic factors (prey, predators, life cycle) and landmarks or navigational aids (sun, moon, weather), values of which were all normalized as ratios. By varying the amplitude and period coefficients of the klinokinesis index using chaotic equations, model results (i.e., spatial orientation patterns of migration through time) were represented as fish feeding, spawning, overwintering, and sheltering. Simulations using this model generated 2-dimesional annual movements of sea bream migration in the southern and western seas of the Korean Peninsula. This model of object-oriented and large-scale fish migration produced complicated and sensitive migratory movements by varying both the klinokinesis coefficients (e.g., the amplitude and period of the physiological month) and the angular variables within chaotic equations.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3053-3055
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• 2000

The Cellular Nonlinear Networks structure for Distance Transform (DT) and the robust optical flow detection algorithm based on the DT are proposed. The proposed algorithm is for detecting the optical flows on the trajectories only of the feature points. The translation lengths and the directions of feature movements are detected on the trajectories of feature points on which Distance Transform Field is developed. The robustness caused from the use of the Distance Transform and the easiness of hardware implementation with local analog circuits are the properties of the proposed structure, To verify the performance of the proposed structure and the algorithm, simulation has been done about zooming image.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3060-3062
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• 2000

A fast optimal path planning algorithm using the analog Cellular Nonlinear Circuits (CNC) is proposed. The proposed algorithm compute the optimal path using subgoal-based dynamic programming algorithm. In the algorithm, the optimal paths are computed regardless of the distance between the initial and the goal position. It begins to find subgoals starting from the initial position when the output of the initial cell becomes nonzero value. The suboal is set as the initial position to find the next subgoal until the final goal is reached. Simulations have been done considering the imprecise hardware fabrication and the limitation of the magnitude of input value.

• Anxiety and mood
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• v.7 no.1
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• pp.3-8
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• 2011

Cognitive models of panic disorder have emphasized cognitive distortions' roles in the maintenance and treatment of panic disorder (PD). However, the patient's difficulty with identifying and managing emotional experiences might contribute to an enduring vulnerability to panic attacks. Numerous researchers, employing emotion processing paradigms and neuroimaging techniques, have investigated the empirical evidence for poor emotion processing in PD. For years, researchers considered that abnormal emotion processing in PD might reflect a dysfunction of the frontal-temporal-limbic circuits. Although neuropsychological studies have not provided consistent results regarding this model, a few studies have tried to find the biological basis of dysfunctional emotion processing in PD. In this article, we examine the possibility of dysregulation of emotion processing in PD. Specifically we discuss the neural basis of emotion processing and the manner in which such neurocognitive impairments may help clarify PD's core symptoms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.325-328
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• 2022

A convolutional neural network(CNN) is widely used in the computer vision tasks, but its computing power requirement needs a design of a special circuit. Most of the computations in a CNN can be implemented efficiently in a digital circuit, but the SoftMax layer has operations unsuitable for circuit implementation, which are exponential and logarithmic functions. This paper proposes a new method to integrate the exponential and logarithmic tables of the conventional circuits into a single table. The proposed structure accesses a look-up table (LUT) only with a few maximum values, and the LUT has the result value directly. Our proposed method significantly reduces the space complexity of the SoftMax layer circuit implementation. But our resulting circuit is comparable to the original baseline with small degradation in precision.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1627-1634
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• 2021

This paper introduces a low-power compact ADC circuit for analog Convolutional filter for low-power neural network accelerator SOC. While convolutional neural network accelerators can speed up the learning and inference process, they have drawback of consuming excessive power and occupying large chip area due to large number of multiply-and-accumulate operators when implemented in complex digital circuits. To overcome these drawbacks, we implemented an analog convolutional filter that consists of an analog multiply-and-accumulate arithmetic circuit along with an ADC. This paper is focused on the design optimization of a low-power 8bit SAR ADC for the analog convolutional filter accelerator We demonstrate how to minimize the capacitor-array DAC, an important component of SAR ADC, which is three times smaller than the conventional circuit. The proposed ADC has been fabricated in CMOS 65nm process. It achieves an overall size of 1355.7㎛², power consumption of 2.6㎼ at a frequency of 100MHz, SNDR of 44.19 dB, and ENOB of 7.04bit.