• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 하계학술대회 논문집 A
• /
• pp.501-503
• /
• 1993

A circuit of sigmoid function for neural network is designed by using Piecewise Linear (PWL) method. The slope of sigmoid function can be adjusted to 2 and 0.25. Also the circuit presents both sigmoid function and its differential form. The circuits is simulated by using ViewLogic. Theoretical and simulated performance agree with 1.8 percent.

• Applied Microscopy
• /
• 제46권4호
• /
• pp.171-175
• /
• 2016

A distinctive neuronal network in the brain is believed to make us unique individuals. Electron microscopy is a valuable tool for examining ultrastructural characteristics of neurons, synapses, and subcellular organelles. A recent technological breakthrough in volume electron microscopy allows large-scale circuit reconstruction of the nervous system with unprecedented detail. Serial-section electron microscopy-previously the domain of specialists-became automated with the advent of innovative systems such as the focused ion beam and serial block-face scanning electron microscopes and the automated tape-collecting ultramicrotome. Further advances in microscopic design and instrumentation are also available, which allow the reconstruction of unprecedentedly large volumes of brain tissue at high speed. The recent introduction of correlative light and electron microscopy will help to identify specific neural circuits associated with behavioral characteristics and revolutionize our understanding of how the brain works.

• Molecules and Cells
• /
• 제40권6호
• /
• pp.379-385
• /
• 2017

Drug addiction is a severe psychiatric disorder characterized by the compulsive pursuit of drugs of abuse despite potential adverse consequences. Although several decades of studies have revealed that psychostimulant use can result in extensive alterations of neural circuits and physiology, no effective therapeutic strategies or medicines for drug addiction currently exist. Changes in neuronal connectivity and regulation occurring after repeated drug exposure contribute to addiction-like behaviors in animal models. Among the involved brain areas, including those of the reward system, the striatum is the major area of convergence for glutamate, GABA, and dopamine transmission, and this brain region potentially determines stereotyped behaviors. Although the physiological consequences of striatal neurons after drug exposure have been relatively well documented, it remains to be clarified how changes in striatal connectivity underlie and modulate the expression of addiction-like behaviors. Understanding how striatal circuits contribute to addiction-like behaviors may lead to the development of strategies that successfully attenuate drug-induced behavioral changes. In this review, we summarize the results of recent studies that have examined striatal circuitry and pathway-specific alterations leading to addiction-like behaviors to provide an updated framework for future investigations.

• The Korean Journal of Physiology and Pharmacology
• /
• 제23권4호
• /
• pp.237-249
• /
• 2019

Confirming the direct link between neural circuit activity and animal behavior has been a principal aim of neuroscience. The genetically encoded calcium indicator (GECI), which binds to calcium ions and emits fluorescence visualizing intracellular calcium concentration, enables detection of in vivo neuronal firing activity. Various GECIs have been developed and can be chosen for diverse purposes. These GECI-based signals can be acquired by several tools including two-photon microscopy and microendoscopy for precise or wide imaging at cellular to synaptic levels. In addition, the images from GECI signals can be analyzed with open source codes including constrained non-negative matrix factorization for endoscopy data (CNMF_E) and miniscope 1-photon-based calcium imaging signal extraction pipeline (MIN1PIPE), and considering parameters of the imaged brain regions (e.g., diameter or shape of soma or the resolution of recorded images), the real-time activity of each cell can be acquired and linked with animal behaviors. As a result, GECI signal analysis can be a powerful tool for revealing the functions of neuronal circuits related to specific behaviors.

• The Korean Journal of Physiology and Pharmacology
• /
• 제17권4호
• /
• pp.321-329
• /
• 2013

Rodents exposed to a 15-min pretest swim in the forced swimming test (FST) exhibit prolonged immobility in a subsequent 5-min test swim, and antidepressant treatment before the test swim reduces immobility. At present, neuronal circuits recruited by antidepressant before the test swim remain unclear, and also less is known about whether antidepressants with different mechanisms of action could influence neural circuits differentially. To reveal the neural circuits associated with antidepressant effect in the FST, we injected desipramine or citalopram 0.5 h, 19 h, and 23 h after the pretest swim and observed changes in c-Fos expression in rats before the test swim, namely 24 h after the pretest swim. Desipramine treatment alone in the absence of pretest swim was without effect, whereas citalopram treatment alone significantly increased the number of c-Fos-like immunoreactive cells in the central nucleus of the amygdala and bed nucleus of the stria terminalis, where this pattern of increase appears to be maintained after the pretest swim. Both desipramine and citalopram treatment after the pretest swim significantly increased the number of c-Fos-like immunoreactive cells in the ventral lateral septum and ventrolateral periaqueductal gray before the test swim. These results suggest that citalopram may affect c-Fos expression in the central nucleus of the amygdala and bed nucleus of the stria terminalis distinctively and raise the possibility that upregulation of c-Fos in the ventral lateral septum and ventrolateral periaqueductal gray before the test swim may be one of the probable common mechanisms underlying antidepressant effect in the FST.

• 전자공학회논문지B
• /
• 제33B권3호
• /
• pp.186-192
• /
• 1996

A modified learning rule is introduced for the implementation of feedforward artificial neural networks with on-chip learning circuitry using standard analog CMOS technology. Learning rule, is modified form the EBP (error back propagation) rule which is one of the well-known learning rules for the feedforward rtificial neural nets(FANNs). The employed MEBP ( modified EBP) rule is well - suited for the hardware implementation of FANNs with on-chip learning rule. As a ynapse circuit, a four-quadrant vector-product linear multiplier is employed, whose input/output signals are given with voltage units. Two $2{\times}2{\times}1$ FANNs are implemented with the learning circuitry. The implemented FANN circuits have been simulatied with learning test patterns using the PSPICE circuit simulator and their results show correct learning functions.

• 전자통신동향분석
• /
• 제35권3호
• /
• pp.76-84
• /
• 2020

Neuromorphic hardware refers to brain-inspired computers or components that model an artificial neural network comprising densely connected parallel neurons and synapses. The major element in the widespread deployment of neural networks in embedded devices are efficient architecture for neuromorphic hardware with regard to performance, power consumption, and chip area. Spiking neural networks (SiNNs) are brain-inspired in which the communication among neurons is modeled in the form of spikes. Owing to brainlike operating modes, SNNs can be power efficient. However, issues still exist with research and actual application of SNNs. In this issue, we focus on the technology development cases and market trends of two typical tracks, which are listed above, from the point of view of artificial intelligence neuromorphic circuits and subsequently describe their future development prospects.

• 생물정신의학
• /
• 제4권1호
• /
• pp.19-23
• /
• 1997

Attention is a phenomenon hard to define, but can be conceptualized as a mental function ranging from sustaining readiness to perceive stimuli to understanding the nature and value and selecting stimuli that are most relevant to the given situation. Manifestations of attention include vigilance, and focused, directed, selective, divided, and sustained attentions. While basic attentional tone is controlled by the interaction among reticular activating system, thalamus and prefrontal cortex, direction and selection of attention is controlled by neural circuits of prefrontal, posterior parietal, and limbic cortex. It is expected that understanding of attention and its neural control could provide answers to the relationship between pathophysiology and clinical symptoms of some major psychiatric disorders. More efforts are required to develop tools to assess more detailed and various aspects of attention in Korea.

• ETRI Journal
• /
• 제26권4호
• /
• pp.297-306
• /
• 2004

A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma-etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high-prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2005년도 추계종합학술대회
• /
• pp.303-306
• /
• 2005

High aspect ration via-hole etching process has emerged as one of the most crucial means to increase component density for ULSI devices. Because of charge accumulation in via hole, this sophisticated and important process still hold several problems, such as etching stop, loading effects during fabrication of integrated circuits. Indeed, the concern actually depends on accumulated charge. For monitoring accumulated charge during plasma etching process, charge-up monitoring sensor was fabricated and tested under some plasma conditions. This paper presents a neural network-based technique for analyzing and modeling several electrical performance of plasma charge-up monitoring sensor.