https://scholar.dgist.ac.kr/handle/20.500.11750/133 2026-04-04T11:08:52Z https://scholar.dgist.ac.kr/handle/20.500.11750/59380 Title: Short-term memory errors are strongly associated with a drift in neural activity in the posterior parietal cortex Author(s): Choi, Joon Ho; Bae, Sungwon; Park, Jiho; Yoo, Minsu; Kim, Chul Hoon; Schmitt, Lukas Ian; Tchoe, Youngbin; Chung, Dongil; Choi, Ji-Woong; Rah, Jong-Cheol Abstract: Understanding the neural mechanisms behind short-term memory (STM) errors is crucial for unraveling cognitive processes and addressing deficits associated with neuropsychiatric disorders. This study examines whether STM errors arise from misrepresentation of sensory information or decay in these representations over time. Using 2-photon calcium imaging in the posterior parietal cortex (PPC) of mice performing a delayed match-to-sample task, we identified a subset of PPC neurons exhibiting both directional and temporal selectivity. Contrary to the hypothesis that STM errors primarily stem from mis-encoding during the sample phase, our findings reveal that these errors are more strongly associated with a drift in neural activity during the delay period. This drift leads to a gradual divergence away from the correct representation, ultimately leading to incorrect behavioral responses. These results emphasize the importance of maintaining stable neural representations in the PPC for accurate STM. Furthermore, they highlight the potential for therapeutic interventions aimed at stabilizing PPC activity during delay periods as a strategy for mitigating cognitive impairments in conditions like schizophrenia. 2025-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59222 Title: Stable olfactory receptor activation across odor complexity Author(s): Kim, Minseok; Lee, Jeongyoon; Park, Inah; Kim, Jihoon; Lee, Keunsoon; So, Jinhyun; Choi, Ji-Woong; Jang, Jae Eun; Kwon, Hyuk-Jun; Moon, Cheil; Choe, Han Kyoung Abstract: Mechanisms underlying single odorant activation of specific olfactory receptors are well understood. However, how the olfactory system processes complex odor mixtures at the receptor level remains unclear. This study examined olfactory receptor activation patterns across odor complexities using phosphoTRAP analysis. For most mixtures, receptor activation patterns closely matched the linear sum of individual component responses. However, distinct receptor sets display non-linear responses unexplained by linear models. Mixture responses were generally located between component responses and often aligned with linear predictions, though some deviations indicated non-linear interactions. Total activated receptors remained relatively constant regardless of odor complexity, suggesting efficient coding that prevented receptor saturation as odorant components increased. These findings provide receptor-level evidence that the olfactory system encodes complex odors primarily through linear integration of receptor activity, with added specificity from non-linear responses in limited receptors, advancing understanding of how the olfactory system normalizes receptor activation in response to natural odors. 2025-10-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59088 Title: Queue-Aware Optimization-Based Scheduling for mmWave Multi-User MIMO Indoor Small Cell Author(s): Park, Hanyoung; Choi, Ji-Woong Abstract: Millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system is considered for higher quality-of-service of wireless communication and extensive literature has considered user equipment (UE) scheduling framework for it. To ensure high QoS, it is important to consider not only the achievable rate and error rate, which are influenced by multi-user interference, but also the queuing latency. However, extant works have not deeply considered the inherent trade-off between multi-user interference and queuing latency, which is more significant in indoor environments. To handle this issue, we propose Queue-Aware Optimization-based Scheduling (QAOS) and Dynamic Jumping QAOS (DJ-QAOS) algorithm for uplink scheduling in mmWave MU-MIMO systems. The proposed methods select UEs based on Lyapunov optimization framework to handle the trade-off between multi-user interference and queuing latency. © 1997-2012 IEEE. 2025-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58451 Title: An Integrated Network-Computing Load Balancing Simulator for VEC-Assisted Autonomous Vehicles Author(s): Kwak, Jeongho; Chwa, Hoon Sung; Jo, Han-Shin; Kang, Wonyul; Kim, Jeonghwan; Song, Juho; Kim, Jeeyoo; Lee, Seoungjae; Nam, Taesik; Seong, Wonwoo; Choi, Ji-Woong Abstract: Achievement of offloaded analytics services through vehicle edge computing (VEC) requires a comprehensive analysis of in-vehicle processing and network environments. However, existing research on autonomous driving technologies leveraging VEC and related simulation studies remains in its early stages. This article presents the development of an integrated network-computing load (INCL) balancing simulator for autonomous vehicles, which combines a network model and an in-vehicle processing model implemented in MATLAB with a vehicle topology model and realistic driving scenarios created using a virtual test drive (VTD). Moreover, eight real-world autonomous driving scenarios are proposed to validate the simulator's performance, demonstrating its ability to effectively balance network and computational loads under diverse conditions. Finally, using a case study in a platooning driving scenario, we evaluate the simulator's capability to optimize resource utilization, paving the way for advanced autonomous driving technologies. © IEEE. 2025-05-31T15:00:00Z