Some neurons (delay cells) in the prefrontal cortex elevate their activities throughout the time period during which the animal is required to remember past events and prepare future behavior, suggesting that working memory is mediated by continuous neural activity. It is unknown, however, how working memory is represented within a population of prefrontal cortical neurons. We recorded from neuronal ensembles in the prefrontal cortex as rats learned a new delayed alternation task. Ensemble activities changed in parallel with behavioral learning so that they increasingly allowed correct decoding of previous and future goal choices. In well-trained rats, considerable decoding was possible based on only a few neurons and after removing continuously active delay cells. These results show that neural activity in the prefrontal cortex changes dynamically during new task learning so that working memory is robustly represented and that working memory can be mediated by sequential activation of different neural populations.

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In our study, the dielectric properties of SiOC low k thin film derived from polyphenylcarbosilane were investigated as a potential interlayer dielectrics for Cu interconnect technology. A SiOC low k thin film was fabricated onto a n-type silicon wafer by dip coating using 30wt % polyphenylcarbosilane in cyclohexane. Curing of the film was performed in air at 300°C for 2h. The thickness of the film ranges from 1 μm to 1.7 μm. The dielectric constant was determined from the capacitance data obtained from metal/polyphenylcarbosilane/conductive Si MIM capacitors and shows a dielectric constant as low as 3.26 without porosity added. The SiOC low k thin film derived from polyphenylcarbosilane shows promising application as an interlayer dielectrics for Cu interconnect technology.

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VR (Virtual Reality) system have been widely used for various purposes. However, during people's immersing in a virtual environment it is commonly reported that simulation sickness can occur, and it prevents us from utilizing a VR environment for the wider purposes.We constructed controlled a VR environment for analyzing the change of bio-signals during VR immersion, where subjects were requested to find trash cans in the virtual environment within five minutes. Each subject's various bio-signals, which were EEGs from 5 different locations, vertical EOG, Lead I ECG, fingertip skin temperature, photoplethysmogram, and skin conductance level, were measured during experiments. We analyzed and compared the signals, and we found out that the characteristics of 28 signals during nausea were statistically different from when the subjects were at rest, or during the first 30 seconds after the immersion was started. We parameterized these characteristics and established 12 principal components using principal component analysis in order to reduce the redundancy in those parameters, and constructed an artificial neural network with those principal components. Using the network we constructed, we could partially detect nausea in real time.

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