Increasing top-down suppression from prefrontal cortex facilitates tactile working memory

Navigated transcranial magnetic stimulation (TMS) combined with diffusion-weighted magnetic resonance imaging (DW-MRI) and tractography allows investigating functional anatomy of the human brain with high precision. Here we demonstrate that working memory (WM) processing of tactile temporal information is facilitated by delivering a single TMS pulse to the middle frontal gyrus (MFG) during memory maintenance. Facilitation was obtained only with a TMS pulse applied to a location of the MFG with anatomical connectivity to the primary somatosensory cortex (S1). TMS improved tactile WM also when distractive tactile stimuli interfered with memory maintenance. Moreover, TMS to the same MFG site attenuated somatosensory evoked responses (SEPs). The results suggest that the TMS-induced memory improvement is explained by increased top-down suppression of interfering sensory processing in S1 via the MFG-S1 link. These results demonstrate an anatomical and functional network that is involved in maintenance of tactile temporal WM. (C) 2009 Elsevier Inc. All rights reserved.

赤腹松鼠一新亚种

查对中国南部赤腹松鼠标本(40l号)的基础上，发现分布于云南东北部昭通地区的赤腹松鼠与赤腹松 鼠其他亚种有明显的区别。毛色特征：背部棕黄色，背中央区域稍带黑色；腹部至前胸栗红色；喉、颏部棕灰色； 尾毛背腹无明显差异，尾毛末端棕黄、次末端黑，尾末端具棕黄色(稍黄白)区域；前后足背棕褐色，稍带黑色。 进一步对头骨可量性状数据进行分析(差异系数)，结果表明：分布于该地区的赤腹松鼠分别与赤腹松鼠其他13个 亚种两两之间至少有一项差异系数大于1．28，系一新亚种c蹴f眦i珏r猫e唧如me谢z，啪t撕g啪s泌s妇p．nov．。

Multiplexed model predictive control

This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The Multiplexed MPC (MMPC) scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem. This paper describes nominal and robust MMPC, states some stability results, and demonstrates the effectiveness of MMPC through two examples. © 2011 Elsevier Ltd. All rights reserved.