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arXiv 2024年

作者： Yu, Yifei Wang, Shaocong Zhang, Woyu Zhang, Xinyuan Wu, Xiuzhe He, Yangu Yang, Jichang Zhang, Yue Lin, Ning Wang, Bo Chen, Xi Wang, Songqi Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Hong Kong Institute of Mind The University of Hong Kong Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, traditional signal reconstruction methods on digital computers face both software and hardware challenges. On the software front, difficulties arise from storage inefficiencies in conventional explicit signal representation. Hardware obstacles include the von Neumann bottleneck, which limits data transfer between the CPU and memory, and the limitations of CMOS circuits in supporting parallel processing. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. Software-wise, we employ neural field to implicitly represent signals via neural networks, which is further compressed using low-rank decomposition and structured pruning. Hardware-wise, we design a resistive memory-based computing-in-memory (CIM) platform, featuring a Gaussian Encoder (GE) and an MLP Processing Engine (PE). The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit. We demonstrate the system’s efficacy on a 40nm 256Kb resistive memory-based in-memory computing macro, achieving 31.5×, 35.5×, and 47.2× energy efficiency improvements and 10.8×, 38.8×, and 6.2× parallelism improvements without compromising reconstruction quality in tasks like 3D CT sparse reconstruction, novel view synthesis, and novel view synthesis for dynamic scenes. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal reconstruction

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Dynamic neural network with memristive CIM and CAM for 2D and 3D vision

arXiv

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arXiv 2024年

作者： Zhang, Yue Zhang, Woyu Wang, Shaocong Lin, Ning Yu, Yifei He, Yangu Wang, Bo Jiang, Hao Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Zhang, Xumeng Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100049 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Institute of the Mind The University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based Computing-In-Memory (CIM) and Content-Addressable Memory (CAM) circuits, respectively. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets, which not only achieves accuracy on par with software but also a 48.1% and 15.9% reduction in computational budget. Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption. © 2024, CC BY.

关键词： Memristors

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P-66: Simulation and Optimization of In-Situ Integration Ultraviolet HEMT-LED Enabled by P-GaN Selective Epitaxy Growth

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SID Symposium Digest of Technical Papers 2023年第1期54卷

作者： Jingyang Zhang Wei Huang Zhaojun Liu Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen Guangdong China 518055 A State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai China 200433

Based on previous work, the ultraviolet HEMT-LED we fabricated by selective epitaxy growth, this paper further performed simulation to explore the most appropriate p-GaN selection size. P-GaN grown by Selective Epitaxy Growth (SEG) can not only be employed as a junction termination to reduce the electric field crowding of the gate of HEMT, but also form an active region by contacting with 2-dimentional electron gas (2DEG). The simulation results provide an effective reference for the design of p-GaN grown by SEG, and further pave the way for the realization of the idea of integration through the selection of growth methods.

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Echo state graph neural networks with analogue random resistor arrays

arXiv

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arXiv 2021年

作者： Wang, Shaocong Li, Yi Wang, Dingchen Zhang, Woyu Chen, Xi Dong, Danian Wang, Songqi Zhang, Xumeng Lin, Peng Gallicchio, Claudio Xu, Xiaoxin Liu, Qi Cheng, Kwang-Ting Wang, Zhongrui Shang, Dashan Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai 200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Recent years have witnessed an unprecedented surge of interest, from social networks to drug discovery, in learning representations of graph-structured data. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conventional digital hardware, including von Neumann bottleneck incurred by physically separated memory and processing units, slowdown of Moore's law due to transistor scaling limit, and expensive training cost. Here we present a novel hardware-software co-design, the random resistor array-based echo state graph neural network, which addresses these challenges. The random resistor arrays not only harness low-cost, nanoscale and stackable resistors for highly efficient in-memory computing using simple physical laws, but also leverage the intrinsic stochasticity of dielectric breakdown to implement random projections in hardware for an echo state network that effectively minimizes the training cost thanks to its fixed and random weights. The system demonstrates state-of-the-art performance on both graph classification using the MUTAG and COLLAB datasets and node classification using the CORA dataset, achieving 34.2×, 93.2×, and 570.4× improvement of energy efficiency and 98.27%, 99.46%, and 95.12% reduction of training cost compared to conventional graph learning on digital hardware, respectively, which may pave the way for the next generation AI system for graph learning. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Two-dimensional materials for future information technology: status and prospects

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Science China(Information Sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao YU Tiange ZHAO Qi ZHANG Mingsheng XU Peifeng LI Taotao LI Wenzhong BAO Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zengfeng DI Zhuo DONG Xidong DUAN Yuhan FENG Yu FU Jingshu GUO Pengwen GUO Yue HAO Jun HE Xiao HE Jingyi HU Weida HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI Weisheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Liwei LIU Sheng LIU Yuan LIU Donglin LU Likuan MA Feng MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN Yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG Wenhui WANG Xingjun WANG Yeliang WANG Yuwei WANG Zhenyu WANG Yao WE... Department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical Engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University School of Electronic Science and Engineering Nanjing University The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology Shaanxi Joint Key Laboratory of GrapheneSchool of MicroelectronicsXidian University Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University School of Integrated Circuits Huazhong University of Science and Technology Center for Nanochemistry (CNC) Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking University State Key Laboratory for Artificial Microstruct

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2D) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

关键词： two-dimensional (2D) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

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Enhancement of sensing performance for yeast cell using tapered optical fibre

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AIP Conference Proceedings 2024年第1期2898卷

作者： Norhafizah Burham Nur Nadia Bachok Naimah Mat Isa Norhana Arsad Photonics Technology Laboratory Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia Integrated Microelectronics System and Applications (IMSaA) School of Electrical Engineering College of Engineering Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia School of Electrical Engineering College of Engineering Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia

This paper demonstrates a simple optical sensor to sense yeast cells using a multimode tapered fibre optic. The sensitivity of various yeast cell concentrations is analysed by comparing two devices which are a spectrometer and an optical power meter. The experiment starts with tapering the optical fibre using a Vytran glass processing workstation to achieve waist diameter and length of 40 µm and 20 mm. Various yeast concentrations of 5% – 25% were used to observe the maximum absorption peak and sensitivity using the optical power meter and spectrometer. The result shows that the maximum absorption wavelength for yeast is 650 nm at 5% concentration. The sensitivity of detecting yeast is 0.005 a.u/% and 0.8931 dBm/% and was analysed by plotting the absorbance peak and output power meter. The growth and sensitivity of live yeast cells can be detected and monitored using an optical sensor and can be applied for food technology applications.

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MK-92 FIRE-CONTROL system

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NAVAL ENGINEERS JOURNAL 1979年第4期91卷 69-76页

作者： RYAN, FN BERLIN, J Mr. Floyd N. Ryan graduated from Frostburg State College and has performed graduate studies in Technology of Management at American University and University of Southern California. He is presently Executive Director of the Surface Missile Systems Sub-Group in the Naval Sea Systems Command. He was previously Deputy Project Manager of the MK-92 FCS/MK-75 Gun Project Deputy Systems Engineering Manager in the AEGIS Project and Surface Missile Systems Branch Head in the Naval Ordnance Systems Command. His earlier experience with the Department of the Navy included the Engineering Interface Management Office in the Bureau of Ships the Foreign Ship Systems Engineering Office and Switching Systems Design Office at RCA Service Company and he served for four years in the U.S. Navy as an Electronic Technician Petty Officer. In addition to ASNE he is a member of the Association of Scientists and Engineers and is the Executive Director (NAVSEA) for the 1978-79 term. In addition to several outstanding performance awards and citations for achievement Mr. Ryan received the Navy Meritorious Civilian Service Award for exceptional contribution to the development of the AEGIS Weapon System. Mr. Jack Berlin received his Bachelor's degree in Electrical Engineering from the City College of New York and his M.S. degree in Electrical Engineering from Columbia University. He spent eight years in the Radar and Microwave Electronics Section of the Naval Material Laboratory. Subsequently. he has had extensive experience in Radar and Fire Control Systems. This has included responsibility for the design and field evaluation of the GFCS MK 87 Radar. From 1972 to 1973 he was the Sperry Program Manager for the Fire Control System (FCS) MK 92. His current assignment is Assistant Manager for New Developments of Missile and Gun Fire Control Systems for the U.S. Navy. He is a member of the Tau Beta Pi and Eta Kappa Nu Honorary Societies.

The MK 92 Fire Control system (FCS) & a new, integrated, highly reliable and light-weight U.S. Navy Fire Control system for missile and gun control. This system, which is in production for the FFG, PCG, PGG and PHM Ship Classes, provides the detection and automation required for modern naval warfare. Search radar data & presented at a very high rate at the operator's console, a highly integrated man-machine interface. Utilization of monopulse and “track-while-scan” techniques result in multiple target tracking capability. The system console(s) offer a self-contained command and control capability and, in addition, standard digital computer channels provide the versatile interface with the ship's command and control, integrating the complete engagement process. Error cancellation techniques are employed to obtain high performance accuracy even under severe environmental conditions. The low manning requirement for both operation and maintenance is a key system attribute for all applications. Comprehensive “at-sea” evaluations, performed by the U.S. Navy, demonstrated successful system operation in all modes of surveillance, multi-target tracking and simultaneous missile and gun engagements. The “at-sea” performance record of the FCS MK 92 was cited by the Chief of Naval Operations to have established new standards for Naval Surface Weapons systems.

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ARCHITECTURE AND NETWORKING ISSUES IN SATELLITE systems FOR PERSONAL COMMUNICATIONS

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS 1994年第1期12卷 33-44页

作者： ANANASSO, F CAROSI, M Received the Electronics and Electrical Communications Degree from the University of Rome in 1973 after which he joined Selenia S.p.A (Rome) as a microwave designer in the Development Laboratory. He was involved in several military and civilian projects concerning microwave subsystems for Radar Avionics and Satellite Communications equipment. In 1981 he joined Telespazio S.p.A. (Rome) as Section Chief in the Space and Advanced Programs Division with responsibilities related to satellite payload and digital transmission channel design. In 1987 he was appointed Associate Professor of Digital Signal Processing at the University of Rome Tor Vergata Electronics Engineering Department. In 1990 he again joined Telespazio where he is now Director of the Advanced Studies and Experimentations Division. He has performed a number of studies for National and International Organizations including ESA EUTELSAT INMARSAT and INTELSAT is author of a Radio Systems book a Chapter (‘Digital transmission channel’) of the ‘Satellite Communication System Design’ book by Plenum Publishing (1993) and over 80 technical papers on communication systems and technology. Received the Electronics and Electrical Communications Degree from the University ‘La Sapienza’ of Rome Italy in 1987. From 1987 to 1990 he worked as a Research Engineer in the INFOCOM department of the University ‘La Sapienza’ of Rome carrying out research on design and performance evaluation of multiple access protocols particularly in the metropolitan area network and in the satellite fields. Since 1990 he has been working in the System Studies and Payload group of Telespazio S.p.A. Rome Italy. He is currently involved in projects sponsored by the European Space Agency and by INMARSAT for communications systems via satellite for mobiles both at study and R&D level. Mr Carosi is a member of the IEEE Communication Society.

The paper expands on some specific aspects of personal communications via satellite, often-although not necessarily-envisaging extensive use of 'small' satellites, using orbits not necessarily coinciding with the geostationary one (36,000 km), but rather at 1000-11,000 km altitude. One key point to be borne in mind is the issue of internetworking between satellite and terrestrial systems. Network integration should be carefully implemented in order to comply with ITU future public land mobile telecommunication systems (FPLMTS) requirements, also accepted in the European Telecommunication Standards Institute (ETSI) universal mobile telecommunication systems (UMTS). In addition to some insight into the technology of small satellites and associated orbits, architectures and networking aspects of most relevant systems providing personal communication services (PCS) are addressed in some detail, and potential applications highlighted. Frequency spectrum allocation is addressed in the light of the 1992 World Administrative Radio Conference (WARC-92), taking into account also multiple access problems and regulatory issues such as Federal Communications Commission (FCC) licensing and spectrum sharing debates. Some remarks on critical technologies and cost conclude the paper.

关键词： PERSONAL COMMUNICATIONS SMALL SATELLITES NETWORKING FREQUENCY SPECTRUM

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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