Thalamic Network Oscillations Synchronize Ontogenetic Columns in the Newborn Rat Barrel Cortex

Neocortical areas are organized in columns, which form the basic structural and functional modules of intracortical information processing. Using voltage-sensitive dye imaging and simultaneous multi-channel extracellular recordings in the barrel cortex of newborn rats in vivo, we found that spontaneously occurring and whisker stimulation-induced gamma bursts followed by longer lasting spindle bursts were topographically organized in functional cortical columns already at the day of birth. Gamma bursts synchronized a cortical network of 300-400 µm in diameter and were coherent with gamma activity recorded simultaneously in the thalamic ventral posterior medial (VPM) nucleus. Cortical gamma bursts could be elicited by focal electrical stimulation of the VPM. Whisker stimulation-induced spindle and gamma bursts and the majority of spontaneously occurring events were profoundly reduced by the local inactivation of the VPM, indicating that the thalamus is important to generate these activity patterns. Furthermore, inactivation of the barrel cortex with lidocaine reduced the gamma activity in the thalamus, suggesting that a cortico-thalamic feedback loop modulates this early thalamic network activit

Results based on 124 cases of breast cancer and 97 controls from Taiwan suggest that the single nucleotide polymorphism (SNP309) in the MDM2 gene promoter is associated with earlier onset and increased risk of breast cancer

<p>Abstract</p> <p>Background</p> <p>It has been suggested that the single nucleotide polymorphism 309 (SNP309, T -> G) in the promoter region of the MDM2 gene is important for tumor development; however, with regards to breast cancer, inconsistent associations have been reported worldwide. It is speculated that these conflicting results may have arisen due to different patient subgroups and ethnicities studied. For the first time, this study explores the effect of the MDM2 SNP309 genotype on Taiwanese breast cancer patients.</p> <p>Methods</p> <p>Genomic DNA was obtained from the whole blood of 124 breast cancer patients and 97 cancer-free healthy women living in Taiwan. MDM2 SNP309 genotyping was carried out by restriction fragment length polymorphism (RFLP) assay. The multivariate logistic regression and the Kaplan-Meier method were used for analyzing the risk association and significance of age at diagnosis among different MDM2 SNP309 genotypes, respectively.</p> <p>Results</p> <p>Compared to the TT genotype, an increased risk association with breast cancer was apparent for the GG genotype (OR = 3.05, 95% CI = 1.04 to 8.95), and for the TG genotype (OR = 2.12, 95% CI = 0.90 to 5.00) after adjusting for age, cardiovascular disease/diabetes, oral contraceptive usage, and body mass index, which exhibits significant difference between cases and controls. Furthermore, the average ages at diagnosis for breast cancer patients were 53.6, 52 and 47 years for those harboring TT, TG and GG genotypes, respectively. A significant difference in median age of onset for breast cancer between GG and TT+TG genotypes was obtained by the log-rank test (p = 0.0067).</p> <p>Conclusion</p> <p>Findings based on the current sample size suggest that the MDM2 SNP309 GG genotype may be associated with both the risk of breast cancer and an earlier age of onset in Taiwanese women.</p

Neural network activity in the neonatal acute slice, slice culture and cell culture

Information processing and storage in the brain may be presented by the oscillations and cell assemblies. Here we address the question of how individual neurons associate together to assemble neural networks and present spontaneous electrical activity. Therefore, we dissected the neonatal brain at three different levels: acute 1-mm thick brain slice, cultured organotypic 350-µm thick brain slice and dissociated neuronal cultures. The spatio-temporal properties of neural activity were investigated by using a 60-channel Micro-electrode arrays (MEA), and the cell assemblies were studied by using a template-matching method. We find local on-propagating as well as large- scale propagating spontaneous oscillatory activity in acute slices, spontaneous network activity characterized by synchronized burst discharges in organotypic cultured slices, and autonomous bursting behaviour in dissociated neuronal cultures. Furthermore, repetitive spike patterns emerge after one week of dissociated neuronal culture and dramatically increase their numbers as well as their complexity and occurrence in the second week. Our data indicate that neurons can self-organize themselves, assembly to a neural network, present spontaneous oscillations, and emerge spatio-temporal activation patterns. The spontaneous oscillations and repetitive spike patterns may serve fundamental functions for information processing and storage in the brain

Neuro-fuzzy modeling and control

Abstract | Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (Adaptive-Network-based Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. Keywords|Fuzzy logic, neural networks, fuzzy modeling, neuro-fuzzy modeling, neuro-fuzzy control, ANFIS. I

Single trial dynamics of hippocampal spatial representations are modulated by reward value

Data and analysis code accompanying paper that describes the influence of reward value on the dynamics of hippocampal spatial representations

Neuro-Fuzzy and soft computing: a computational approach to learning and machine intelligence/ Jang

xxvi, 614 hal.: ill.; 25 cm

Neuro-Fuzzy and soft computing: a computational approach to learning and machine intelligence/ Jang

xxvi, 614 hal.: ill.; 25 cm

Neuro-Fuzzy and Soft Computing : A Computational Approach to Learning and Machine Intelligence

Neuro-Fuzzy Modeling and Soft Computing places particular emphasis on the theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. Neuro-Fuzzy Modeling and Soft Computing is oriented toward methodologies that are likely to be of practical use. It includes exercises, some of which involve MATLAB programming tasks to provide readers with hands-on programming experiences for practical problem-solving. Each chapter also includes a reference list to the research literature so that readers may pursue topics in greater depth. This book is suitable as a self-study guide by researchers who want to learn basic and advanced neuro-fuzzy and soft computing within the framework of computational intelligence