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https://www.readbyqxmd.com/read/28318488/computational-models-of-o-lm-cells-are-recruited-by-low-or-high-theta-frequency-inputs-depending-on-h-channel-distributions
#1
Vladislav Sekulić, Frances K Skinner
Although biophysical details of inhibitory neurons are becoming known, it is challenging to map these details onto function. Oriens-lacunosum/moleculare (O-LM) cells are inhibitory cells in the hippocampus that gate information flow, firing while phase-locked to theta rhythms. We build on our existing computational model database of O-LM cells to link model with function. We place our models in high-conductance states and modulate inhibitory inputs at a wide range of frequencies. We find preferred spiking recruitment of models at high (4-9 Hz) or low (2-5 Hz) theta depending on, respectively, the presence or absence of h-channels on their dendrites...
March 20, 2017: ELife
https://www.readbyqxmd.com/read/28318265/cytoskeletal-like-filaments-of-camkii-are-formed-in-a-regulated-and-zn2-dependent-manner
#2
Laurel Hoffman, Lin Li, Emil Alexov, Hugo Sanabria, Melvin Neal Waxham
Ca2+-Calmodulin-dependent protein kinase II (CaMKII) is highly abundant in neurons, where its concentration reaches that typically found for cytoskeletal proteins. Functional reasons for such a high concentration are not known, but given the multitude of known binding partners for CaMKII, a role as a scaffolding molecule has been proposed. In this report, we provide experimental evidence that demonstrates a novel structural role for CaMKII. We discovered that CaMKII forms filaments that can extend for several microns in the presence of certain divalent cations (Zn2+, Cd2+ and Cu2+) but not with others (Ca2+, Mg2+, Co2+ and Ni2+)...
March 20, 2017: Biochemistry
https://www.readbyqxmd.com/read/28316111/pattern-separation-in-the-hippocampus-through-the-eyes-of-computational-modeling
#3
REVIEW
Spyridon Chavlis, Panayiota Poirazi
Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modelling studies...
March 18, 2017: Synapse
https://www.readbyqxmd.com/read/28299833/unsupervised-pharmacophore-modeling-combined-with-qsar-analyses-revealed-novel-low-micromolar-sirt2-inhibitors
#4
Mohammad A Khanfar, Mutasem O Taha
Situin 2 (SIRT2) enzyme is a histone deacetylase that has important role in neuronal development. SIRT2 is clinically validated target for neurodegenerative diseases and some cancers. In this study, exhaustive unsupervised pharmacophore modeling was combined with quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent SIRT2 inhibitors using 146 known SIRT2 ligands. A computational workflow that combines genetic function algorithm with k-nearest neighbor or multiple linear regression was implemented to build self-consistent and predictive QSAR models based on combinations of pharmacophores and physicochemical descriptors...
March 15, 2017: Journal of Molecular Recognition: JMR
https://www.readbyqxmd.com/read/28298703/a-tutorial-on-the-free-energy-framework-for-modelling-perception-and-learning
#5
Rafal Bogacz
This paper provides an easy to follow tutorial on the free-energy framework for modelling perception developed by Friston, which extends the predictive coding model of Rao and Ballard. These models assume that the sensory cortex infers the most likely values of attributes or features of sensory stimuli from the noisy inputs encoding the stimuli. Remarkably, these models describe how this inference could be implemented in a network of very simple computational elements, suggesting that this inference could be performed by biological networks of neurons...
February 2017: Journal of Mathematical Psychology
https://www.readbyqxmd.com/read/28297857/mean-field-message-passing-equations-in-the-hopfield-model-and-its-generalizations
#6
Marc Mézard
Motivated by recent progress in using restricted Boltzmann machines as preprocessing algorithms for deep neural network, we revisit the mean-field equations [belief-propagation and Thouless-Anderson Palmer (TAP) equations] in the best understood of such machines, namely the Hopfield model of neural networks, and we explicit how they can be used as iterative message-passing algorithms, providing a fast method to compute the local polarizations of neurons. In the "retrieval phase", where neurons polarize in the direction of one memorized pattern, we point out a major difference between the belief propagation and TAP equations: The set of belief propagation equations depends on the pattern which is retrieved, while one can use a unique set of TAP equations...
February 2017: Physical Review. E
https://www.readbyqxmd.com/read/28294334/comparative-assessment-of-6-18-f-fluoro-l-m-tyrosine-and-6-18-f-fluoro-l-dopa-to-evaluate-dopaminergic-presynaptic-integrity-in-a-parkinson-s-disease-rat-model
#7
REVIEW
Guillaume Becker, Bahri Mohamed Ali, Anne Michel, Fabian Hustadt, Gaëtan Garraux, André Luxen, Christian Lemaire, Alain Plenevaux
Because of the progressive loss of nigro-striatal dopaminergic terminals in Parkinson's disease (PD), in vivo quantitative imaging of dopamine (DA) containing neurons in animal models of PD is of critical importance in the pre-clinical evaluation of highly awaited disease-modifying therapies. Among existing methods, the high sensitivity of positron emission tomography (PET) is attractive to achieve that goal. The aim of this study was to perform a quantitative comparison of brain images obtained in 6-hydroxydopamine (6-OHDA) lesioned rats using two dopaminergic PET radiotracers, namely [(18) F]fluoro-3,4-dihydroxyphenyl-L-alanine ([(18) F]FDOPA) and 6-[(18) F]fluoro-L-m-tyrosine ([(18) F]FMT)...
March 10, 2017: Journal of Neurochemistry
https://www.readbyqxmd.com/read/28293164/double-barrier-memristive-devices-for-unsupervised-learning-and-pattern-recognition
#8
Mirko Hansen, Finn Zahari, Martin Ziegler, Hermann Kohlstedt
The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al2O3/Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al2O3 tunnel barrier and a 2.5 mm thick Nb x O y memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28293163/an-fpga-platform-for-real-time-simulation-of-spiking-neuronal-networks
#9
Danilo Pani, Paolo Meloni, Giuseppe Tuveri, Francesca Palumbo, Paolo Massobrio, Luigi Raffo
In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28291787/fast-online-deconvolution-of-calcium-imaging-data
#10
Johannes Friedrich, Pengcheng Zhou, Liam Paninski
Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session...
March 14, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28289370/energetic-constraints-produce-self-sustained-oscillatory-dynamics-in-neuronal-networks
#11
Javier Burroni, P Taylor, Cassian Corey, Tengiz Vachnadze, Hava T Siegelmann
Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28287987/computational-model-based-on-neural-network-of-visual-cortex-for-human-action-recognition
#12
Haihua Liu, Na Shu, Qiling Tang, Wensheng Zhang
In this paper, we propose a bioinspired model for human action recognition through modeling neural mechanisms of information processing in two visual cortical areas: the primary visual cortex (V1) and the middle temporal cortex (MT) dedicated to motion. This model, named V1-MT, is composed of V1 and MT models (layers) corresponding to their cortical areas, which are built with layered spiking neural networks (SNNs). Some neuron properties in V1 and MT, such as direction and speed selectivity, spatiotemporal inseparability, and center surround suppression, are integrated into SNNs...
March 8, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28286470/screening-the-molecular-framework-underlying-local-dendritic-mrna-translation
#13
REVIEW
Sanjeev V Namjoshi, Kimberly F Raab-Graham
In the last decade, bioinformatic analyses of high-throughput proteomics and transcriptomics data have enabled researchers to gain insight into the molecular networks that may underlie lasting changes in synaptic efficacy. Development and utilization of these techniques have advanced the field of learning and memory significantly. It is now possible to move from the study of activity-dependent changes of a single protein to modeling entire network changes that require local protein synthesis. This data revolution has necessitated the development of alternative computational and statistical techniques to analyze and understand the patterns contained within...
2017: Frontiers in Molecular Neuroscience
https://www.readbyqxmd.com/read/28283561/alteration-of-neuronal-excitability-and-short-term-synaptic-plasticity-in-the-prefrontal-cortex-of-a-mouse-model-of-mental-illness
#14
Gregg W Crabtree, Ziyi Sun, Mirna Kvajo, Jantine Ac Broek, Karine Fénelon, Heather McKellar, Lan Xiao, Bin Xu, Sabine Bahn, James M O'Donnell, Joseph A Gogos
Employing a genetic mouse model that faithfully recapitulates a DISC1 genetic alteration strongly associated with schizophrenia and other psychiatric disorders, we examined the impact of this mutation within the prefrontal cortex. Although cortical layering, cytoarchitecture and proteome were found to be largely unaffected, electrophysiological examination of the mPFC revealed both neuronal hyper-excitability and alterations in short-term synaptic plasticity consistent with enhanced neurotransmitter release...
March 10, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28277064/inhibition-of-nmda-receptor-function-with-an-anti-glun1-s2-antibody-impairs-human-platelet-function-and-thrombosis
#15
Taryn N Green, Justin R Hamilton, Marie-Christine Morel-Kopp, Zhaohua Zheng, Ting-Yu T Chen, James I Hearn, Peng P Sun, Jack U Flanagan, Deborah Young, P Alan Barber, Matthew J During, Christopher M Ward, Maggie L Kalev-Zylinska
GluN1 is a mandatory component of N-methyl-D-aspartate receptors (NMDARs) best known for their roles in the brain, but with increasing evidence for relevance in peripheral tissues, including platelets. Certain anti-GluN1 antibodies reduce brain infarcts in rodent models of ischaemic stroke. There is also evidence that human anti-GluN1 autoantibodies reduce neuronal damage in stroke patients, but the underlying mechanism is unclear. This study investigated whether anti-GluN1-mediated neuroprotection involves inhibition of platelet function...
February 21, 2017: Platelets
https://www.readbyqxmd.com/read/28275720/hetereogeneity-in-neuronal-intrinsic-properties-a-possible-mechanism-for-hub-like-properties-of-the-rat-anterior-cingulate-cortex-during-network-activity
#16
Natalie E Adams, Jason S Sherfey, Nancy J Kopell, Miles A Whittington, Fiona E N LeBeau
The anterior cingulate cortex (ACC) is vital for a range of brain functions requiring cognitive control and has highly divergent inputs and outputs, thus manifesting as a hub in connectomic analyses. Studies show diverse functional interactions within the ACC are associated with network oscillations in the β (20-30 Hz) and γ (30-80 Hz) frequency range. Oscillations permit dynamic routing of information within cortex, a function that depends on bandpass filter-like behavior to selectively respond to specific inputs...
January 2017: ENeuro
https://www.readbyqxmd.com/read/28270761/reproducibility-and-comparability-of-computational-models-for-astrocyte-calcium-excitability
#17
Tiina Manninen, Riikka Havela, Marja-Leena Linne
The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28270760/determine-neuronal-tuning-curves-by-exploring-optimum-firing-rate-distribution-for-information-efficiency
#18
Fang Han, Zhijie Wang, Hong Fan
This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy consumption is composed of two parts: neuronal basic energy consumption and neuronal spike emission energy consumption. A parameter to model the relative importance of energy consumption is introduced in the definition of the information efficiency...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28270635/-molecular-mechanisms-of-circadian-rhythm-and-sleep-homeostasis
#19
Kazuhiro Kon, Koji L Ode, Hiroki R Ueda
Sleep-wake cycle is controlled by the interplay between circadian rhythm and sleep homeostasis. Genetic studies, through the discovery of mutants with altered sleep-wake behaviors, have explored the molecular components that regulate our daily rhythms. In mammalian circadian clocks, negative-feedback loops composed of a set of transcription activators and inhibitors generate a cell-autonomous oscillation of transcriptional activity. Recent studies further discovered that such transcriptional feedback is controlled through post-translational modifications for the fine-tuning of the oscillation period...
March 2017: Brain and Nerve, Shinkei Kenkyū No Shinpo
https://www.readbyqxmd.com/read/28269816/automatic-epileptic-seizure-detection-in-eegs-using-mf-dfa-svm-based-on-cloud-computing
#20
Zhongnan Zhang, Tingxi Wen, Wei Huang, Meihong Wang, Chunfeng Li
BACKGROUND: Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. OBJECTIVE: In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM)...
March 3, 2017: Journal of X-ray Science and Technology
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