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Frontiers in Computational Neuroscience

Hanin H Alahmadi, Yuan Shen, Shereen Fouad, Caroline Di B Luft, Peter Bentham, Zoe Kourtzi, Peter Tino
Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task...
2016: Frontiers in Computational Neuroscience
Juan-Miguel López-Gil, Jordi Virgili-Gomá, Rosa Gil, Teresa Guilera, Iolanda Batalla, Jorge Soler-González, Roberto García
[This corrects the article on p. 85 in vol. 10, PMID: 27594831.].
2016: Frontiers in Computational Neuroscience
Basabdatta Sen Bhattacharya, Thomas P Bond, Louise O'Hare, Daniel Turner, Simon J Durrant
Experimental studies on the Lateral Geniculate Nucleus (LGN) of mammals and rodents show that the inhibitory interneurons (IN) receive around 47.1% of their afferents from the retinal spiking neurons, and constitute around 20-25% of the LGN cell population. However, there is a definite gap in knowledge about the role and impact of IN on thalamocortical dynamics in both experimental and model-based research. We use a neural mass computational model of the LGN with three neural populations viz. IN, thalamocortical relay (TCR), thalamic reticular nucleus (TRN), to study the causality of IN on LGN oscillations and state-transitions...
2016: Frontiers in Computational Neuroscience
Milad Lankarany, Jaime E Heiss, Ilan Lampl, Taro Toyoizumi
Advanced statistical methods have enabled trial-by-trial inference of the underlying excitatory and inhibitory synaptic conductances (SCs) of membrane-potential recordings. Simultaneous inference of both excitatory and inhibitory SCs sheds light on the neural circuits underlying the neural activity and advances our understanding of neural information processing. Conventional Bayesian methods can infer excitatory and inhibitory SCs based on a single trial of observed membrane potential. However, if multiple recorded trials are available, this typically leads to suboptimal estimation because they neglect common statistics (of synaptic inputs (SIs)) across trials...
2016: Frontiers in Computational Neuroscience
Maciej Kaminski, Aneta Brzezicka, Jan Kaminski, Katarzyna J Blinowska
This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined...
2016: Frontiers in Computational Neuroscience
Jun-Wei Mao, Xiao-Lai Ye, Yong-Hua Li, Pei-Ji Liang, Ji-Wen Xu, Pu-Ming Zhang
Objectives: Accurate localization of epileptogenic zones (EZs) is essential for successful surgical treatment of refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG) signals is effective in localizing EZs. Methods: SEEG data were recorded from seven patients who underwent presurgical evaluation for the treatment of refractory focal epilepsy and for whom the subsequent resective surgery gave a good outcome...
2016: Frontiers in Computational Neuroscience
Jinglin Li, Jens P Lindemann, Martin Egelhaaf
Flying insects, such as flies or bees, rely on consistent information regarding the depth structure of the environment when performing their flight maneuvers in cluttered natural environments. These behaviors include avoiding collisions, approaching targets or spatial navigation. Insects are thought to obtain depth information visually from the retinal image displacements ("optic flow") during translational ego-motion. Optic flow in the insect visual system is processed by a mechanism that can be modeled by correlation-type elementary motion detectors (EMDs)...
2016: Frontiers in Computational Neuroscience
Oran Zohar, Maoz Shamir
Response latency has been suggested as a possible source of information in the central nervous system when fast decisions are required. The accuracy of latency codes was studied in the past using a simplified readout algorithm termed the temporal-winner-take-all (tWTA). The tWTA is a competitive readout algorithm in which populations of neurons with a similar decision preference compete, and the algorithm selects according to the preference of the population that reaches the decision threshold first. It has been shown that this algorithm can account for accurate decisions among a small number of alternatives during short biologically relevant time periods...
2016: Frontiers in Computational Neuroscience
Shuihua Wang, Ming Yang, Sidan Du, Jiquan Yang, Bin Liu, Juan M Gorriz, Javier Ramírez, Ti-Fei Yuan, Yudong Zhang
Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls...
2016: Frontiers in Computational Neuroscience
Mengjiao Chen, Wei Ren, Xingang Wang
Long-term potentiation (LTP) of synaptic strength is strongly implicated in learning and memory. On the other hand, depotentiation, the reversal of synaptic strength from potentiated LTP state to the pre-LTP level, is required in extinction of the obsolete memory. A generic tristable system, which couples the phosphatase and kinase switches, exclusively explains how moderate and high elevation of intracellular calcium concentration triggers long-term depression (LTD) and LTP, respectively. The present study, introducing calcium influx and calcium release from internal store into the tristable system, further show that significant elevation of cytoplasmic calcium concentration switches activation of both kinase and phosphatase to their basal states, thereby depotentiate the synaptic strength...
2016: Frontiers in Computational Neuroscience
Fikret E Kapucu, Inkeri Välkki, Jarno E Mikkonen, Chiara Leone, Kerstin Lenk, Jarno M A Tanskanen, Jari A K Hyttinen
Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands...
2016: Frontiers in Computational Neuroscience
Hisashi Kada, Jun-Nosuke Teramae, Isao T Tokuda
Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons...
2016: Frontiers in Computational Neuroscience
Viktor Müller, Dionysios Perdikis, Timo von Oertzen, Rita Sleimen-Malkoun, Viktor Jirsa, Ulman Lindenberger
Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz...
2016: Frontiers in Computational Neuroscience
Sijie Zhou, Brendan Z Allison, Andrea Kübler, Andrzej Cichocki, Xingyu Wang, Jing Jin
Several studies have explored brain computer interface (BCI) systems based on auditory stimuli, which could help patients with visual impairments. Usability and user satisfaction are important considerations in any BCI. Although background music can influence emotion and performance in other task environments, and many users may wish to listen to music while using a BCI, auditory, and other BCIs are typically studied without background music. Some work has explored the possibility of using polyphonic music in auditory BCI systems...
2016: Frontiers in Computational Neuroscience
Hugo Angleys, Sune N Jespersen, Leif Østergaard
Glucose is the brain's principal source of ATP, but the extent to which cerebral glucose consumption (CMRglc) is coupled with its oxygen consumption (CMRO2) remains unclear. Measurements of the brain's oxygen-glucose index OGI = CMRO2/CMRglc suggest that its oxygen uptake largely suffices for oxidative phosphorylation. Nevertheless, during functional activation and in some disease states, brain tissue seemingly produces lactate although cerebral blood flow (CBF) delivers sufficient oxygen, so-called aerobic glycolysis...
2016: Frontiers in Computational Neuroscience
Lei Wang, Yi-Hong Qiu, Yanjun Zeng
As the sole output neurons in the retina, ganglion cells play significant roles in transforming visual information into spike trains, and then transmitting them to the higher visual centers. However, coding strategies that retinal ganglion cells (RGCs) adopt to accomplish these processes are not completely clear yet. To clarify these issues, we investigate the coding properties of three types of RGCs (repetitive spiking, tonic firing, and phasic firing) by two different measures (spike-rate and spike-latency)...
2016: Frontiers in Computational Neuroscience
Xuemei Lei, Zhuo Han, Chuansheng Chen, Lu Bai, Gui Xue, Qi Dong
The striatum is an important subcortical structure with extensive connections to other regions of the brain. These connections are believed to play important roles in behaviors such as reward-related processes and impulse control, which show significant sex differences. However, little is known about sex differences in the striatum-projected fiber connectivity. The current study examined sex differences between 50 Chinese males and 79 Chinese females in their fiber connections between the striatum and nine selected cortical and subcortical regions...
2016: Frontiers in Computational Neuroscience
Jesus Minguillon, Miguel A Lopez-Gordo, Francisco Pelayo
Stress assessment has been under study in the last years. Both biochemical and physiological markers have been used to measure stress level. In neuroscience, several studies have related modification of stress level to brain activity changes in limbic system and frontal regions, by using non-invasive techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). In particular, previous studies suggested that the exhibition or inhibition of certain brain rhythms in frontal cortical areas indicates stress...
2016: Frontiers in Computational Neuroscience
Wolf Singer, Andreea Lazar
The discovery of stimulus induced synchronization in the visual cortex suggested the possibility that the relations among low-level stimulus features are encoded by the temporal relationship between neuronal discharges. In this framework, temporal coherence is considered a signature of perceptual grouping. This insight triggered a large number of experimental studies which sought to investigate the relationship between temporal coordination and cognitive functions. While some core predictions derived from the initial hypothesis were confirmed, these studies, also revealed a rich dynamical landscape beyond simple coherence whose role in signal processing is still poorly understood...
2016: Frontiers in Computational Neuroscience
Rodrigo Echeveste, Claudius Gros
The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period...
2016: Frontiers in Computational Neuroscience
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