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Frontiers in Computational Neuroscience

Or P Mendels, Maoz Shamir
Noise correlations in neuronal responses can have a strong influence on the information available in large populations. In addition, the structure of noise correlations may have a great impact on the utility of different algorithms to extract this information that may depend on the specific algorithm, and hence may affect our understanding of population codes in the brain. Thus, a better understanding of the structure of noise correlations and their interplay with different readout algorithms is required. Here we use eigendecomposition to investigate the structure of noise correlations in populations of about 50-100 simultaneously recorded neurons in the primary visual cortex of anesthetized monkeys, and we relate this structure to the performance of two common decoders: the population vector and the optimal linear estimator...
2018: Frontiers in Computational Neuroscience
Rodrigo F O Pena, Sebastian Vellmer, Davide Bernardi, Antonio C Roque, Benjamin Lindner
Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about...
2018: Frontiers in Computational Neuroscience
Roey Schurr, Mor Nitzan, Ruth Eliahou, Laurent Spinelli, Margitta Seeck, Olaf Blanke, Shahar Arzy
In mental time travel (MTT) one is "traveling" back-and-forth in time, remembering, and imagining events. Despite intensive research regarding memory processes in the hippocampus, it was only recently shown that the hippocampus plays an essential role in encoding the temporal order of events remembered, and therefore plays an important role in MTT. Does it also encode the temporal relations of these events to the remembering self? We asked patients undergoing pre-surgical evaluation with depth electrodes penetrating the temporal lobes bilaterally toward the hippocampus to project themselves in time to a past, future, or present time-point, and then make judgments regarding various events...
2018: Frontiers in Computational Neuroscience
Sharon Israely, Gerry Leisman, Chay C Machluf, Eli Carmeli
Purpose: A muscle synergies model was suggested to represent a simplifying motor control mechanism by the brainstem and spinal cord. The aim of the study was to investigate the feasibility of such control mechanisms in the rehabilitation of post-stroke individuals during the execution of hand-reaching movements in multiple directions, compared to non-stroke individuals. Methods: Twelve non-stroke and 13 post-stroke individuals participated in the study. Muscle synergies were extracted from EMG data that was recorded during hand reaching tasks, using the NMF algorithm...
2018: Frontiers in Computational Neuroscience
Archana Ram, Andrew W Lo
Bacteria are easily characterizable model organisms with an impressively complicated set of abilities. Among them is quorum sensing, a cell-cell signaling system that may have a common evolutionary origin with eukaryotic cell-cell signaling. The two systems are behaviorally similar, but quorum sensing in bacteria is more easily studied in depth than cell-cell signaling in eukaryotes. Because of this comparative ease of study, bacterial dynamics are also more suited to direct interpretation than eukaryotic dynamics, e...
2018: Frontiers in Computational Neuroscience
Fatma Gargouri, Fathi Kallel, Sebastien Delphine, Ahmed Ben Hamida, Stéphane Lehéricy, Romain Valabregue
Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory. We applied the most standard preprocessing steps: slice-timing, realign, smoothing, filtering, and the tCompCor method...
2018: Frontiers in Computational Neuroscience
Yu-Xuan Fu, Yan-Mei Kang, Yong Xie
The FitzHugh-Nagumo model is improved to consider the effect of the electromagnetic induction on single neuron. On the basis of investigating the Hopf bifurcation behavior of the improved model, stochastic resonance in the stochastic version is captured near the bifurcation point. It is revealed that a weak harmonic oscillation in the electromagnetic disturbance can be amplified through stochastic resonance, and it is the cooperative effect of random transition between the resting state and the large amplitude oscillating state that results in the resonant phenomenon...
2018: Frontiers in Computational Neuroscience
Tatiana Dashevskiy, Gennady Cymbalyuk
The coexistence of neuronal activity regimes has been reported under normal and pathological conditions. Such multistability could enhance the flexibility of the nervous system and has many implications for motor control, memory, and decision making. Multistability is commonly promoted by neuromodulation targeting specific membrane ionic currents. Here, we investigated how modulation of different ionic currents could affect the neuronal propensity for bistability. We considered a leech heart interneuron model...
2018: Frontiers in Computational Neuroscience
Sorinel A Oprisan, Julia Imperatore, Jessica Helms, Tamas Tompa, Antonieta Lavin
Optogenetically evoked local field potential (LFP) recorded from the medial prefrontal cortex (mPFC) of mice during basal conditions and following a systemic cocaine administration were analyzed. Blue light stimuli were delivered to mPFC through a fiber optic every 2 s and each trial was repeated 100 times. As in the previous study, we used a surrogate data method to check that nonlinearity was present in the experimental LFPs and only used the last 1.5 s of steady activity to measure the LFPs phase resetting induced by the brief 10 ms light stimulus...
2018: Frontiers in Computational Neuroscience
Andrés Úbeda, José M Azorín, Dario Farina, Massimo Sartori
One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices...
2018: Frontiers in Computational Neuroscience
Bin Min, Douglas Zhou, David Cai
Synaptic plasticity is believed to be the biological substrate underlying learning and memory. One of the most widespread forms of synaptic plasticity, spike-timing-dependent plasticity (STDP), uses the spike timing information of presynaptic and postsynaptic neurons to induce synaptic potentiation or depression. An open question is how STDP organizes the connectivity patterns in neuronal circuits. Previous studies have placed much emphasis on the role of firing rate in shaping connectivity patterns. Here, we go beyond the firing rate description to develop a self-consistent linear response theory that incorporates the information of both firing rate and firing variability...
2018: Frontiers in Computational Neuroscience
Qiulei Dong, Hong Wang, Zhanyi Hu
No abstract text is available yet for this article.
2018: Frontiers in Computational Neuroscience
Tjeerd V Olde Scheper, Rhiannon M Meredith, Huibert D Mansvelder, Jaap van Pelt, Arjen van Ooyen
Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation...
2017: Frontiers in Computational Neuroscience
Yan Chastagnier, Enora Moutin, Anne-Laure Hemonnot, Julie Perroy
A growing number of tools now allow live recordings of various signaling pathways and protein-protein interaction dynamics in time and space by ratiometric measurements, such as Bioluminescence Resonance Energy Transfer (BRET) Imaging. Accurate and reproducible analysis of ratiometric measurements has thus become mandatory to interpret quantitative imaging. In order to fulfill this necessity, we have developed an open source toolset for Fiji-BRET-Analyzer-allowing a systematic analysis, from image processing to ratio quantification...
2017: Frontiers in Computational Neuroscience
Qing Li, Xia Wu, Lele Xu, Kewei Chen, Li Yao
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition...
2017: Frontiers in Computational Neuroscience
Marcel van Gerven
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes...
2017: Frontiers in Computational Neuroscience
Andrea Clerico, Abhishek Tiwari, Rishabh Gupta, Srinivasan Jayaraman, Tiago H Falk
The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG)...
2017: Frontiers in Computational Neuroscience
Kento Suzuki, Toshio Aoyagi, Katsunori Kitano
A dynamic system showing stable rhythmic activity can be represented by the dynamics of phase oscillators. This would provide a useful mathematical framework through which one can understand the system's dynamic properties. A recent study proposed a Bayesian approach capable of extracting the underlying phase dynamics directly from time-series data of a system showing rhythmic activity. Here we extended this method to spike data that otherwise provide only limited phase information. To determine how this method performs with spike data, we applied it to simulated spike data generated by a realistic neuronal network model...
2017: Frontiers in Computational Neuroscience
Elisa Magosso, Cristiano Cuppini, Caterina Bertini
Hemianopic patients exhibit visual detection improvement in the blind field when audiovisual stimuli are given in spatiotemporally coincidence. Beyond this "online" multisensory improvement, there is evidence of long-lasting, "offline" effects induced by audiovisual training: patients show improved visual detection and orientation after they were trained to detect and saccade toward visual targets given in spatiotemporal proximity with auditory stimuli. These effects are ascribed to the Superior Colliculus (SC), which is spared in these patients and plays a pivotal role in audiovisual integration and oculomotor behavior...
2017: Frontiers in Computational Neuroscience
Stephan Kratzer, Corinna Mattusch, Paul S Garcia, Sebastian Schmid, Eberhard Kochs, Gerhard Rammes, Gerhard Schneider, Matthias Kreuzer, Rainer Haseneder
The neuronal mechanisms how anesthetics lead to loss of consciousness are unclear. Thalamocortical interactions are crucially involved in conscious perception; hence the thalamocortical network might be a promising target for anesthetic modulation of neuronal information pertaining to arousal and waking behavior. General anesthetics affect the neurophysiology of the thalamus and the cortex but the exact mechanisms of how anesthetics interfere with processing thalamocortical information remain to be elucidated...
2017: Frontiers in Computational Neuroscience
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