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Frontiers in Computational Neuroscience

Hong Qiao, Li Hu
No abstract text is available yet for this article.
2016: Frontiers in Computational Neuroscience
Kang Li, Vladislav Kozyrev, Søren Kyllingsbæk, Stefan Treue, Susanne Ditlevsen, Claus Bundesen
A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al...
2016: Frontiers in Computational Neuroscience
Xiaolong Zou, Da-Hui Wang
Characteristic phase shifts between discharges of pyramidal cells and interneurons in oscillation have been widely observed in experiments, and they have been suggested to play important roles in neural computation. Previous studies mainly explored two independent mechanisms to generate neural oscillation, one is based on the interaction loop between pyramidal cells and interneurons, referred to as the E-I loop, and the other is based on the interaction loop between interneurons, referred to as the I-I loop...
2016: Frontiers in Computational Neuroscience
Maria Constantinou, Soledad Gonzalo Cogno, Daniel H Elijah, Emilio Kropff, John Gigg, Inés Samengo, Marcelo A Montemurro
Burst spike patterns are common in regions of the hippocampal formation such as the subiculum and medial entorhinal cortex (MEC). Neurons in these areas are immersed in extracellular electrical potential fluctuations often recorded as the local field potential (LFP). LFP rhythms within different frequency bands are linked to different behavioral states. For example, delta rhythms are often associated with slow-wave sleep, inactivity and anesthesia; whereas theta rhythms are prominent during awake exploratory behavior and REM sleep...
2016: Frontiers in Computational Neuroscience
Patrick McClure, Nikolaus Kriegeskorte
Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher...
2016: Frontiers in Computational Neuroscience
Maciej Labecki, Rafal Kus, Alicja Brzozowska, Tadeusz Stacewicz, Basabdatta S Bhattacharya, Piotr Suffczynski
Steady state visual evoked potentials (SSVEPs) are steady state oscillatory potentials elicited in the electroencephalogram (EEG) by flicker stimulation. The frequency of these responses maches the frequency of the stimulation and of its harmonics and subharmonics. In this study, we investigated the origin of the harmonic and subharmonic components of SSVEPs, which are not well understood. We applied both sine and square wave visual stimulation at 5 and 15 Hz to human subjects and analyzed the properties of the fundamental responses and harmonically related components...
2016: Frontiers in Computational Neuroscience
Grzegorz Bokota, Marta Magnowska, Tomasz Kuśmierczyk, Michał Łukasik, Matylda Roszkowska, Dariusz Plewczynski
: The common approach in morphological analysis of dendritic spines of mammalian neuronal cells is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia shaped. The corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate the synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist...
2016: Frontiers in Computational Neuroscience
Mina Shahi, Carl van Vreeswijk, Gordon Pipa
Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes...
2016: Frontiers in Computational Neuroscience
Yaki Stern, Amit Reches, Amir B Geva
The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects...
2016: Frontiers in Computational Neuroscience
Guoqi Li, Lei Deng, Dong Wang, Wei Wang, Fei Zeng, Ziyang Zhang, Huanglong Li, Sen Song, Jing Pei, Luping Shi
Chunking refers to a phenomenon whereby individuals group items together when performing a memory task to improve the performance of sequential memory. In this work, we build a bio-plausible hierarchical chunking of sequential memory (HCSM) model to explain why such improvement happens. We address this issue by linking hierarchical chunking with synaptic plasticity and neuromorphic engineering. We uncover that a chunking mechanism reduces the requirements of synaptic plasticity since it allows applying synapses with narrow dynamic range and low precision to perform a memory task...
2016: Frontiers in Computational Neuroscience
Sungwoo Ahn, S Elizabeth Zauber, Robert M Worth, Leonid L Rubchinsky
Hypokinetic symptoms of Parkinson's disease are usually associated with excessively strong oscillations and synchrony in the beta frequency band. The origin of this synchronized oscillatory dynamics is being debated. Cortical circuits may be a critical source of excessive beta in Parkinson's disease. However, subthalamo-pallidal circuits were also suggested to be a substantial component in generation and/or maintenance of Parkinsonian beta activity. Here we study how the subthalamo-pallidal circuits interact with input signals in the beta frequency band, representing cortical input...
2016: Frontiers in Computational Neuroscience
José Luis Carrillo-Medina, Roberto Latorre
Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems...
2016: Frontiers in Computational Neuroscience
Ran Manor, Liran Mishali, Amir B Geva
Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images...
2016: Frontiers in Computational Neuroscience
Elaine E Orendorff, Laurynas Kalesinskas, Robert T Palumbo, Mark V Albert
To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)-a self-reported measure of elevation angle. This framework provides a single, coherent model explaining a set of experimentally observed PEL over a range of experimental conditions. Further, it provides a parsimonious explanation for the additive effect of low fidelity cues as well as the averaging effect of high fidelity cues, as also found in other Bayesian cue combination psychophysical studies...
2016: Frontiers in Computational Neuroscience
Eric Chalmers, Artur Luczak, Aaron J Gruber
The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide "goal-directed" behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals' ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change...
2016: Frontiers in Computational Neuroscience
James J Wright, Paul D Bourke
This paper furthers our attempts to resolve two major controversies-whether gamma synchrony plays a role in cognition, and whether cortical columns are functionally important. We have previously argued that the configuration of cortical cells that emerges in development is that which maximizes the magnitude of synchronous oscillation and minimizes metabolic cost. Here we analyze the separate effects in development of minimization of axonal lengths, and of early Hebbian learning and selective distribution of resources to growing synapses, by showing in simulations that these effects are partially antagonistic, but their interaction during development produces accurate anatomical and functional properties for both columnar and non-columnar cortex...
2016: Frontiers in Computational Neuroscience
Yuan Yang, Teodoro Solis-Escalante, Mark van de Ruit, Frans C T van der Helm, Alfred C Schouten
Coupling between cortical oscillations and muscle activity facilitates neuronal communication during motor control. The linear part of this coupling, known as corticomuscular coherence, has received substantial attention, even though neuronal communication underlying motor control has been demonstrated to be highly nonlinear. A full assessment of corticomuscular coupling, including the nonlinear part, is essential to understand the neuronal communication within the sensorimotor system. In this study, we applied the recently developed n:m coherence method to assess nonlinear corticomuscular coupling during isotonic wrist flexion...
2016: Frontiers in Computational Neuroscience
Wei Xu, Stuart N Baker
We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons...
2016: Frontiers in Computational Neuroscience
Linling Li, Hui Wang, Xijie Ke, Xiaowu Liu, Yuan Yuan, Deren Zhang, Donglin Xiong, Yunhai Qiu
[This corrects the article on p. 45 in vol. 10, PMID: 27242501.].
2016: Frontiers in Computational Neuroscience
Hamed Bahmani, Siegfried Wahl
No abstract text is available yet for this article.
2016: Frontiers in Computational Neuroscience
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