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Frontiers in Computational Neuroscience

Gemma Huguet, Xiangying Meng, John Rinzel
Phasic neurons typically fire only for a fast-rising input, say at the onset of a step current, but not for steady or slow inputs, a property associated with type III excitability. Phasic neurons can show extraordinary temporal precision for phase locking and coincidence detection. Exemplars are found in the auditory brain stem where precise timing is used in sound localization. Phasicness at the cellular level arises from a dynamic, voltage-gated, negative feedback that can be recruited subthreshold, preventing the neuron from reaching spike threshold if the voltage does not rise fast enough...
2017: Frontiers in Computational Neuroscience
Tomoyasu Horikawa, Yukiyasu Kamitani
Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming...
2017: Frontiers in Computational Neuroscience
Anis Yuniati, Te-Lun Mai, Chi-Ming Chen
In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy...
2017: Frontiers in Computational Neuroscience
Yi Yuan, Na Pang, Yudong Chen, Yi Wang, Xiaoli Li
Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors...
2017: Frontiers in Computational Neuroscience
Max Garagnani, Guglielmo Lucchese, Rosario Tomasello, Thomas Wennekers, Friedemann Pulvermüller
Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only...
2016: Frontiers in Computational Neuroscience
Naser Mehrabi, Reza Sharif Razavian, Borna Ghannadi, John McPhee
This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics...
2016: Frontiers in Computational Neuroscience
Carl Y Saab, Lisa Feldman Barrett
No abstract text is available yet for this article.
2016: Frontiers in Computational Neuroscience
David Logan, Tim Kiemel, John J Jeka
Here we apply a control theoretic view of movement to the behavior of human locomotion with the goal of using perturbations to learn about subtask control. Controlling one's speed and maintaining upright posture are two critical subtasks, or underlying functions, of human locomotion. How the nervous system simultaneously controls these two subtasks was investigated in this study. Continuous visual and mechanical perturbations were applied concurrently to subjects (n = 20) as probes to investigate these two subtasks during treadmill walking...
2016: Frontiers in Computational Neuroscience
Viola Folli, Marco Leonetti, Giancarlo Ruocco
Recurrent neural networks (RNN) have traditionally been of great interest for their capacity to store memories. In past years, several works have been devoted to determine the maximum storage capacity of RNN, especially for the case of the Hopfield network, the most popular kind of RNN. Analyzing the thermodynamic limit of the statistical properties of the Hamiltonian corresponding to the Hopfield neural network, it has been shown in the literature that the retrieval errors diverge when the number of stored memory patterns (P) exceeds a fraction (≈ 14%) of the network size N...
2016: 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
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