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Frontiers in Computational Neuroscience

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https://www.readbyqxmd.com/read/29326578/audiovisual-rehabilitation-in-hemianopia-a-model-based-theoretical-investigation
#1
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
https://www.readbyqxmd.com/read/29321737/propofol-and-sevoflurane-differentially-modulate-cortical-depolarization-following-electric-stimulation-of-the-ventrobasal-thalamus
#2
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
https://www.readbyqxmd.com/read/29311884/editorial-artificial-neural-networks-as-models-of-neural-information-processing
#3
EDITORIAL
Marcel van Gerven, Sander Bohte
No abstract text is available yet for this article.
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29249953/learning-peri-saccadic-remapping-of-receptive-field-from-experience-in-lateral-intraparietal-area
#4
Xiao Wang, Yan Wu, Mingsha Zhang, Si Wu
Our eyes move constantly at a frequency of 3-5 times per second. These movements, called saccades, induce the sweeping of visual images on the retina, yet we perceive the world as stable. It has been suggested that the brain achieves this visual stability via predictive remapping of neuronal receptive field (RF). A recent experimental study disclosed details of this remapping process in the lateral intraparietal area (LIP), that is, about the time of the saccade, the neuronal RF expands along the saccadic trajectory temporally, covering the current RF (CRF), the future RF (FRF), and the region the eye will sweep through during the saccade...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29238299/the-htm-spatial-pooler-a-neocortical-algorithm-for-online-sparse-distributed-coding
#5
Yuwei Cui, Subutai Ahmad, Jeff Hawkins
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29230172/sensor-motor-maps-for-describing-linear-reflex-composition-in-hopping
#6
Christian Schumacher, André Seyfarth
In human and animal motor control several sensory organs contribute to a network of sensory pathways modulating the motion depending on the task and the phase of execution to generate daily motor tasks such as locomotion. To better understand the individual and joint contribution of reflex pathways in locomotor tasks, we developed a neuromuscular model that describes hopping movements. In this model, we consider the influence of proprioceptive length (LFB), velocity (VFB) and force feedback (FFB) pathways of a leg extensor muscle on hopping stability, performance and efficiency (metabolic effort)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29230171/spatial-temporal-feature-analysis-on-single-trial-event-related-potential-for-rapid-face-identification
#7
Lei Jiang, Yun Wang, Bangyu Cai, Yueming Wang, Yiwen Wang
The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29209192/response-of-electrical-activity-in-an-improved-neuron-model-under-electromagnetic-radiation-and-noise
#8
Feibiao Zhan, Shenquan Liu
Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L) model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29209191/conduction-delay-learning-model-for-unsupervised-and-supervised-classification-of-spatio-temporal-spike-patterns
#9
Takashi Matsubara
Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29209190/classification-of-eeg-signals-based-on-pattern-recognition-approach
#10
Hafeez Ullah Amin, Wajid Mumtaz, Ahmad Rauf Subhani, Mohamad Naufal Mohamad Saad, Aamir Saeed Malik
Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29209189/spike-triggered-regression-for-synaptic-connectivity-reconstruction-in-neuronal-networks
#11
Yaoyu Zhang, Yanyang Xiao, Douglas Zhou, David Cai
How neurons are connected in the brain to perform computation is a key issue in neuroscience. Recently, the development of calcium imaging and multi-electrode array techniques have greatly enhanced our ability to measure the firing activities of neuronal populations at single cell level. Meanwhile, the intracellular recording technique is able to measure subthreshold voltage dynamics of a neuron. Our work addresses the issue of how to combine these measurements to reveal the underlying network structure. We propose the spike-triggered regression (STR) method, which employs both the voltage trace and firing activity of the neuronal population to reconstruct the underlying synaptic connectivity...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29201003/electrical-activity-in-a-time-delay-four-variable-neuron-model-under-electromagnetic-induction
#12
Keming Tang, Zuolei Wang, Xuerong Shi
To investigate the effect of electromagnetic induction on the electrical activity of neuron, the variable for magnetic flow is used to improve Hindmarsh-Rose neuron model. Simultaneously, due to the existence of time-delay when signals are propagated between neurons or even in one neuron, it is important to study the role of time-delay in regulating the electrical activity of the neuron. For this end, a four-variable neuron model is proposed to investigate the effects of electromagnetic induction and time-delay...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29184491/computational-and-experimental-approaches-to-visual-aesthetics
#13
REVIEW
Anselm Brachmann, Christoph Redies
Aesthetics has been the subject of long-standing debates by philosophers and psychologists alike. In psychology, it is generally agreed that aesthetic experience results from an interaction between perception, cognition, and emotion. By experimental means, this triad has been studied in the field of experimental aesthetics, which aims to gain a better understanding of how aesthetic experience relates to fundamental principles of human visual perception and brain processes. Recently, researchers in computer vision have also gained interest in the topic, giving rise to the field of computational aesthetics...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29163117/deep-learning-predicts-correlation-between-a-functional-signature-of-higher-visual-areas-and-sparse-firing-of-neurons
#14
Chengxu Zhuang, Yulong Wang, Daniel Yamins, Xiaolin Hu
Visual information in the visual cortex is processed in a hierarchical manner. Recent studies show that higher visual areas, such as V2, V3, and V4, respond more vigorously to images with naturalistic higher-order statistics than to images lacking them. This property is a functional signature of higher areas, as it is much weaker or even absent in the primary visual cortex (V1). However, the mechanism underlying this signature remains elusive. We studied this problem using computational models. In several typical hierarchical visual models including the AlexNet, VggNet, and SHMAX, this signature was found to be prominent in higher layers but much weaker in lower layers...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29163116/a-sensitivity-analysis-of-an-inverted-pendulum-balance-control-model
#15
Jantsje H Pasma, Tjitske A Boonstra, Joost van Kordelaar, Vasiliki V Spyropoulou, Alfred C Schouten
Balance control models are used to describe balance behavior in health and disease. We identified the unique contribution and relative importance of each parameter of a commonly used balance control model, the Independent Channel (IC) model, to identify which parameters are crucial to describe balance behavior. The balance behavior was expressed by transfer functions (TFs), representing the relationship between sensory perturbations and body sway as a function of frequency, in terms of amplitude (i.e., magnitude) and timing (i...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29123478/neural-synchronization-from-the-perspective-of-non-linear-dynamics
#16
Ramon Guevara Erra, Jose L Perez Velazquez, Michael Rosenblum
No abstract text is available yet for this article.
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29123477/the-effects-of-medium-spiny-neuron-morphologcial-changes-on-basal-ganglia-network-under-external-electric-field-a-computational-modeling-study
#17
Xiaohan Zhang, Shenquan Liu, Feibiao Zhan, Jing Wang, Xiaofang Jiang
The damage of dopaminergic neurons that innervate the striatum has been considered to be the proximate cause of Parkinson's disease (PD). In the dopamine-denervated state, the loss of dendritic spines and the decrease of dendritic length may prevent medium spiny neuron (MSN) from receiving too much excitatory stimuli from the cortex, thereby reducing the symptom of Parkinson's disease. However, the reduction in dendritic spine density obtained by different experiments is significantly different. We developed a biological-based network computational model to quantify the effect of dendritic spine loss and dendrites tree degeneration on basal ganglia (BG) signal regulation...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29114215/feasibility-of-3d-reconstruction-of-neural-morphology-using-expansion-microscopy-and-barcode-guided-agglomeration
#18
Young-Gyu Yoon, Peilun Dai, Jeremy Wohlwend, Jae-Byum Chang, Adam H Marblestone, Edward S Boyden
We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29093676/bioinspired-technologies-to-connect-musculoskeletal-mechanobiology-to-the-person-for-training-and-rehabilitation
#19
REVIEW
Claudio Pizzolato, David G Lloyd, Rod S Barrett, Jill L Cook, Ming H Zheng, Thor F Besier, David J Saxby
Musculoskeletal tissues respond to optimal mechanical signals (e.g., strains) through anabolic adaptations, while mechanical signals above and below optimal levels cause tissue catabolism. If an individual's physical behavior could be altered to generate optimal mechanical signaling to musculoskeletal tissues, then targeted strengthening and/or repair would be possible. We propose new bioinspired technologies to provide real-time biofeedback of relevant mechanical signals to guide training and rehabilitation...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29093675/a-factor-graph-description-of-deep-temporal-active-inference
#20
Bert de Vries, Karl J Friston
Active inference is a corollary of the Free Energy Principle that prescribes how self-organizing biological agents interact with their environment. The study of active inference processes relies on the definition of a generative probabilistic model and a description of how a free energy functional is minimized by neuronal message passing under that model. This paper presents a tutorial introduction to specifying active inference processes by Forney-style factor graphs (FFG). The FFG framework provides both an insightful representation of the probabilistic model and a biologically plausible inference scheme that, in principle, can be automatically executed in a computer simulation...
2017: Frontiers in Computational Neuroscience
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