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Frontiers in Computational Neuroscience

Takumi Sase, Yuichi Katori, Motomasa Komuro, Kazuyuki Aihara
We investigate a discrete-time network model composed of excitatory and inhibitory neurons and dynamic synapses with the aim at revealing dynamical properties behind oscillatory phenomena possibly related to brain functions. We use a stochastic neural network model to derive the corresponding macroscopic mean field dynamics, and subsequently analyze the dynamical properties of the network. In addition to slow and fast oscillations arising from excitatory and inhibitory networks, respectively, we show that the interaction between these two networks generates phase-amplitude cross-frequency coupling (CFC), in which multiple different frequency components coexist and the amplitude of the fast oscillation is modulated by the phase of the slow oscillation...
2017: Frontiers in Computational Neuroscience
Alexander Reyes, Christopher M Laine, Jason J Kutch, Francisco J Valero-Cuevas
During force production, hand muscle activity is known to be coherent with activity in primary motor cortex, specifically in the beta-band (15-30 Hz) frequency range. It is not clear, however, if this coherence reflects the control strategy selected by the nervous system for a given task, or if it instead reflects an intrinsic property of cortico-spinal communication. Here, we measured corticomuscular and intermuscular coherence between muscles of index finger and thumb while a two-finger pinch grip of identical net force was applied to objects which were either stable (allowing synergistic activation of finger muscles) or unstable (requiring individuated finger control)...
2017: Frontiers in Computational Neuroscience
Taegyo Kim, Khaldoun C Hamade, Dmitry Todorov, William H Barnett, Robert A Capps, Elizaveta M Latash, Sergey N Markin, Ilya A Rybak, Yaroslav I Molkov
It is widely accepted that the basal ganglia (BG) play a key role in action selection and reinforcement learning. However, despite considerable number of studies, the BG architecture and function are not completely understood. Action selection and reinforcement learning are facilitated by the activity of dopaminergic neurons, which encode reward prediction errors when reward outcomes are higher or lower than expected. The BG are thought to select proper motor responses by gating appropriate actions, and suppressing inappropriate ones...
2017: Frontiers in Computational Neuroscience
Seungmoon Song, Hartmut Geyer
Neuromechanical simulations have been used to study the spinal control of human locomotion which involves complex mechanical dynamics. So far, most neuromechanical simulation studies have focused on demonstrating the capability of a proposed control model in generating normal walking. As many of these models with competing control hypotheses can generate human-like normal walking behaviors, a more in-depth evaluation is required. Here, we conduct the more in-depth evaluation on a spinal-reflex-based control model using five representative gait disturbances, ranging from electrical stimulation to mechanical perturbation at individual leg joints and at the whole body...
2017: Frontiers in Computational Neuroscience
Alberto Testolin, Michele De Filippo De Grazia, Marco Zorzi
The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations...
2017: Frontiers in Computational Neuroscience
Marek Rudnicki, Werner Hemmert
Globular bushy cells (GBCs) located in the ventral cochlear nucleus are an essential part of the sound localization pathway in the mammalian auditory system. They receive inputs directly from the auditory nerve and are particularly sensitive to temporal cues due to their synaptic and membrane specializations. GBCs act as coincidence detectors for incoming spikes through large synapses-endbulbs of Held-which connect to their soma. Since endbulbs of Held are an integral part of the auditory information conveying and processing pathway, they were extensively studied...
2017: Frontiers in Computational Neuroscience
Ricardo P Monti, Romy Lorenz, Peter Hellyer, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana
An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification...
2017: Frontiers in Computational Neuroscience
Kesheng Xu, Jean P Maidana, Mauricio Caviedes, Daniel Quero, Pablo Aguirre, Patricio Orio
In this article, we describe and analyze the chaotic behavior of a conductance-based neuronal bursting model. This is a model with a reduced number of variables, yet it retains biophysical plausibility. Inspired by the activity of cold thermoreceptors, the model contains a persistent Sodium current, a Calcium-activated Potassium current and a hyperpolarization-activated current (Ih) that drive a slow subthreshold oscillation. Driven by this oscillation, a fast subsystem (fast Sodium and Potassium currents) fires action potentials in a periodic fashion...
2017: Frontiers in Computational Neuroscience
David A Peterson, Terrence J Sejnowski
Blepharospasm (sometimes called "benign essential blepharospasm," BEB) is one of the most common focal dystonias. It involves involuntary eyelid spasms, eye closure, and increased blinking. Despite the success of botulinum toxin injections and, in some cases, pharmacologic or surgical interventions, BEB treatments are not completely efficacious and only symptomatic. We could develop principled strategies for preventing and reversing the disease if we knew the pathogenesis of primary BEB. The objective of this study was to develop a conceptual framework and dynamic circuit hypothesis for the pathogenesis of BEB...
2017: Frontiers in Computational Neuroscience
Marko Wilke, Mekibib Altaye, Scott K Holland
Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous...
2017: Frontiers in Computational Neuroscience
Fang Han, Zhijie Wang, Hong Fan
This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy consumption is composed of two parts: neuronal basic energy consumption and neuronal spike emission energy consumption. A parameter to model the relative importance of energy consumption is introduced in the definition of the information efficiency...
2017: Frontiers in Computational Neuroscience
Francesca Pittau, Lorenzo Ferri, Firas Fahoum, François Dubeau, Jean Gotman
Purpose: To examine the ability of the BOLD response to EEG spikes to assess the epileptogenicity of the lesion in patients with focal cortical dysplasia (FCD). Method: Patients with focal epilepsy and FCD who underwent 3T EEG-fMRI from 2006 to 2010 were included. Diagnosis of FCD was based on neuroradiology (MRI+), or histopathology in MRI-negative cases (MRI-). Patients underwent 120 min EEG-fMRI recording session. Spikes similar to those recorded outside the scanner were marked in the filtered EEG. The lesion (in MRI+) or the removed cortex (in MRI-) was marked on the anatomical T1 sequence, blindly to the BOLD response, after reviewing the FLAIR images...
2017: Frontiers in Computational Neuroscience
Irene Elices, Pablo Varona
Central Pattern Generator (CPG) circuits are neural networks that generate rhythmic motor patterns. These circuits are typically built of half-center oscillator subcircuits with reciprocally inhibitory connections. Another common property in many CPGs is the remarkable rich spiking-bursting dynamics of their constituent cells, which balance robustness and flexibility to generate their joint coordinated rhythms. In this paper, we use conductance-based models and realistic connection topologies inspired by the crustacean pyloric CPG to address the study of asymmetry factors shaping CPG bursting rhythms...
2017: Frontiers in Computational Neuroscience
Umut Güçlü, Marcel A J van Gerven
Encoding models are used for predicting brain activity in response to sensory stimuli with the objective of elucidating how sensory information is represented in the brain. Encoding models typically comprise a nonlinear transformation of stimuli to features (feature model) and a linear convolution of features to responses (response model). While there has been extensive work on developing better feature models, the work on developing better response models has been rather limited. Here, we investigate the extent to which recurrent neural network models can use their internal memories for nonlinear processing of arbitrary feature sequences to predict feature-evoked response sequences as measured by functional magnetic resonance imaging...
2017: Frontiers in Computational Neuroscience
Marije Ter Wal, Paul H Tiesinga
Communication between cortical sites is mediated by long-range synaptic connections. However, these connections are relatively static, while everyday cognitive tasks demand a fast and flexible routing of information in the brain. Synchronization of activity between distant cortical sites has been proposed as the mechanism underlying such a dynamic communication structure. Here, we study how oscillatory activity affects the excitability and input-output relation of local cortical circuits and how it alters the transmission of information between cortical circuits...
2017: 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
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