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Journal of Computational Neuroscience

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https://www.readbyqxmd.com/read/28271301/mechanisms-of-circumferential-gyral-convolution-in-primate-brains
#1
Tuo Zhang, Mir Jalil Razavi, Hanbo Chen, Yujie Li, Xiao Li, Longchuan Li, Lei Guo, Xiaoping Hu, Tianming Liu, Xianqiao Wang
Mammalian cerebral cortices are characterized by elaborate convolutions. Radial convolutions exhibit homology across primate species and generally are easily identified in individuals of the same species. In contrast, circumferential convolutions vary across species as well as individuals of the same species. However, systematic study of circumferential convolution patterns is lacking. To address this issue, we utilized structural MRI (sMRI) and diffusion MRI (dMRI) data from primate brains. We quantified cortical thickness and circumferential convolutions on gyral banks in relation to axonal pathways and density along the gray matter/white matter boundaries...
March 7, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/28236135/deriving-theoretical-phase-locking-values-of-a-coupled-cortico-thalamic-neural-mass-model-using-center-manifold-reduction
#2
Yutaro Ogawa, Ikuhiro Yamaguchi, Kiyoshi Kotani, Yasuhiko Jimbo
Cognitive functions such as sensory processing and memory processes lead to phase synchronization in the electroencephalogram or local field potential between different brain regions. There are a lot of computational researches deriving phase locking values (PLVs), which are an index of phase synchronization intensity, from neural models. However, these researches derive PLVs numerically. To the best of our knowledge, there have been no reports on the derivation of a theoretical PLV. In this study, we propose an analytical method for deriving theoretical PLVs from a cortico-thalamic neural mass model described by a delay differential equation...
February 24, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/28102460/neural-mass-models-as-a-tool-to-investigate-neural-dynamics-during-seizures
#3
Tatiana Kameneva, Tianlin Ying, Ben Guo, Dean R Freestone
Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value...
January 19, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27812835/anti-correlations-in-the-degree-distribution-increase-stimulus-detection-performance-in-noisy-spiking-neural-networks
#4
Marijn B Martens, Arthur R Houweling, Paul H E Tiesinga
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks...
February 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27726048/hierarchical-winner-take-all-particle-swarm-optimization-social-network-for-neural-model-fitting
#5
Brandon S Coventry, Aravindakshan Parthasarathy, Alexandra L Sommer, Edward L Bartlett
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons...
February 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27714569/optimal-nonlinear-cue-integration-for-sound-localization
#6
Brian J Fischer, Jose Luis Peña
Integration of multiple sensory cues can improve performance in detection and estimation tasks. There is an open theoretical question of the conditions under which linear or nonlinear cue combination is Bayes-optimal. We demonstrate that a neural population decoded by a population vector requires nonlinear cue combination to approximate Bayesian inference. Specifically, if cues are conditionally independent, multiplicative cue combination is optimal for the population vector. The model was tested on neural and behavioral responses in the barn owl's sound localization system where space-specific neurons owe their selectivity to multiplicative tuning to sound localization cues interaural phase (IPD) and level (ILD) differences...
February 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27704337/modeling-the-differentiation-of-a-and-c-type-baroreceptor-firing-patterns
#7
Jacob Sturdy, Johnny T Ottesen, Mette S Olufsen
The baroreceptor neurons serve as the primary transducers of blood pressure for the autonomic nervous system and are thus critical in enabling the body to respond effectively to changes in blood pressure. These neurons can be separated into two types (A and C) based on the myelination of their axons and their distinct firing patterns elicited in response to specific pressure stimuli. This study has developed a comprehensive model of the afferent baroreceptor discharge built on physiological knowledge of arterial wall mechanics, firing rate responses to controlled pressure stimuli, and ion channel dynamics within the baroreceptor neurons...
February 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27629590/twenty-years-of-modeldb-and-beyond-building-essential-modeling-tools-for-the-future-of-neuroscience
#8
REVIEW
Robert A McDougal, Thomas M Morse, Ted Carnevale, Luis Marenco, Rixin Wang, Michele Migliore, Perry L Miller, Gordon M Shepherd, Michael L Hines
Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them...
February 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27629491/artefactual-origin-of-biphasic-cortical-spike-lfp-correlation
#9
Michael Okun
Electrophysiological data acquisition systems introduce various distortions into the signals they record. While such distortions were discussed previously, their effects are often not appreciated. Here I show that the biphasic shape of cortical spike-triggered LFP average (stLFP), reported in multiple studies, is likely an artefact introduced by high-pass filter of the neural data acquisition system when the actual stLFP has a single trough around the zero lag.
February 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/28025784/multi-scale-detection-of-rate-changes-in-spike-trains-with-weak-dependencies
#10
Michael Messer, Kauê M Costa, Jochen Roeper, Gaby Schneider
The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs)...
December 26, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27942935/propagation-and-synchronization-of-reverberatory-bursts-in-developing-cultured-networks
#11
Chih-Hsu Huang, Yu-Ting Huang, Chun-Chung Chen, C K Chan
Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. Before the network matures, the activity level of a burst can reverberate in repeated rise-and-falls in periods of hundreds of milliseconds following an initial wave-like propagation of spiking activity, while the burst itself lasts for seconds. To investigate the spatiotemporal structure of the reverberatory bursts, we culture dissociated, rat cortical neurons on a high-density multi-electrode array to record the dynamics of neural activity over the growth and maturation of the network...
December 9, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27909842/the-relationship-between-nernst-equilibrium-variability-and-the-multifractality-of-interspike-intervals-in-the-hippocampus
#12
Stephen R Meier, Jarrett L Lancaster, Dustin Fetterhoff, Robert A Kraft, Robert E Hampson, Joseph M Starobin
Spatiotemporal patterns of action potentials are considered to be closely related to information processing in the brain. Auto-generating neurons contributing to these processing tasks are known to cause multifractal behavior in the inter-spike intervals of the output action potentials. In this paper we define a novel relationship between this multifractality and the adaptive Nernst equilibrium in hippocampal neurons. Using this relationship we are able to differentiate between various drugs at varying dosages...
December 1, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27909841/the-shaping-of-intrinsic-membrane-potential-oscillations-positive-negative-feedback-ionic-resonance-amplification-nonlinearities-and-time-scales
#13
Horacio G Rotstein
The generation of intrinsic subthreshold (membrane potential) oscillations (STOs) in neuronal models requires the interaction between two processes: a relatively fast positive feedback that favors changes in voltage and a slower negative feedback that opposes these changes. These are provided by the so-called resonant and amplifying gating variables associated to the participating ionic currents. We investigate both the biophysical and dynamic mechanisms of generation of STOs and how their attributes (frequency and amplitude) depend on the model parameters for biophysical (conductance-based) models having qualitatively different types of resonant currents (activating and inactivating) and an amplifying current...
December 1, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27650865/linking-dynamics-of-the-inhibitory-network-to-the-input-structure
#14
Maxim Komarov, Maxim Bazhenov
Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation...
December 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27624733/a-hidden-markov-model-for-decoding-and-the-analysis-of-replay-in-spike-trains
#15
Marc Box, Matt W Jones, Nick Whiteley
We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding)...
December 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27844245/a-model-of-signal-processing-at-the-isolated-hair-cell-of-the-frog-semicircular-canal
#16
Rita Canella, Marta Martini, Maria Lisa Rossi
A computational model has been developed to simulate the electrical behavior of the type II hair cell dissected from the crista ampullaris of frog semicircular canals. In its basolateral membrane, it hosts a system of four voltage-dependent conductances (g A , g KV , g KCa , g Ca ). The conductance behavior was mathematically described using original patch-clamp experimental data. The transient K current, IA, was isolated as the difference between the currents obtained before and after removing IA inactivation...
November 15, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27778248/analysis-of-the-dynamics-of-temporal-relationships-of-neural-activities-using-optical-imaging-data
#17
Jannetta S Steyn, Peter Andras
The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy...
October 24, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27696002/modelling-zinc-changes-at-the-hippocampal-mossy-fiber-synaptic-cleft
#18
M E Quinta-Ferreira, F D S Sampaio Dos Aidos, C M Matias, P J Mendes, J C Dionísio, R M Santos, L M Rosário, R M Quinta-Ferreira
Zinc, a transition metal existing in very high concentrations in the hippocampal mossy fibers from CA3 area, is assumed to be co-released with glutamate and to have a neuromodulatory role at the corresponding synapses. The synaptic action of zinc is determined both by the spatiotemporal characteristics of the zinc release process and by the kinetics of zinc binding to sites located in the cleft area, as well as by their concentrations. This work addresses total, free and complexed zinc concentration changes, in an individual synaptic cleft, following single, short and long periods of evoked zinc release...
October 1, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27515518/consistent-estimation-of-complete-neuronal-connectivity-in-large-neuronal-populations-using-sparse-shotgun-neuronal-activity-sampling
#19
Yuriy Mishchenko
We investigate the properties of recently proposed "shotgun" sampling approach for the common inputs problem in the functional estimation of neuronal connectivity. We study the asymptotic correctness, the speed of convergence, and the data size requirements of such an approach. We show that the shotgun approach can be expected to allow the inference of complete connectivity matrix in large neuronal populations under some rather general conditions. However, we find that the posterior error of the shotgun connectivity estimator grows quickly with the size of unobserved neuronal populations, the square of average connectivity strength, and the square of observation sparseness...
October 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27488433/interaction-between-synaptic-inhibition-and-glial-potassium-dynamics-leads-to-diverse-seizure-transition-modes-in-biophysical-models-of-human-focal-seizures
#20
E C Y Ho, Wilson Truccolo
How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K (+)] o ) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled: seizures characterized by sustained (∼30-60 Hz) gamma local field potential (LFP) oscillations; seizures where the onset in the propagated site consisted of LFP spikes that later evolved into rhythmic (∼2-3 Hz) spike-wave complexes (SWCs); and seizures where a brief stage of low-amplitude fast-oscillation (∼10-20 Hz) LFPs preceded the SWC activity...
October 2016: Journal of Computational Neuroscience
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