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Journal of Computational Neuroscience

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
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...
October 10, 2016: Journal of Computational Neuroscience
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...
October 6, 2016: Journal of Computational Neuroscience
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...
October 5, 2016: Journal of Computational Neuroscience
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
Bernard Grandjean, Marc A Maier
Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator...
September 27, 2016: Journal of Computational Neuroscience
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...
September 21, 2016: Journal of Computational Neuroscience
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...
September 15, 2016: Journal of Computational Neuroscience
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.
September 14, 2016: Journal of Computational Neuroscience
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)...
September 13, 2016: Journal of Computational Neuroscience
Philippe Vincent-Lamarre, Guillaume Lajoie, Jean-Philippe Thivierge
A large body of experimental and theoretical work on neural coding suggests that the information stored in brain circuits is represented by time-varying patterns of neural activity. Reservoir computing, where the activity of a recurrently connected pool of neurons is read by one or more units that provide an output response, successfully exploits this type of neural activity. However, the question of system robustness to small structural perturbations, such as failing neurons and synapses, has been largely overlooked...
September 2, 2016: Journal of Computational Neuroscience
Henning U Voss, Nigel Stepp
We propose that feedback-delayed manual tracking performance is limited by fundamental constraints imposed by the physics of negative group delay. To test this hypothesis, the results of an experiment in which subjects demonstrate both reactive and predictive dynamics are modeled by a linear system with delay-induced negative group delay. Although one of the simplest real-time predictors conceivable, this model explains key components of experimental observations. Most notably, it explains the observation that prediction time linearly increases with feedback delay, up to a certain point when tracking performance deteriorates...
August 17, 2016: Journal of Computational Neuroscience
Yangyang Wang, Jonathan E Rubin
Experimental results in rodent medullary slices containing the pre-Bötzinger complex (pre-BötC) have identified multiple bursting mechanisms based on persistent sodium current (I NaP) and intracellular Ca(2+). The classic two-timescale approach to the analysis of pre-BötC bursting treats the inactivation of I NaP, the calcium concentration, as well as the Ca(2+)-dependent inactivation of IP 3 as slow variables and considers other evolving quantities as fast variables. Based on its time course, however, it appears that a novel mixed bursting (MB) solution, observed both in recordings and in model pre-BötC neurons, involves at least three timescales...
August 5, 2016: Journal of Computational Neuroscience
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
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
Bahar Moezzi, Nicolangelo Iannella, Mark D McDonnell
Neural spike trains are commonly characterized as a Poisson point process. However, the Poisson assumption is a poor model for spiking in auditory nerve fibres because it is known that interspike intervals display positive correlation over long time scales and negative correlation over shorter time scales. We have therefore developed a biophysical model based on the well-known Meddis model of the peripheral auditory system, to produce simulated auditory nerve fibre spiking statistics that more closely match the firing correlations observed in empirical data...
October 2016: Journal of Computational Neuroscience
Sora Ahn, Sang Beom Jun, Hyang Woon Lee, Seungjun Lee
In this paper, we propose a comprehensive computational model that is able to reproduce three epileptiform activities. The model targets a hippocampal formation that is known to be an important lesion in medial temporal lobe epilepsy. It consists of four sub-networks consisting of excitatory and inhibitory neurons and well-known signal pathways, with consideration of propagation delay. The three epileptiform activities involve fast and slow interictal discharge and ictal discharge, and those activities can be induced in vitro by application of 4-Aminopyridine in entorhinal cortex combined hippocampal slices...
October 2016: Journal of Computational Neuroscience
Sarah Fribance, Jicheng Wang, James R Roppolo, William C de Groat, Changfeng Tai
Recent studies indicate that a rapid increase in local temperature plays an important role in nerve stimulation by laser. To analyze the temperature effect, our study modified the classical HH axonal model by incorporating a membrane capacitance-temperature relationship. The modified model successfully simulated the generation and propagation of action potentials induced by a rapid increase in local temperature when the Curie temperature of membrane capacitance is below 40 °C, while the classical model failed to simulate the axonal excitation by temperature stimulation...
October 2016: Journal of Computational Neuroscience
Vito Di Maio, Francesco Ventriglia, Silvia Santillo
Coexistence of AMPA and NMDA receptors in glutamatergic synapses leads to a cooperative effect that can be very complex. This effect is dependent on many parameters including the relative and absolute number of the two types of receptors and biophysical parameters that can vary among synapses of the same cell. Herein we simulate the AMPA/NMDA cooperativity by using different number of the two types of receptors and considering the effect of the spine resistance on the EPSC production. Our results show that the relative number of NMDA with respect to AMPA produces a different degree of cooperation which depends also on the spine resistance...
October 2016: Journal of Computational Neuroscience
Nicholas Altieri, Cheng-Ta Yang
One of the most common examples of audiovisual speech integration is the McGurk effect. As an example, an auditory syllable /ba/ recorded over incongruent lip movements that produce "ga" typically causes listeners to hear "da". This report hypothesizes reasons why certain clinical and listeners who are hard of hearing might be more susceptible to visual influence. Conversely, we also examine why other listeners appear less susceptible to the McGurk effect (i.e., they report hearing just the auditory stimulus without being influenced by the visual)...
October 2016: Journal of Computational Neuroscience
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