Journal of Computational Neuroscience This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Analysis of the dynamics of temporal relationships of neural activities using optical imaging data
AbstractThe 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. Here we propose a numerical...
Source: Journal of Computational Neuroscience - October 23, 2016 Category: Neuroscience Source Type: research

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting
AbstractParticle 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. We show that the winner-take-all network perf...
Source: Journal of Computational Neuroscience - October 9, 2016 Category: Neuroscience Source Type: research

Optimal nonlinear cue integration for sound localization
AbstractIntegration 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...
Source: Journal of Computational Neuroscience - October 5, 2016 Category: Neuroscience Source Type: research

Modeling the differentiation of A- and C-type baroreceptor firing patterns
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. With this model, we were able to predict firing rates observed in previously published experiments in both A- and C-type neurons. These results were obtained by adjusting model parameters determining the maximal ion-channel conductances. The observed variation in the model parameters are hypothesized to correspond to physiological differences between A- and C-type neur...
Source: Journal of Computational Neuroscience - October 4, 2016 Category: Neuroscience Source Type: research

Modelling zinc changes at the hippocampal mossy fiber synaptic cleft
AbstractZinc, 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. The result...
Source: Journal of Computational Neuroscience - September 30, 2016 Category: Neuroscience Source Type: research

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system
AbstractMuscle 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 a ntagonist wrist simulator...
Source: Journal of Computational Neuroscience - September 26, 2016 Category: Neuroscience Source Type: research

Linking dynamics of the inhibitory network to the input structure
AbstractNetworks 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. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal ...
Source: Journal of Computational Neuroscience - September 20, 2016 Category: Neuroscience Source Type: research

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience
AbstractNeuron 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 scient ific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics....
Source: Journal of Computational Neuroscience - September 14, 2016 Category: Neuroscience Source Type: research

Artefactual origin of biphasic cortical spike-LFP correlation
AbstractElectrophysiological 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. (Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - September 13, 2016 Category: Neuroscience Source Type: research

A hidden Markov model for decoding and the analysis of replay in spike trains
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 algor ithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolutio...
Source: Journal of Computational Neuroscience - September 12, 2016 Category: Neuroscience Source Type: research

Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks
AbstractA 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. This contrasts with well-studied dynamical perturbations that lead to divergent network activity i...
Source: Journal of Computational Neuroscience - September 1, 2016 Category: Neuroscience Source Type: research

A negative group delay model for feedback-delayed manual tracking performance
AbstractWe 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 de...
Source: Journal of Computational Neuroscience - August 16, 2016 Category: Neuroscience Source Type: research

Consistent estimation of complete neuronal connectivity in large neuronal populations using sparse “shotgun” neuronal activity sampling
We present a numerical approach for solving the shotgun estimat ion problem in general settings and use it to demonstrate the shotgun connectivity inference in the examples of simulated synfire and weakly coupled cortical neuronal networks. (Source: Journal of Computational Neuroscience)
Source: Journal of Computational Neuroscience - August 10, 2016 Category: Neuroscience Source Type: research

Multiple timescale mixed bursting dynamics in a respiratory neuron model
AbstractExperimental results in rodent medullary slices containing the pre-B ötzinger complex (pre-BötC) have identified multiple bursting mechanisms based on persistent sodium current (INaP) and intracellular Ca2+. The classic two-timescale approach to the analysis of pre-B ötC bursting treats the inactivation ofINaP, the calcium concentration, as well as the Ca2+-dependent inactivation of IP3 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-B ötC neurons, in...
Source: Journal of Computational Neuroscience - August 4, 2016 Category: Neuroscience Source Type: research

Interaction between synaptic inhibition and glial-potassium dynamics leads to diverse seizure transition modes in biophysical models of human focal seizures
AbstractHow 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 spi...
Source: Journal of Computational Neuroscience - August 2, 2016 Category: Neuroscience Source Type: research