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Numerical optimization of coordinated reset stimulation for desynchronizing neuronal network dynamics
In this study, we perform numerical optimization to find the energy-op timal current waveform for desynchronizing neuronal network with CR stimulation, by proposing and applying a new optimization method based on the direct search algorithm. In the proposed optimization method, the stimulating current is described as a Fourier series, and each Fourier coefficient as we ll as the stimulation period are directly optimized by evaluating the order parameter, which quantifies the synchrony level, from network simulation. This direct optimization scheme has an advantage that arbitrary changes in the dynamical properties of the n...
Source: Journal of Computational Neuroscience - August 1, 2018 Category: Neuroscience Source Type: research

A model of motor and sensory axon activation in the median nerve using surface electrical stimulation
AbstractSurface electrical stimulation has the potential to be a powerful and non-invasive treatment for a variety of medical conditions but currently it is difficult to obtain consistent evoked responses. A viable clinical system must be able to adapt to variations in individuals to produce repeatable results. To more fully study the effect of these variations without performing exhaustive testing on human subjects, a system of computer models was created to predict motor and sensory axon activation in the median nerve due to surface electrical stimulation at the elbow. An anatomically-based finite element model of the ar...
Source: Journal of Computational Neuroscience - August 1, 2018 Category: Neuroscience Source Type: research

A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands
AbstractWe propose a method - Frequency extracted hierarchical decomposition (FEHD) - for studying multivariate time series that identifies linear combinations of its components that possess a causally hierarchical structure - the method orders the components so that those at the “top” of the hierarchy drive those below. The method shares many of the features of the “hierarchical decomposition” method of Repucci et al. (Annals of Biomedical Engineering,29, 1135 –1149,2001) but makes a crucial advance - the proposed method is capable of determining this causal hierarchy over arbitrarily specified frequency bands. ...
Source: Journal of Computational Neuroscience - July 30, 2018 Category: Neuroscience Source Type: research

A model of motor and sensory axon activation in the median nerve using surface electrical stimulation
AbstractSurface electrical stimulation has the potential to be a powerful and non-invasive treatment for a variety of medical conditions but currently it is difficult to obtain consistent evoked responses. A viable clinical system must be able to adapt to variations in individuals to produce repeatable results. To more fully study the effect of these variations without performing exhaustive testing on human subjects, a system of computer models was created to predict motor and sensory axon activation in the median nerve due to surface electrical stimulation at the elbow. An anatomically-based finite element model of the ar...
Source: Journal of Computational Neuroscience - June 26, 2018 Category: Neuroscience Source Type: research

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise
AbstractSpontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the ...
Source: Journal of Computational Neuroscience - June 19, 2018 Category: Neuroscience Source Type: research

Numerical optimization of coordinated reset stimulation for desynchronizing neuronal network dynamics
In this study, we perform numerical optimization to find the energy-op timal current waveform for desynchronizing neuronal network with CR stimulation, by proposing and applying a new optimization method based on the direct search algorithm. In the proposed optimization method, the stimulating current is described as a Fourier series, and each Fourier coefficient as we ll as the stimulation period are directly optimized by evaluating the order parameter, which quantifies the synchrony level, from network simulation. This direct optimization scheme has an advantage that arbitrary changes in the dynamical properties of the n...
Source: Journal of Computational Neuroscience - June 7, 2018 Category: Neuroscience Source Type: research

Convolutional neural network models of V1 responses to complex patterns
In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized linear models. We then systematically dissected different components of the CNN and found two key factors that made CNNs outperform other models: thresholding nonlinearity and convolution. In addition, we fitted our data using a pre-trained deep CNN via transfer learning. The deep CNN ’s higher layers, which ...
Source: Journal of Computational Neuroscience - June 5, 2018 Category: Neuroscience Source Type: research

Analytical modelling of temperature effects on an AMPA-type synapse
AbstractIt was previously reported, that temperature may significantly influence neural dynamics on the different levels of brain function. Thus, in computational neuroscience, it would be useful to make models scalable for a wide range of various brain temperatures. However, lack of experimental data and an absence of temperature-dependent analytical models of synaptic conductance does not allow to include temperature effects at the multi-neuron modeling level. In this paper, we propose a first step to deal with this problem: A new analytical model of AMPA-type synaptic conductance, which is able to incorporate temperatur...
Source: Journal of Computational Neuroscience - June 1, 2018 Category: Neuroscience Source Type: research

The effect of inhibition on the existence of traveling wave solutions for a neural field model of human seizure termination
AbstractIn this paper we study the influence of inhibition on an activity-based neural field model consisting of an excitatory population with a linear adaptation term that directly regulates the activity of the excitatory population. Such a model has been used to replicate traveling wave data as observed in high density local field potential recordings (Gonz ález-Ramírez et al.PLoS Computational Biology,11(2), e1004065,2015). In this work, we show that by adding an inhibitory population to this model we can still replicate wave properties as observed in human clinical data preceding seizure termination, but the paramete...
Source: Journal of Computational Neuroscience - June 1, 2018 Category: Neuroscience Source Type: research

The role of phase shifts of sensory inputs in walking revealed by means of phase reduction
In this study, we employed phase reduction and averaging theory to this large network model in order to reduce it to a system of coupled phase oscillators. This enabled us to analyze the complex behavior of the system in a reduced parameter space. In this paper, we show that the reduced model reproduces the results of the original model. By analyzing the interaction of just two coupled phase oscillators, we found that the neighboring CPGs could operate within distinct regimes, depending on the phase shift between the sensory inputs from the extremities and the phases of the individual CPGs. We demonstrate that this depende...
Source: Journal of Computational Neuroscience - June 1, 2018 Category: Neuroscience Source Type: research

Phase model-based neuron stabilization into arbitrary clusters
AbstractDeep brain stimulation (DBS) is a common method of combating pathological conditions associated with Parkinson ’s disease, Tourette syndrome, essential tremor, and other disorders, but whose mechanisms are not fully understood. One hypothesis, supported experimentally, is that some symptoms of these disorders are associated with pathological synchronization of neurons in the basal ganglia and thalamus. For this reason, there has been interest in recent years in finding efficient ways to desynchronize neurons that are both fast-acting and low-power. Recent results on coordinated reset and periodically forced oscil...
Source: Journal of Computational Neuroscience - June 1, 2018 Category: Neuroscience Source Type: research

An integrate-and-fire model to generate spike trains with long-range dependence
AbstractLong-range dependence (LRD) has been observed in a variety of phenomena in nature, and for several years also in the spiking activity of neurons. Often, this is interpreted as originating from a non-Markovian system. Here we show that a purely Markovian integrate-and-fire (IF) model, with a noisy slow adaptation term, can generate interspike intervals (ISIs) that appear as having LRD. However a proper analysis shows that this is not the case asymptotically. For comparison, we also consider a new model of individual IF neuron with fractional (non-Markovian) noise. The correlations of its spike trains are studied and...
Source: Journal of Computational Neuroscience - June 1, 2018 Category: Neuroscience Source Type: research

Synaptic efficacy shapes resource limitations in working memory
AbstractWorking memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space. Our analysis relates the neural architectur...
Source: Journal of Computational Neuroscience - June 1, 2018 Category: Neuroscience Source Type: research