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Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail
AbstractReplicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that mod el replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve differe...
Source: Journal of Computational Neuroscience - October 31, 2018 Category: Neuroscience Source Type: research

An intracerebral exploration of functional connectivity during word production
AbstractLanguage is mediated by pathways connecting distant brain regions that have diverse functional roles. For word production, the network includes a ventral pathway, connecting temporal and inferior frontal regions, and a dorsal pathway, connecting parietal and frontal regions. Despite the importance of word production for scientific and clinical purposes, the functional connectivity underlying this task has received relatively limited attention, and mostly from techniques limited in either spatial or temporal resolution. Here, we exploited data obtained from depth intra-cerebral electrodes stereotactically implanted ...
Source: Journal of Computational Neuroscience - October 13, 2018 Category: Neuroscience Source Type: research

Predicting state transitions in brain dynamics through spectral difference of phase-space graphs
AbstractNetworks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This res...
Source: Journal of Computational Neuroscience - October 12, 2018 Category: Neuroscience Source Type: research

A numerical simulation of neural fields on curved geometries
AbstractDespite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of ne;urons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units. To illustrate our methods, we study localised activity patterns in a two-dimensional n...
Source: Journal of Computational Neuroscience - October 11, 2018 Category: Neuroscience Source Type: research

Linear-nonlinear-time-warp-poisson models of neural activity
AbstractProminent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to...
Source: Journal of Computational Neuroscience - October 8, 2018 Category: Neuroscience Source Type: research

A common goodness-of-fit framework for neural population models using marked point process time-rescaling
AbstractA critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more rece...
Source: Journal of Computational Neuroscience - October 8, 2018 Category: Neuroscience Source Type: research

Stability of point process spiking neuron models
AbstractPoint process regression models, based on generalized linear model (GLM) technology, have been widely used for spike train analysis, but a recent paper by Gerhard et al. described a kind of instability, in which fitted models can generate simulated spike trains with explosive firing rates. We analyze the problem by extending the methods of Gerhard et al. First, we improve their instability diagnostic and extend it to a wider class of models. Next, we point out some common situations in which instability can be traced to model lack of fit. Finally, we investigate distinctions between models that use a single filter ...
Source: Journal of Computational Neuroscience - September 15, 2018 Category: Neuroscience Source Type: research

Ensembles of change-point detectors: implications for real-time BMI applications
AbstractBrain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed “ensembles of change-point detectors” (ECPDs) integrate multiple decisions from independent detectors, which may be...
Source: Journal of Computational Neuroscience - September 12, 2018 Category: Neuroscience Source Type: research

Adjusted regularization of cortical covariance
AbstractIt is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an\(L_{1}\) penalty. However, the partial correlations found in neural spike count data a...
Source: Journal of Computational Neuroscience - September 6, 2018 Category: Neuroscience Source Type: research

Firing-rate models for neurons with a broad repertoire of spiking behaviors
AbstractCapturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate...
Source: Journal of Computational Neuroscience - August 27, 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 - August 1, 2018 Category: Neuroscience Source Type: research