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Journal of Computational Neuroscience

Journal of Computational Neuroscience OnlineFirst articles


Stability of point process spiking neuron models

Point 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 …


Ensembles of change-point detectors: implications for real-time BMI applications

Brain-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 …


Adjusted regularization of cortical covariance

It 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 …

27.08.2018 Open Access

Firing-rate models for neurons with a broad repertoire of spiking behaviors

Capturing 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 …


A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands

We 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 …

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The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily, theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods with the potential to yield insights into the function of the nervous system, are also welcomed. It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience. However, papers that are primarily devoted to new methods or analyses should demonstrate their utility for the investigation of mechanisms or principles of neural function.

Neuroscience Peer Review Consortium
The Journal is pleased to be a member of the Neuroscience Peer Review Consortium (NPRC), an alliance of neuroscience journals that have agreed to share manuscript reviews at the author's request.

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