Journal of Computational Neuroscience
Call for Papers: Special Issue on the Statistical Analysis of Neural Data
Models of Neural Systems: Mechanistic and statistical models are used to understand and explain observed data. Such models can also be used to estimate latent variables (other neural or behavioral signals) that correlate with measured data. For example state-space models are used to understand how latent variables (states) influence neural and behavioral measurements or to simply explain how and why control systems in the central nervous system operate the way they do. Papers that develop models to estimate latent signals or to explain observed phenomena are encouraged to submit for this topic.
Control of Neural Systems: Control theory is a field that entails the analysis of dynamical systems and the synthesis of controllers that actuate these systems to meet specific objectives (e.g. tracking a signal, rejecting disturbances, stabilizing an unstable system). Control theory has emerged as an important field in neuroscience because it has become possible to more easily manipulate the chemical and electrical patterns in the brain (the dynamical system to be controlled) with drugs that cross the blood brain barrier, electrical stimulation delivered through electrodes implanted into the brain, or via light delivered through optical fibers that excites genetically manipulated neurons. Papers addressing methods and/or applications to study (model) or manipulate neural systems with exogenous inputs using modeling are encouraged to submit for this topic.
Analysis of Neural Systems: Analysis of neurophysiological and behavioral data from neuroscience investigations is a fundamental task in computational and statistical neuroscience. The task can be challenging when the following one or more experimental conditions are present: (i) The dimensionality of the data are scaled up from an order of tens to hundreds or even larger; (ii) The data are either very noisy with a very low signal-to-noise ratio and/or exhibit high variability (across trials or time); (iii) There is an unknown relationship between neural recordings and measured behavior, especially at different temporal scales. Papers addressing methods and/or applications of methods to analyze neurophysiological and behavioral data are encouraged to submit for this topic.
Due Date: January 15, 2018
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.
Numerical optimization of coordinated reset stimulation for desynchronizing neuronal network dynamics
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