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Destexhe ; J. Journal no. 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. 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.

Bernstein Conference

Analysis of Neural Systems : Analysis of neurophysiological and behavioral data from neuroscience investigations is a fundamental task in computational and statistical neuroscience. 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.

#isiCNI - Computational Neuroscience Imbizo

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. You are not logged in! Please log in to edit your catalogs.

Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. 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 yielding insights into the function of the nervous system, are also welcomed in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields.

Examples of work that is not within the Journal's scope include i presentations of signal-processing algorithms that are purely methodological or for biomedical applications such as brain-computer interfaces or seizure detection, and ii computational analysis of genomic data without a clear tie-in to neural mechanisms of brain function. Prospective authors who are unsure of whether their manuscript is in the scope of the Journal are encouraged to contact one of the Editors in Chief prior to submission. The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors.

Transfer of copyright to Springer respective to owner if other than Springer becomes effective if and when a Copyright Transfer Statement is signed or transferred electronically by the corresponding author.

IBRO-SIMONS COMPUTATIONAL NEUROSCIENCE IMBIZO

After submission of the Copyright Transfer Statement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted by Springer. The copyright to this article, including any graphic elements therein e.

Machine Learning in Neuroscience

The copyright assignment includes without limitation the exclusive, assignable and sublicensable right, unlimited in time and territory, to reproduce, publish, distribute, transmit, make available and store the article, including abstracts thereof, in all forms of media of expression now known or developed in the future, including pre- and reprints, translations, photographic reproductions and microform. Springer may use the article in whole or in part in electronic form, such as use in databases or data networks for display, print or download to stationary or portable devices. This includes interactive and multimedia use and the right to alter the article to the extent necessary for such use.

Authors may self-archive the Author's accepted manuscript of their articles on their own websites. Authors may also deposit this version of the article in any repository, provided it is only made publicly available 12 months after official publication or later.

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Aims and Scope

Prerequisites Calculus and Differential equations at the level of a bachelor in physics, math, or electrical engineering. Interested in this course for your Business or Team?

Train your employees in the most in-demand topics, with edX for Business. Purchase now Request Information. About this course What happens in your brain when you make a decision?

And what happens if you recall a memory from your last vacation? Why is our perception of simple objects sometimes strangely distorted? How can millions of neurons in the brain work together without a central control unit? This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to answer the above questions.

The core of the answer to cognition may lie in the collective dynamics of thousands of interacting neurons - and these dynamics are mathematically analyzed in this course using methods such as mean-field theory and non-linear differential equations. Textbook: Neuronal Dynamics - from single neurons to networks and models of cognition W. Gerstner, W. Kistler, R.

Naud and L. Paninski , Cambridge Univ.