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.

  • Alain Destexhe,
  • Jonathan Victor
Publishing model
Hybrid. Open Choice – What is this?
Impact factor: 1.568 (2018)
Five year impact factor: 1.712 (2018)
Submission to first decision: 40 days
Acceptance to publication: 25 days
Downloads: 84,739 (2018)


Societies, partners and affiliations


Note this is only the net price. Taxes will be calculated during checkout.
  • Immediate online access
  • Full Journal access includes all articles
  • Downloadable in PDF
  • Subscription expires 12/31/2019


About this journal

Electronic ISSN
Print ISSN
Abstracted and indexed in
  1. ACM Digital Library
  3. Biological Abstracts
  4. CAB Abstracts
  5. Current Contents/ Life Sciences
  6. DBLP
  7. EBSCO Discovery Service
  9. Elsevier Biobase
  10. Gale
  11. Gale Academic OneFile
  12. Gale InfoTrac
  13. Google Scholar
  14. INSPEC
  15. Institute of Scientific and Technical Information of China
  16. Japanese Science and Technology Agency (JST)
  17. Journal Citation Reports/Science Edition
  18. Mathematical Reviews
  19. Medline
  20. Meta
  21. Naver
  22. OCLC WorldCat Discovery Service
  23. ProQuest Advanced Technologies & Aerospace Database
  24. ProQuest Agricultural & Environmental Science Database
  25. ProQuest Biological Science Database
  26. ProQuest Biotechnology Research Abstracts
  27. ProQuest Central
  28. ProQuest Computer Science
  29. ProQuest Engineering
  30. ProQuest Environmental Science
  31. ProQuest Health & Medical Collection
  32. ProQuest Health Research Premium Collection
  33. ProQuest Materials Science and Engineering Database
  34. ProQuest Medical Database
  35. ProQuest Natural Science Collection
  36. ProQuest Neurosciences Abstracts
  37. ProQuest Pharma Collection
  38. ProQuest Psychology Database
  39. ProQuest SciTech Premium Collection
  40. ProQuest Technology Collection
  41. ProQuest-ExLibris Primo
  42. ProQuest-ExLibris Summon
  43. Reaxys
  44. SCImago
  45. SCOPUS
  46. Science Citation Index
  47. Science Citation Index Expanded (SciSearch)
  48. zbMATH
Copyright information

Rights and permissions

Springer Nature policies

© Springer Science+Business Media, LLC, part of Springer Nature