Connectionists: Call for Papers: Special Issue on the Statistical Analysis of Neural Data

[Byron Yu]

Journal of Computational Neuroscience

Call for Papers: Special Issue on the Statistical Analysis of Neural Data
Due Date: December 1, 2017
Website: http://www.springer.com/biomed/neuroscience/journal/10827

Topics

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.