Special Issue from the 2017 International Conference on Mathematical Neuroscience

Zachary P. Kilpatrick; Julijana Gjorgjieva; Robert Rosenbaum

doi:10.1186/s13408-018-0069-5

Special Issue from the 2017 International Conference on Mathematical Neuroscience

Zachary P. Kilpatrick, Julijana Gjorgjieva, Robert Rosenbaum

Research output: Contribution to journal › Editorial

Abstract

The ongoing acquisition of large and multifaceted data sets in neuroscience requires new mathematical tools for quantitatively grounding these experimental findings. Since 2015, the International Conference on Mathematical Neuroscience (ICMNS) has provided a forum for researchers to discuss current mathematical innovations emerging in neuroscience. This special issue assembles current research and tutorials that were presented at the 2017 ICMNS held in Boulder, Colorado from May 30 to June 2. Topics discussed at the meeting include correlation analysis of network activity, information theory for plastic synapses, combinatorics for attractor neural networks, and novel data assimilation methods for neuroscience—all of which are represented in this special issue.

Original language	English
Article number	1
Journal	Journal of Mathematical Neuroscience
Volume	9
Issue number	1
DOIs	https://doi.org/10.1186/s13408-018-0069-5
State	Published - 1 Dec 2019
Externally published	Yes

Access to Document

10.1186/s13408-018-0069-5

Cite this

@article{f53c1ac56d8746589dc325ba104ace3d,

title = "Special Issue from the 2017 International Conference on Mathematical Neuroscience",

abstract = "The ongoing acquisition of large and multifaceted data sets in neuroscience requires new mathematical tools for quantitatively grounding these experimental findings. Since 2015, the International Conference on Mathematical Neuroscience (ICMNS) has provided a forum for researchers to discuss current mathematical innovations emerging in neuroscience. This special issue assembles current research and tutorials that were presented at the 2017 ICMNS held in Boulder, Colorado from May 30 to June 2. Topics discussed at the meeting include correlation analysis of network activity, information theory for plastic synapses, combinatorics for attractor neural networks, and novel data assimilation methods for neuroscience—all of which are represented in this special issue.",

author = "Kilpatrick, {Zachary P.} and Julijana Gjorgjieva and Robert Rosenbaum",

note = "Publisher Copyright: {\textcopyright} 2019, The Author(s).",

year = "2019",

month = dec,

day = "1",

doi = "10.1186/s13408-018-0069-5",

language = "English",

volume = "9",

journal = "Journal of Mathematical Neuroscience",

issn = "2190-8567",

publisher = "Springer Verlag",

number = "1",

}

TY - JOUR

T1 - Special Issue from the 2017 International Conference on Mathematical Neuroscience

AU - Kilpatrick, Zachary P.

AU - Gjorgjieva, Julijana

AU - Rosenbaum, Robert

PY - 2019/12/1

Y1 - 2019/12/1

N2 - The ongoing acquisition of large and multifaceted data sets in neuroscience requires new mathematical tools for quantitatively grounding these experimental findings. Since 2015, the International Conference on Mathematical Neuroscience (ICMNS) has provided a forum for researchers to discuss current mathematical innovations emerging in neuroscience. This special issue assembles current research and tutorials that were presented at the 2017 ICMNS held in Boulder, Colorado from May 30 to June 2. Topics discussed at the meeting include correlation analysis of network activity, information theory for plastic synapses, combinatorics for attractor neural networks, and novel data assimilation methods for neuroscience—all of which are represented in this special issue.

AB - The ongoing acquisition of large and multifaceted data sets in neuroscience requires new mathematical tools for quantitatively grounding these experimental findings. Since 2015, the International Conference on Mathematical Neuroscience (ICMNS) has provided a forum for researchers to discuss current mathematical innovations emerging in neuroscience. This special issue assembles current research and tutorials that were presented at the 2017 ICMNS held in Boulder, Colorado from May 30 to June 2. Topics discussed at the meeting include correlation analysis of network activity, information theory for plastic synapses, combinatorics for attractor neural networks, and novel data assimilation methods for neuroscience—all of which are represented in this special issue.

UR - http://www.scopus.com/inward/record.url?scp=85059610583&partnerID=8YFLogxK

U2 - 10.1186/s13408-018-0069-5

DO - 10.1186/s13408-018-0069-5

M3 - Editorial

C2 - 30626600

AN - SCOPUS:85059610583

SN - 2190-8567

VL - 9

JO - Journal of Mathematical Neuroscience

JF - Journal of Mathematical Neuroscience

IS - 1

M1 - 1

ER -

Special Issue from the 2017 International Conference on Mathematical Neuroscience

Abstract

Access to Document

Other files and links

Fingerprint

Cite this