TY - JOUR
T1 - The dynamics of spatial behavior
T2 - How can robust smoothing techniques help?
AU - Hen, Itay
AU - Sakov, Anat
AU - Kafkafi, Neri
AU - Golani, Ilan
AU - Benjamini, Yoav
N1 - Funding Information:
This study is part of the project “Phenotyping mouse exploratory behavior” supported by NIH 1 R01 NS40234-03. We thank two anonymous reviewers for substantial comments that helped improve this paper considerably. We thank Noldus Information Technology for the use of their EthoVision ® system in Tel-Aviv University. SEE Path Smoother and other SEE related programs can be downloaded at http://www.tau.ac.il/∼ilan99/see/help .
PY - 2004/3/15
Y1 - 2004/3/15
N2 - A variety of setups and paradigms are used in the neurosciences for automatically tracking the location of an animal in an experiment and for extracting features of interest out of it. Many of these features, however, are critically sensitive to the unavoidable noise and artifacts of tracking. Here, we examine the relevant properties of several smoothing methods and suggest a combination of methods for retrieving locations and velocities and recognizing arrests from time series of coordinates of an animal's center of gravity. We accomplish these by using robust nonparametric methods, such as Running Median (RM) and locally weighted regression methods. The smoothed data may, subsequently, be segmented to obtain discrete behavioral units with proven ethological relevance. New parameters such as the length, duration, maximal speed, and acceleration of these units provide a wealth of measures for, e.g., mouse behavioral phenotyping, studies on spatial orientation in vertebrates and invertebrates, and studies on rodent hippocampal function. This methodology may have implications for many tests of spatial behavior.
AB - A variety of setups and paradigms are used in the neurosciences for automatically tracking the location of an animal in an experiment and for extracting features of interest out of it. Many of these features, however, are critically sensitive to the unavoidable noise and artifacts of tracking. Here, we examine the relevant properties of several smoothing methods and suggest a combination of methods for retrieving locations and velocities and recognizing arrests from time series of coordinates of an animal's center of gravity. We accomplish these by using robust nonparametric methods, such as Running Median (RM) and locally weighted regression methods. The smoothed data may, subsequently, be segmented to obtain discrete behavioral units with proven ethological relevance. New parameters such as the length, duration, maximal speed, and acceleration of these units provide a wealth of measures for, e.g., mouse behavioral phenotyping, studies on spatial orientation in vertebrates and invertebrates, and studies on rodent hippocampal function. This methodology may have implications for many tests of spatial behavior.
KW - Exploratory behavior
KW - LOWESS
KW - Open field behavior
KW - Path smoothing
KW - Repeated Running Median
KW - Rodent
KW - Velocity
UR - http://www.scopus.com/inward/record.url?scp=0742272070&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2003.10.013
DO - 10.1016/j.jneumeth.2003.10.013
M3 - מאמר
C2 - 14757357
AN - SCOPUS:0742272070
VL - 133
SP - 161
EP - 172
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
IS - 1-2
ER -