TY - JOUR
T1 - Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain
AU - McCullough, Michael H.
AU - Goodhill, Geoffrey J.
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
AB - Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
UR - http://www.scopus.com/inward/record.url?scp=85114127160&partnerID=8YFLogxK
U2 - 10.1016/j.conb.2021.07.014
DO - 10.1016/j.conb.2021.07.014
M3 - Review article
C2 - 34482006
AN - SCOPUS:85114127160
SN - 0959-4388
VL - 70
SP - 89
EP - 100
JO - Current Opinion in Neurobiology
JF - Current Opinion in Neurobiology
ER -