Contribution to a conference proceedings/Contribution to a book

DZNE-2023-01140

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Hierarchical network analysis of behavior and neuronal population activity

Luxem, K. (First author)DZNE* ; Fuhrmann, F.DZNE* ; Remy, S.DZNE* ; Bauer, P. (Last author)DZNE*

2019
Cognitive Computational Neuroscience Brentwood, Tennessee, USA

This record in other databases:  

Please use a persistent id in citations: doi:10.32470/CCN.2019.1261-0

Abstract: Recording of neuronal population activity in behaving animals is becoming increasingly popular. Computationalmarkerless annotation tools allow for tracking of animal body-parts throughout the experiment. However, thequestion remains of how to cross-correlate the extractedbehavioral data with the simultaneously acquired neuronal population activity, when both datasets are of highdimensionality. Here we propose a combined analysis,where the behavioral data is clustered into discrete statesusing a deep learning model and the occurrence of eachstate is correlated to clusters of neuronal activity. Wethen model the relationship between behavioral states asa network, where related states are hierarchically groupedwhile the similarity between their neuronal correlates ismaximized. This type of analysis allows for hierarchicalexploration of the bidirectional relationship between behavior and its neuronal correlates at different temporalscales.

---

Contributing Institute(s):

1. Neuronal Networks (AG Remy)

Research Program(s):

1. 351 - Brain Function (POF4-351) (POF4-351)

Appears in the scientific report 2019

---

Database coverage:
;

---