Inferring Morphology of a Neuron from In Vivo LFP Data.
Int IEEE EMBS Conf Neural Eng
; 2021: 774-777, 2021 May.
Article
in En
| MEDLINE
| ID: mdl-35502315
ABSTRACT
We propose a computational pipeline that uses biophysical modeling and sequential neural posterior estimation algorithm to infer the position and morphology of single neurons using multi-electrode in vivo extracellular voltage recordings. In this inverse modeling scheme, we designed a generic biophysical single neuron model with stylized morphology that had adjustable parameters for the dimensions of the soma, basal and apical dendrites, and their location and orientations relative to the multi-electrode probe. Preliminary results indicate that the proposed methodology can infer up to eight neuronal parameters well. We highlight the issues involved in the development of the novel pipeline and areas for further improvement.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Int IEEE EMBS Conf Neural Eng
Year:
2021
Document type:
Article