Extraction of bouton-like structures from neuropil calcium imaging data. (English) Zbl 1526.92002
The authors propose a new method to decompose calcium imaging data of the neuropil into populations of bouton-like synaptic structures with a standard desktop computer. More precisely, the authors introduced a new type of modularity, a widely used quality measure in graph theory, and optimized the clustering configuration by a simulated annealing algorithm, which is established in statistical physics. Simulation showed the case where spatially overlapping boutons were grouped into a single cluster (see Fig. 8.D): a part of detected boutons in high spatial accuracy exhibited additional activity peaks that were not included in the correct signals but were originated from spatially overlapped neighboring boutons. It is shown that, the spatiotemporal population activity of boutons could be a novel approach to examining neural network dynamics at the cellular level.
Reviewer: Nikolay Kyurkchiev (Plovdiv)
MSC:
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 92C55 | Biomedical imaging and signal processing |
Keywords:
neuropil; calcium imaging; patch-wise modularity; pixel clustering; simulated annealing; scale-free networksSoftware:
CaImAnReferences:
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