Supervised Machine Learning for Greedy Agglomeration in Connectomics
Report ID: TR-000-17Author: Kathpalia, Karan
Date: 2017-04-24
Pages: 27
Download Formats: |PDF|
Abstract:
This study explores the use of supervised machine learning methods for greedy agglomeration in the application of constructing connectomes or neural wiring diagrams that show how neurons are connected to each other. The current approach to this problem is mean affinity greedy agglomeration, which makes locally optimal merge/not-merge decisions. The rationale behind using supervised learning methods is that they may lead to locally optimal merge/not-merge decisions that are more globally optimal than those made by mean affinity agglomeration. The results of this study are inconclusive on whether supervised machine learning methods are better than mean affinity agglomeration because of evaluation issues. Future work should thus explore better methods of evaluating agglomeration performance. In addition, future work should address the specific weaknesses of the pipeline for producing connectomes: misalignment errors and errors where the dendritic spines do not grow out from the dendritic shafts.