| Autor: |
Angela Zhang, S. Shailja, Cezar Borba, Yishen Miao, Michael Goebel, Raphael Ruschel, Kerrianne Ryan, William Smith, B.S. Manjunath |
| Rok vydání: |
2021 |
| Popis: |
This paper presents a deep-learning based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) EM images. Identifying synapses from electron microscopy (EM) images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse detection and classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Activation maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables prediction of neurotransmitter types for neurons in Ciona which were previously unknown. The prediction model with code is available on Github. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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