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| Title | Authors | What’s new | Contributions | Methods, Tools, and Datasets | Evaluation / Gaps / Limitations | First-Principles Insights | Next Steps / Actionable Items |
|---|---|---|---|---|---|---|---|
| A Complete Electron Microscopy Volume of the Brain | |||||||
| of Adult Drosophila melanogaster | Zhihao Zheng, J. Scott Lauritzen |
Year: 2018 Read: Mar 30th | | traced brainspanning circuitry involving the mushroom body | a new type of electron microscopy with a custom high-throughput EM platform | EM tells different story with LM | now we have FlyWire and this to build GNN, but this does not have clear node so far as i concerned | | | | | | | | | | |
of Adult Drosophila melanogaster
Abstract:
Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brainspanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience.
—neural circuit mapping at synaptic resolution
—traced brainspanning circuitry involvig MB
—find new cell types and unexpected clustering of some neurons
🧠 Attractor Dynamics in the Brain — A Visual Introduction
Attractor networks are models of how the brain maintains stable or smoothly changing activity patterns. They help explain memory, navigation, motor control, and even the sense of self. Here are three types of attractors, visualized through simulations:
1. 🔲 Discrete Attractor Network (DAN)
• Represents a system that snaps to one of a few stable patterns.
• Useful for memory recall, decision-making, and pattern completion.
• When given partial or noisy input, the network retrieves the closest stored pattern.
📷 Graph: Snap-to-pattern dynamics over time