Summary: Identifying the “type” of a neuron has traditionally been like identifying a tree by its leaves—a manual, slow process based on shape (morphology). However, have flipped the script.
Their new AI system, Neuronal Type Assignment from Connectivity (NTAC), proves that a neuron’s “wiring diagram”—who it talks to—is a much more accurate fingerprint than its physical appearance. NTAC can classify thousands of neurons in minutes on a standard laptop with over 90% accuracy, even in brain regions where neurons look identical.
Key Facts
- Connectivity as DNA: NTAC demonstrates that synaptic connections alone contain enough information to identify a neuron’s type, making manual morphological sorting (which is often deceptive) obsolete.
- Superior Accuracy: In the fruit fly’s optic lobe—where neurons look very similar—NTAC achieved 90% accuracy, while traditional shape-based methods (like NBLAST) struggled to reach 50%.
- Speed and Efficiency: What once took experts months of manual labor can now be done in minutes on a conventional laptop.
- Two Operational Modes:
- Semi-Supervised: Uses a tiny fraction of pre-labeled neurons to “teach” the AI to identify the rest.
- Unsupervised: Clusters neurons into types based purely on wiring patterns without any prior labels (achieving ~70% accuracy in complex regions).
- The Genomic Parallel: Researchers compare this breakthrough to the rise of genomics; just as mapping the genome revolutionized medicine, mapping the “Connectome” could unlock how circuits fail in brain disorders.
Source: JAIST
Recent technological advances facilitate the reconstruction of complete brain connectomes in small organisms and partial connectomes in mammals, involving the mapping of the network of neurons and synaptic connections. Accurate cell typing of these connectomes aids in interpreting circuit functions and comparing brain organization across species.
Traditionally, cell typing relied on manual morphological classification by experts—a slow process that required detailed anatomical information. However, morphology can be deceptive or inadequate in many brain regions, especially in circuits with repeated cell types, where neurons can share very similar morphology despite differing in connectivity.
In a recently published study, researchers have developed Neuronal Type Assignment from Connectivity (NTAC), an automated system that delivers high-precision results and runs efficiently even on conventional computers, demonstrating that synaptic connectivity alone contains sufficient information to identify neuronal types without relying on morphological features.
The article is the result of an international collaboration between the Japan Advanced Institute of Science and Technology (JAIST), the Princeton Neuroscience Institute, the University of Edinburgh, and the Technical University of Catalonia. The research was led by Dr. Gregory Schwartzman, Associate Professor at JAIST, and also included Dr. Ben Jourdan from the University of Edinburgh, Dr. David García-Soriano from the Technical University of Catalonia, and Dr. Arie Matsliah from Princeton University.
The article was published in Volume 17 of Nature Communications on January 6, 2026. It was featured on the Editors’ Highlights page, which aims to showcase the top papers recently published in the area.
Explaining their research, Dr. Schwartzman says, “Our research comes in the context of expanding connectomes and a growing need for automatic and scalable tools. As connectome datasets grow, manual cell typing becomes a bottleneck.
“NTAC is capable of assigning neuronal types based exclusively on synaptic connectivity, with very high accuracy. It shows that the wiring diagram itself carries enough signal to identify neuron types quickly, even when only a small fraction of neurons is labeled.”
The researchers developed two operational modes for NTAC. One is a semi-supervised version, where a small fraction of neurons is pre-labeled, and the algorithm uses connectivity patterns to infer the types of the remaining neurons. In the unsupervised version, no prior labels are required; the algorithm groups neurons purely based on similarities in their wiring.
The algorithm was applied to multiple state-of-the-art fruit fly brain connectomes, and the accuracy of NTAC was compared to morphology-based approaches that rely on NBLAST, a widely used method for comparing neuronal shapes. In the optic lobe, a region where neurons tile space and are difficult to distinguish morphologically, NTAC substantially outperformed the NBLAST-based classifiers.
While morphology-based methods required many more labeled examples and still struggled to reach 50% accuracy in some settings, NTAC surpassed 90% with a fraction of the labeled data and in only minutes on a laptop.
In the fully unsupervised mode, NTAC achieved around 70% accuracy, far exceeding morphology-based clustering methods, which often remained below 10%. For the full brain, which contains thousands of unique cell types, unsupervised accuracy reached 52%, an encouraging result given the scale and complexity of the data.
“The long-term objective of connectomics is to map the complete human brain and derive scientific and medical insights from it, similar to how biology and medicine were revolutionized by genomics. Currently, complete connectomes have only been mapped for very small organisms such as fruit flies.
“NTAC can accelerate the creation and analysis of connectomes, potentially speeding scientific discovery, and, in the future, may contribute to efforts to classify neuronal cell types in large-scale mammalian and eventually human connectomes.
“This algorithm has already been applied effectively to label thousands of neurons in the brain-and-cord connectome (BANC) dataset. The next frontier in connectomics is mapping the mouse brain, and our algorithm can play a substantial role in this endeavor,” explained Dr. Schwartzman.
Further developing the algorithm by incorporating multimodal data can enhance classification performance and yield a more comprehensive understanding of neuronal cell types.
Funding information
Arie Matsliah was supported by funding provided through grants to Murthy and Seung from the NIH BRAIN Initiative (RF1 MH117815, RF1 MH129268, U24 NS126935). Gregory Schwartzman was supported by the following research grants: KAKENHI 25K00370, JST ASPIRE JPMJAP2302, and JST CRONOS JPMJCS24K2. Ben Jourdan was supported through an EPSRC Early Career Fellowship (EP/T00729). David García-Soriano was supported by the Spanish Agencia Estatal de Investigación AEI/10.13039/501100011033 (Project PID2020-112581GB-C21 MOTION).
Key Questions Answered:
A: Imagine two identical-looking electrical wires in your house. One connects to a light switch; the other connects to a doorbell. Looking at the wire itself won’t tell you its function—you have to see what it’s plugged into. In the brain’s “optic lobe,” neurons “tile” the space and look like clones of each other, but NTAC shows their unique “plugs” (synapses) reveal their true identity.
A: We are getting closer. Currently, we’ve only mapped tiny organisms like fruit flies. However, NTAC is designed to be scalable. As we move toward mapping the mouse brain and eventually the human brain (which has 86 billion neurons), automated tools like NTAC are the only way to process that massive amount of data.
A: Surprisingly, no. One of the biggest highlights of the study is that NTAC is highly efficient. It was designed to run on conventional computers (laptops), making high-level neuroscience accessible to more researchers worldwide without needing massive server farms.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this neuroscience research news
Author: Gregory Schwartzman
Source: JAIST
Contact: Gregory Schwartzman – JAIST
Image: The image is credited to Neuroscience News
Original Research: Open access.
“NTAC: Neuronal type assignment from connectivity” by Gregory Schwartzman, Ben Jourdan, David García-Soriano & Arie Matsliah. Nature Communications
DOI:10.1038/s41467-025-68044-1
Abstract
NTAC: Neuronal type assignment from connectivity
Recent advances in electron microscopy and computer vision now allow the reconstruction of complete wiring diagrams, or connectomes, of animal brains. This creates an urgent need for methods that can automatically identify neuronal cell types directly from these large connectivity datasets.
Here we show that synaptic connectivity alone can be used to assign neurons to cell types with high accuracy. We introduce NTAC (Neuronal Type Assignment from Connectivity), which groups neurons based only on connectivity.
NTAC has two forms: a semi-supervised one that leverages a small fraction of labeled neurons to infer the types of all others, and an unsupervised one that requires no labels at all.
Applied to multiple state-of-the-art fruit fly brain connectomes, NTAC achieves high accuracy within only minutes on a laptop, demonstrating that connectivity provides a powerful and scalable basis for classifying neuronal cell types across the brain.
