Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
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| Format: | Preprint |
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2025
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| _version_ | 1866910001426071552 |
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| author | Liu, Xingyu Li, Yubin Chen, Guozhang |
| author_facet | Liu, Xingyu Li, Yubin Chen, Guozhang |
| contents | A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, to learn this underlying representation from detailed connectivity maps of mouse cortical microcircuits. Our model successfully captures the essential structural information of these circuits in a compressed latent space. We found that specific, interpretable directions within this space directly relate to understandable network properties. Building on this, we demonstrate a novel method to controllably generate new, synthetic microcircuits with desired structural features by navigating this latent space. This work offers a new way to investigate the design principles of neural circuits and explore how structure gives rise to function, potentially informing the development of more advanced artificial neural networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_11062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis Liu, Xingyu Li, Yubin Chen, Guozhang Neurons and Cognition Artificial Intelligence Neural and Evolutionary Computing A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, to learn this underlying representation from detailed connectivity maps of mouse cortical microcircuits. Our model successfully captures the essential structural information of these circuits in a compressed latent space. We found that specific, interpretable directions within this space directly relate to understandable network properties. Building on this, we demonstrate a novel method to controllably generate new, synthetic microcircuits with desired structural features by navigating this latent space. This work offers a new way to investigate the design principles of neural circuits and explore how structure gives rise to function, potentially informing the development of more advanced artificial neural networks. |
| title | Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis |
| topic | Neurons and Cognition Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2506.11062 |