Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing

• URL: http://arxiv.org/abs/2609.06093v1
• Date: Sat, 05 Sep 2026 13:39:08 GMT
• Title: Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing
• Abstract summary: We propose a conditional generative latent framework that encodes connectome graphs into a compact structural space.<n>From this space, the model can reconstruct observed connectivity with a mean edge-reconstruction AUC up to 0.910.<n>Using connectome-derived graphs as recurrent computational substrates, we found that the learned latent space captures functional variation across reservoir-computing experiments.
• Score: 3.376576692516218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support function and computation. However, mapping connectome structure to computation remains difficult because these graphs are high-dimensional, sparse, and sensitive to local structural variation. Existing approaches often depend on hand-crafted structural descriptors or task-specific predictors, which limits their ability to represent connectomes in a form that is both generative and functionally meaningful. We propose a conditional generative latent framework that encodes connectome graphs into a compact structural space while using available node-level conditions to guide reconstruction and generation. From this space, the model can reconstruct observed connectivity with a mean edge-reconstruction AUC up to 0.910 and generate new candidate connectomes, enabling a unified analysis of graph structure and computational behavior. Using connectome-derived graphs as recurrent computational substrates, we found that the learned latent space captures functional variation across reservoir-computing experiments, with cross-validated $R^2$ values up to approximately 0.87. Interpretability analysis further revealed task-specific structural mechanisms: in our examples, memory performance is associated with reciprocal recurrent connectivity, whereas prediction and classification are more strongly associated with spectral properties of the recurrent network. These findings suggest an AI-for-science approach to linking neural connectivity to computation and provide a generative and interpretable basis for studying how distinct structural mechanisms shape computational capacity.

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