{"id":"doi:10.5281/zenodo.23067549","kind":"article","title":"Learning is robust, seeing it is hard: attractor dynamics gate the readout of synaptic memory in a whole-brain spiking connectome model","authors":["Andrey A. Smarygin"],"author_count":1,"year":2026,"published":"2026-09-30","venue":"Zenodo (CERN European Organization for Nuclear Research)","cited_by":0,"doi":"10.5281/zenodo.23067549","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.5281/zenodo.23067549","archive_url":"https://space-kitty.com/papers/doi:10.5281/zenodo.23067549","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W7214947410","pdf_url":null,"abstract":"No published work demonstrates reproducible associative learning inside a full connectome-constrained spiking model of the Drosophila brain; independent community attempts consistently report negative results. We show that the obstacle is not the learning rule but the readout. In the MaleCNS connectome (166,700 neurons, brain+VNC), a host-side dopaminergic depression of Kenyon-cell-to-MBON synapses writes robust, odour-specific synaptic memory after a single pairing (N=1), with consolidation that survives interference (protected-fraction ratio 1.737, 7/8 seeds, p=0.0039), a cross-seed depression-pattern cosine of 0.880, and an exactly zero no-reward control (8/8). A pre-registered ablation shows that type-specific spike-frequency adaptation is not required to write this memory (weight-level addressing is comparable within seed variability; 2.7x more edges are updated without it) but is required to see it: on the activity readout the same learning is dominated by attractor-basin switching (SPEC scatter 0.28-10.12). Even after physiological stabilization, the learned signal remains gated downstream: it decodes from synaptic weights (0.71-0.78) and from the postsynaptic drive (0.97-0.99) but only weakly from spikes (0.31-0.64), and seven candidate release mechanisms are negated with pre-registered controls. Degree-preserving null models abolish learning, working memory and benchmark performance, proving the effects are topologically constrained. The canon does not transfer to the FlyWire v783 connectome (connectome-specificity boundary). The framework parsimoniously explains the community's learning failures and defines the physiology required to read out synaptic memory in whole-brain models. Project channel: «Правила игры» — https://t.me/law_of_the_game","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Neurobiology and Insect Physiology Research"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Learning is robust, seeing it is hard: attractor dynamics gate the readout of synaptic memory in a whole-brain spiking connectome model\" by Andrey A. Smarygin, https://doi.org/10.5281/zenodo.23067549, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"connectomics","name":"Connectomics & Brain Mapping","domain_slug":"neuroscience"}],"cited_in_dossiers":[]}