{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-time-invariant-representations-for","title":"Learning Time-Invariant Representations for Individual Neurons from Population Dynamics","arxiv_id":"2311.02258","date":"2023-11-03","proceeding":"NeurIPS 2023 11","authors":["Lu Mi","Trung Le","Tianxing He","Eli Shlizerman","Uygar Sümbül"],"abstract":"Neurons can display highly variable dynamics. While such variability presumably supports the wide range of behaviors generated by the organism, their gene expressions are relatively stable in the adult brain. This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit. Here, we propose a self-supervised learning based method to assign time-invariant representations to individual neurons based on permutation-, and population size-invariant summary of population recordings. We fit dynamical models to neuronal activity to learn a representation by considering the activity of both the individual and the neighboring population. Our self-supervised approach and use of implicit representations enable robust inference against imperfections such as partial overlap of neurons across sessions, trial-to-trial variability, and limited availability of molecular (transcriptomic) labels for downstream supervised tasks. We demonstrate our method on a public multimodal dataset of mouse cortical neuronal activity and transcriptomic labels. We report > 35% improvement in predicting the transcriptomic subclass identity and > 20% improvement in predicting class identity with respect to the state-of-the-art.","url_abs":"https://arxiv.org/abs/2311.02258v1","url_pdf":"https://arxiv.org/pdf/2311.02258v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-time-invariant-representations-for","repo_url":"https://github.com/lumimim/neuprint","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2311.02258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.02258"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/lumimim/NeuPRINT","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lumimim/neuprint","reach":{"status":"ok"}}],"summary":{"ran":9,"ran_fixture":2,"unverified":1},"by_repo_kind":{"official":{"samples":12,"ran":11,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":12,"samples":[{"code_sha256_prefix":"c7df3c3c23f7713d","entry":"PositionalEncoding","repo":"lumimim/neuprint","repo_kind":"official","path":"dynamical_models.py","file_url":"https://github.com/lumimim/neuprint/blob/HEAD/dynamical_models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c7df3c3c23f7713d"}},{"code_sha256_prefix":"df1a186afb283a27","entry":"count_nonan","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"data_preprocess.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/data_preprocess.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"df1a186afb283a27"}},{"code_sha256_prefix":"8e15e2cba7d6d161","entry":"extract_train_with_valid_test","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"data_split.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/data_split.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8e15e2cba7d6d161"}},{"code_sha256_prefix":"9e63d9d49d63b691","entry":"find_unique","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"data_preprocess.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/data_preprocess.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9e63d9d49d63b691"}},{"code_sha256_prefix":"0f7ed4f21a555c3c","entry":"generate_context_mask","repo":"lumimim/neuprint","repo_kind":"official","path":"dynamical_models.py","file_url":"https://github.com/lumimim/neuprint/blob/HEAD/dynamical_models.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0f7ed4f21a555c3c"}},{"code_sha256_prefix":"9ac2f2f0fc796527","entry":"interpolate_nans","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"data_preprocess.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/data_preprocess.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9ac2f2f0fc796527"}},{"code_sha256_prefix":"e0654ea3213297bf","entry":"logLikelihoodGaussian","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"loss_function.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/loss_function.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e0654ea3213297bf"}},{"code_sha256_prefix":"0f63788db52f629a","entry":"logLikelihoodPoisson","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"loss_function.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/loss_function.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0f63788db52f629a"}},{"code_sha256_prefix":"df83b0e5f148c9a5","entry":"mask_batch","repo":"lumimim/neuprint","repo_kind":"official","path":"dynamical_models.py","file_url":"https://github.com/lumimim/neuprint/blob/HEAD/dynamical_models.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"df83b0e5f148c9a5"}},{"code_sha256_prefix":"2e91a0aa058296be","entry":"mse_loss","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"loss_function.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/loss_function.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2e91a0aa058296be"}},{"code_sha256_prefix":"4cfd5faff34b333e","entry":"split_train_valid_test","repo":"lumimim/NeuPRINT","repo_kind":"official","path":"data_split.py","file_url":"https://github.com/lumimim/NeuPRINT/blob/HEAD/data_split.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4cfd5faff34b333e"}},{"code_sha256_prefix":"6dc221c604a9d6eb","entry":"time_invariant_permutation_invariant_transformer_recon","repo":"lumimim/neuprint","repo_kind":"official","path":"dynamical_models.py","file_url":"https://github.com/lumimim/neuprint/blob/HEAD/dynamical_models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6dc221c604a9d6eb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}