{"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/high-performance-evolutionary-algorithms-for","title":"High-performance Evolutionary Algorithms for Online Neuron Control","arxiv_id":"2204.06765","date":"2022-04-14","proceeding":null,"authors":["Binxu Wang","Carlos R. Ponce"],"abstract":"Recently, optimization has become an emerging tool for neuroscientists to study neural code. In the visual system, neurons respond to images with graded and noisy responses. Image patterns eliciting highest responses are diagnostic of the coding content of the neuron. To find these patterns, we have used black-box optimizers to search a 4096d image space, leading to the evolution of images that maximize neuronal responses. Although genetic algorithm (GA) has been commonly used, there haven't been any systematic investigations to reveal the best performing optimizer or the underlying principles necessary to improve them. Here, we conducted a large scale in silico benchmark of optimizers for activation maximization and found that Covariance Matrix Adaptation (CMA) excelled in its achieved activation. We compared CMA against GA and found that CMA surpassed the maximal activation of GA by 66% in silico and 44% in vivo. We analyzed the structure of Evolution trajectories and found that the key to success was not covariance matrix adaptation, but local search towards informative dimensions and an effective step size decay. Guided by these principles and the geometry of the image manifold, we developed SphereCMA optimizer which competed well against CMA, proving the validity of the identified principles. Code available at https://github.com/Animadversio/ActMax-Optimizer-Dev","url_abs":"https://arxiv.org/abs/2204.06765v1","url_pdf":"https://arxiv.org/pdf/2204.06765v1.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":"high-performance-evolutionary-algorithms-for","repo_url":"https://github.com/animadversio/actmax-optimizer-dev","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"ga","method_name":"GA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.06765","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06765"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/animadversio/actmax-optimizer-dev","reach":null}],"summary":{"ran_draft_wrong":3,"ran_honours":1},"by_repo_kind":{"official":{"samples":4,"ran":4,"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":4,"samples":[{"code_sha256_prefix":"03a3b48ad6b1e6a0","entry":"mate","repo":"animadversio/actmax-optimizer-dev","repo_kind":"official","path":"core/Optimizers.py","file_url":"https://github.com/animadversio/actmax-optimizer-dev/blob/HEAD/core/Optimizers.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"03a3b48ad6b1e6a0"}},{"code_sha256_prefix":"32564fbad862f4ae","entry":"mutate","repo":"animadversio/actmax-optimizer-dev","repo_kind":"official","path":"core/Optimizers.py","file_url":"https://github.com/animadversio/actmax-optimizer-dev/blob/HEAD/core/Optimizers.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"32564fbad862f4ae"}},{"code_sha256_prefix":"754bece31da2c0bb","entry":"normalize","repo":"animadversio/actmax-optimizer-dev","repo_kind":"official","path":"tutorials/GAN_geometry_demo.py","file_url":"https://github.com/animadversio/actmax-optimizer-dev/blob/HEAD/tutorials/GAN_geometry_demo.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"754bece31da2c0bb"}},{"code_sha256_prefix":"b85320d809b19a37","entry":"rankweight","repo":"animadversio/actmax-optimizer-dev","repo_kind":"official","path":"core/Optimizers.py","file_url":"https://github.com/animadversio/actmax-optimizer-dev/blob/HEAD/core/Optimizers.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b85320d809b19a37"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}