{"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/tim-an-efficient-temporal-interaction-module","title":"TIM: An Efficient Temporal Interaction Module for Spiking Transformer","arxiv_id":"2401.11687","date":"2024-01-22","proceeding":null,"authors":["Sicheng Shen","Dongcheng Zhao","Guobin Shen","Yi Zeng"],"abstract":"Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in processing diverse datasets. The integration of attention mechanisms, inspired by advancements in neural network architectures, has led to the development of Spiking Transformers. These have shown promise in enhancing SNNs' capabilities, particularly in the realms of both static and neuromorphic datasets. Despite their progress, a discernible gap exists in these systems, specifically in the Spiking Self Attention (SSA) mechanism's effectiveness in leveraging the temporal processing potential of SNNs. To address this, we introduce the Temporal Interaction Module (TIM), a novel, convolution-based enhancement designed to augment the temporal data processing abilities within SNN architectures. TIM's integration into existing SNN frameworks is seamless and efficient, requiring minimal additional parameters while significantly boosting their temporal information handling capabilities. Through rigorous experimentation, TIM has demonstrated its effectiveness in exploiting temporal information, leading to state-of-the-art performance across various neuromorphic datasets. The code is available at https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/TIM.","url_abs":"https://arxiv.org/abs/2401.11687v3","url_pdf":"https://arxiv.org/pdf/2401.11687v3.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":"tim-an-efficient-temporal-interaction-module","repo_url":"https://github.com/braincog-x/brain-cog","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"tim-an-efficient-temporal-interaction-module","repo_url":"https://github.com/Fancyssc/Spiking-Transformers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[{"method_slug":"snn","method_name":"SNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2401.11687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11687"}},"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/Fancyssc/Spiking-Transformers","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/braincog-x/brain-cog","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/BrainCog-X/Brain-Cog","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":5,"unverified":2},"by_repo_kind":{"official":{"samples":7,"ran":5,"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":0,"samples":[{"code_sha256_prefix":"34b2b48215f91ce3","entry":"deriv_sigma","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/model_zoo/qsnn.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/model_zoo/qsnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"34b2b48215f91ce3"}},{"code_sha256_prefix":"403277d0bfbb477b","entry":"find_classes","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/datasets/TinyImageNet.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/datasets/TinyImageNet.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"403277d0bfbb477b"}},{"code_sha256_prefix":"ec18b3af081dcead","entry":"kappa","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/model_zoo/qsnn.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/model_zoo/qsnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ec18b3af081dcead"}},{"code_sha256_prefix":"5ded0879a886f3c4","entry":"make_dataset","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/datasets/TinyImageNet.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/datasets/TinyImageNet.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"5ded0879a886f3c4"}},{"code_sha256_prefix":"00a6c38b2c651cd6","entry":"sigma","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/model_zoo/qsnn.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/model_zoo/qsnn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"00a6c38b2c651cd6"}},{"code_sha256_prefix":"de955f3e3ead10d1","entry":"conv1x1","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/model_zoo/resnet.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/model_zoo/resnet.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"de955f3e3ead10d1"}},{"code_sha256_prefix":"71d699d243383ea6","entry":"conv3x3","repo":"BrainCog-X/Brain-Cog","repo_kind":"official","path":"braincog/model_zoo/resnet.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/model_zoo/resnet.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"71d699d243383ea6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}