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In stark contrast, biological neural networks\ncontinually adapt to changing domains, possibly by leveraging complex molecular\nmachinery to solve many tasks simultaneously. In this study, we introduce\nintelligent synapses that bring some of this biological complexity into\nartificial neural networks. Each synapse accumulates task relevant information\nover time, and exploits this information to rapidly store new memories without\nforgetting old ones. We evaluate our approach on continual learning of\nclassification tasks, and show that it dramatically reduces forgetting while\nmaintaining computational efficiency.","url_abs":"http://arxiv.org/abs/1703.04200v3","url_pdf":"http://arxiv.org/pdf/1703.04200v3.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":"continual-learning-through-synaptic","repo_url":"https://github.com/ganguli-lab/pathint","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"continual-learning-through-synaptic","repo_url":"https://github.com/Minhchuyentoancbn/Continual-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"continual-learning-through-synaptic","repo_url":"https://github.com/chrhenning/hypercl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"continual-learning-through-synaptic","repo_url":"https://github.com/shriramsb/batchRL-SI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"continual-learning-through-synaptic","repo_url":"https://github.com/ContinualAI/avalanche","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"continual-learning-through-synaptic","repo_url":"https://github.com/aimagelab/mammoth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.04200"}},"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. 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