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However, networks based on memristive switching exhibit inherent limitations in dynamical richness and short-term memory capacity. Memcapacitors are expected to overcome these dynamical constraints through their topology-dependent dynamics. In this work, inspired by the distributed processing of neural systems, we endow Ag nanowire networks with memcapacitive properties via a temperature-modulated fabrication process, realizing a multi-path channel-dependent reservoir system with richer reservoir dynamics and establishing its circuit model. Benefiting from its memcapacitive nature, the system exhibits channel-specific responses and approximate linear superposition, enabling parallel and spontaneous fusion of multi-input signals with controllable and predictable behavior. This allows input states to expand exponentially with channel count. Leveraging this capability, we designed a multi-threshold encoding scheme that generates 256 highly separable states within four time steps while consuming only 18.98 pJ\/pulse. This approach reduces the information loss inherent in conventional binarization and delivers over 94% accuracy on a compressed MNIST recognition task, demonstrating a potent strategy for high-performance neuromorphic edge computing.<\/jats:p>","DOI":"10.1021\/acsami.6c12382","type":"journal-article","created":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T15:04:10Z","timestamp":1792249450000},"page":"57820-57832","source":"Crossref","is-referenced-by-count":0,"title":["Memcapacitive-Dominated Reservoir Computing with Self-Organizing Nanowire Networks"],"prefix":"10.1021","volume":"18","author":[{"given":"Haochen","family":"Wang","sequence":"first","affiliation":[{"name":"Anhui University , , , ,","place":["Hefei, Anhui, China, 230601"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6465-5570","authenticated-orcid":true,"given":"Zuheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Anhui University , , , 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