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Comput. Eng."],"published-print":{"date-parts":[[2025,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In an era characterized by the rapid growth of data processing, developing new and efficient information processing technologies has become a priority. We address this by proposing a novel type of neuromorphic technology we call Fused-MemBrain. Our proposal is inspired by Golgi\u2019s theory modeling the brain as a syncytial continuum, in contrast to Cajal\u2019s theory of neurons and synapses being discrete elements. While Cajal\u2019s theory has long been the dominant and experimentally validated view of the nervous system, recent discoveries showed that a species of marine invertebrate (ctenophore <jats:italic>Mnemiopsis leidyi<\/jats:italic>) may be better described by Golgi\u2019s theory. The core idea is to develop hardware that functions analogously to a syncytial network, exploiting self-assembled memristive systems and combining them with complementary metal oxide semiconductor (CMOS) technologies, interfacing with the silicon back-end-of-line. In this way, a memristive self-assembled material can cheaply and efficiently replace the synaptic connections between CMOS neuron implementations in neuromorphic hardware, enhancing the capability of massively parallel computation. The fusion of CMOS circuits with a memristive \u2018plexus\u2019 allows information transfer without requiring engineered synapses, which typically consume significant area. As the first step toward this ambitious goal, we present a simulation of a memristive network interfaced with spiking neural networks. Additionally, we describe the potential benefits of such a system, along with key technical aspects it should incorporate.<\/jats:p>","DOI":"10.1088\/2634-4386\/adc6b5","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T23:02:56Z","timestamp":1743202976000},"page":"024002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Fused-MemBrain: a spiking processor combining CMOS and self-assembled memristive networks"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4350-8691","authenticated-orcid":true,"given":"Davide","family":"Cipollini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3716-3992","authenticated-orcid":true,"given":"Hugh","family":"Greatorex","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2850-8079","authenticated-orcid":false,"given":"Michele","family":"Mastella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5518-8990","authenticated-orcid":true,"given":"Elisabetta","family":"Chicca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2351-930X","authenticated-orcid":true,"given":"Lambert","family":"Schomaker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"article-title":"Carbon emissions and large neural network training","year":"2021","author":"Patterson","key":"nceadc6b5bib1"},{"article-title":"World energy outlook 2024 \u2013 analysis - IEA \u2014 iea.org","year":"2024","author":"","key":"nceadc6b5bib2"},{"key":"nceadc6b5bib3","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1016\/j.joule.2023.09.004","article-title":"The growing energy footprint of artificial intelligence","volume":"7","author":"de Vries","year":"2023","journal-title":"Joule"},{"key":"nceadc6b5bib4","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1038\/530144a","article-title":"The chips are down for moore\u2019s law","volume":"530","author":"Waldrop","year":"2016","journal-title":"Nat. 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