[2209.06865] Sketch of a novel approach to a neural model Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Neurons and Cognition arXiv:2209.06865 (q-bio) [Submitted on 14 Sep 2022 ( v1 ), last revised 24 Apr 2026 (this version, v9)] Title: Sketch of a novel approach to a neural model Authors: Gabriele Scheler View a PDF of the paper titled Sketch of a novel approach to a neural model, by Gabriele Scheler View PDF HTML (experimental) Abstract: We present an account of neuroplasticity with respect to cell-internal processing pathways in relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the complexity of neuroplasticity. In these accounts, the model is a network of neurons connected by adaptive transmission links. The adaptation of the transmission links relies on weight changes according to use of the transmission link (short-term and long-term potentiation/depression). In contrast, we propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on its history of use) to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by coupling strength. There is filtering and selection of signals for storage. Not every transmission event leaves a trace. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary, because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure. Subjects: Neurons and Cognition (q-bio.NC) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Molecular Networks (q-bio.MN) Cite as: arXiv:2209.06865 [q-bio.NC] (or arXiv:2209.06865v9 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2209.06865 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Gabriele Scheler [ view email ] [v1] Wed, 14 Sep 2022 18:28:39 UTC (493 KB) [v2] Sat, 8 Oct 2022 08:43:55 UTC (856 KB) [v3] Tue, 1 Nov 2022 13:20:18 UTC (4,365 KB) [v4] Thu, 10 Nov 2022 09:14:05 UTC (4,362 KB) [v5] Wed, 16 Nov 2022 13:39:09 UTC (4,362 KB) [v6] Thu, 5 Jan 2023 21:08:14 UTC (4,353 KB) [v7] Mon, 2 Mar 2026 09:23:53 UTC (1,225 KB) [v8] Wed, 15 Apr 2026 13:17:20 UTC (1,331 KB) [v9] Fri, 24 Apr 2026 07:47:10 UTC (1,001 KB) Full-text links: Access Paper: View a PDF of the paper titled Sketch of a novel approach to a neural model, by Gabriele Scheler View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.NC < prev | next > new | recent | 2022-09 Change to browse by: cond-mat cond-mat.dis-nn cs cs.AI cs.NE q-bio q-bio.MN References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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