In this position paper, we present a biological account of neuroplasticity with respect to cell-internal processing pathways and their relation to membrane and synaptic plasticity. We believe that traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. In standard accounts, a neuronal network consists of a network of neurons connected by adaptive transmission links. Each neuron has a 'vertical' dimension where internal parameters steer the external membrane- and synapse-expressed parameters. In contrast to this, we propose a paradigm switch from a synapse-centric model 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 horizontal network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by associative coupling strength. There is filtering and selection of signals for processing and storage. 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.

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