Sketch of a novel approach to a neural model
This is a conceptual advance for neuroscience and AI modeling, though it remains a position paper without empirical validation.
The authors propose shifting from synapse-centric to neuron-centric neural models, arguing that traditional weight-based approaches inadequately capture neuroplasticity's complexity by introducing internal neuronal parameters for memory and signal processing.
In this position paper, we present biological detail about 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. The adaptation of these transmission links is overly simplified in the standard model of short-term and long-term potentiation or depression assuming weight adaptation according to use. We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane). Each neuron has a 'vertical' dimension where internal parameters steer the external membrane- and synapse-expressed parameters. 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 neuron in the horizontal network has its own internal memory. Transmission and memory are separate, not linked by strict use-dependence. There is filtering and selection of signals for processing and storage. Not every transmission event leaves a trace. This is a conceptual advance over synaptic weight models. The neuron is a self-programming device, rather than a transfer function determined by input. A new approach to neural modeling is better able to capture experimental evidence than synapse-centric models.