Mechanistic explanation of neuroplasticity using equivalent circuits

This paper presents a comprehensive mechanistic model of a neuron with plasticity that explains how information input as time-varying signals is processed and stored. Additionally, the model addresses two long-standing, specific biological challenges: Integrating Hebbian and homeostatic plasticity, and identifying a concise synaptic learning rule. A biologically accurate electric-circuit equivalent is derived through a one-to-one mapping from the known properties of ion channels. The often-overlooked dynamics of the synaptic cleft is essential in this process. Analysis of the model reveals a simple and succinct learning rule, indicating that the neuron functions as an internal-feedback adaptive filter, a common concept in signal processing. Simulations confirm the models functionality, stability, and convergence, demonstrating that even a single neuron without external feedback can act as a potent signal processor. The model replicates several key characteristics typical of biological neurons, which are seldom captured in other neuron models. It can encode time-varying functions, learn without risking instability, and bootstrap from a state where all synaptic weights are zero. This paper explores the function of neurons with a focus on biological accuracy, not computational efficiency. Unlike neuromorphic models, it does not aim to design devices. The electronic circuit analogy aids understanding by leveraging decades of electronics expertise but is not intended for physical implementation. This interdisciplinary work spans a broad range of subjects within the realm of neurobiophysics, including neurobiology, electronics, and signal processing. Significance statementMechanistic neuron models with plasticity are crucial for understanding the complexities of the brain and the processes behind learning and memory. These models provide a way to study how individual neurons and synapses in the brain change over time in response to stimuli, allowing for a more nuanced understanding of neuronal circuits and assemblies. Plasticity is a key aspect of these models, as it represents the ability of the brain to modify its connections and functions in response to experiences. By incorporating plasticity into these models, researchers can explore how changes at the synaptic level contribute to higher-level changes in behavior and cognition. Thus, these models are essential for advancing our understanding of the brain and its functions. PhySH termsNeuroplasticity, Neural basis of learning and memory, Synapses. MeSH 2023 termsNeuronal Plasticity [G11.561.638], Association learning [F02.463.425.069.296], Memory [F02.463.425.540], Synaptic transmission [G02.111.820.850]