Abstract
This paper introduces a comprehensive mechanistic model of a neuron with plasticity that explains the creation of engrams, the biophysical correlates of memory. In the context of a single neuron, this means clarifying how information input as time-varying signals is processed, stored, and subsequently recalled. Moreover, the model addresses two additional, long-standing, specific biological problems: the integration of Hebbian and homeostatic plasticity, and the identification of a concise learning rule for synapses.
In this study, a biologically accurate Hodgkin-Huxley-style electric-circuit equivalent is derived through a one-to-one mapping from the known properties of ion channels. The dynamics of the synaptic cleft, which is often overlooked, is found to be 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, which is commonly used in signal processing. Simulation results confirm the circuit’s functionality, stability, and convergence, demonstrating that even a single neuron without external feedback can function as a potent signal processor.
The article is interdisciplinary and spans a broad range of subjects within the realm of biophysics, including neurobiology, electronics, and signal processing.
Significance statement Mechanistic 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 crucial 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 2023 terms Neuroplasticity, Learning, Memory, Synapses.
MeSH 2023 terms Neuronal Plasticity [G11.561.638], Association learning [F02.463.425.069.296], Memory [F02.463.425.540], Synaptic transmission [G02.111.820.850]
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵* The author declares no competing financial interests.
neuronplasticity{at}drnil.com
This version of the manuscript has been updated with numerous minor clarifications and corrections.
