Abstract

Structural plasticity, characterized by the formation and elimination of synapses, plays a big role in learning and long-term memory formation in the brain. The majority of the synapses in the neocortex occur between the axonal boutons and dendritic spines. Therefore, understanding the dynamics of the dendritic spine growth and elimination can provide key insights to the mechanisms of structural plasticity. In addition to learning and memory formation, the connectivity of neural networks affects cognition, perception, and behavior. Unsurprisingly, psychiatric and neurological disorders such as schizophrenia and autism are accompanied by pathological alterations in spine morphology and synapse numbers. Hence, it is vital to develop a model to understand the mechanisms governing dendritic spine dynamics throughout the lifetime. Here, we applied the density dependent Ricker population model to investigate the feasibility of ecological population concepts and mathematical foundations in spine dynamics. The model includes “immigration,” which is based on the filopodia type transient spines, and we show how this effect can potentially stabilize the spine population theoretically. For the long-term dynamics we employed a time dependent carrying capacity based on the brain's metabolic energy allocation. The results show that the mathematical model can explain the spine density fluctuations in the short-term and also account for the long term trends in the developing brain during synaptogenesis and pruning.

Highlights

  • Rewiring of biological neural networks via structural plasticity is a fundamental mechanism for continuous learning (Bremner, 2017)

  • Neural network activity has been modeled (Moreau et al, 1999; Burroni et al, 2017) by population ecology equations to describe the natural oscillations in neuronal networks

  • We investigate the feasibility of population ecology modeling for dendritic spine dynamics

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Summary

Introduction

Rewiring of biological neural networks via structural plasticity is a fundamental mechanism for continuous learning (Bremner, 2017). Compared to changes only in synaptic strength , structural network changes dramatically increases the memory capacity and flexibility (Chklovskii et al, 2004; Holtmaat and Svoboda, 2009). Dentritic spines are very active and their populations on dendrites are especially dynamic during activity and learning (Yasumatsu et al, 2008). Synaptic properties can change spontaneously (i.e., regardless of activity), reinforcing the “dynamic synapse” (Choquet and Triller, 2013) view of neuronal connections. In addition to the spontaneous changes in synaptic-strength, there is evidence for structural modifications on dendrites independent of activity (Cohen-Cory, 2002).

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