Modeling the impact of protein statistics on spontaneous and plasticity-induced synaptic dynamics

Abstract

The fundamental question of how the brain encodes, processes, and retrieves stored information remains a central, unresolved challenge in neuroscientific research. This question holds great importance for a number of reasons, from the purest philosophical and scientific charm, to the plethora of technological applications its answer could bring, the most notable possibly being medical treatment. Understanding, on a mechanistic, molecular level, the specific role a particular biochemical factor plays in synaptic plasticity and, in general, neuronal activity, is the first step towards its control and, ultimately, towards recovering it from malfunction. <br/> Synaptic plasticity is regarded as one of the central mechanisms responsible for neuronal information encoding and, consequently, the emergence of anything that can be considered repeatable and investigable behaviour. Governed by an elaborate interplay of biochemical components, it exhibits strikingly rich dynamical features, with multi-scale changes occurring both across different spines and over time — a possible signature of memory formation. To dissect the common origin, as well as the general theoretical implications of this variability, my work investigates synaptic plasticity through the lens of mathematical modeling. Focusing on the remarkable experimental observations obtained in the last decades, computational models are used to provide a novel perspective on the mechanisms driving synaptic plasticity, gaining insight not only into average, deterministic synaptic change but also into the substantial variability that characterizes this change across experimental trials. <br/> This thesis is organized as follows: <br/> Chapter 1 provides a comprehensive overview of synaptic plasticity. Following a brief historical introduction, I will introduce the synaptic plasticity concept, focusing on its strong compatibility with a biological basis of memory formation and learning. Subsequently, I will present the primary biochemical factors driving synaptic plasticity, with a brief discussion of their individual functions and mutual interactions. <br/> In Chapter 2, I introduce my first original contribution, which consists of a stochastic, descriptive model of synaptic size statistics. Building upon experimental observations, an equation describing synaptic fluctuations is constructed from fundamental principles, developed incrementally, and then utilized to identify possible "governing principles" of spontaneous synaptic fluctuations, as well as their relation with external, potentiation-inducing, synaptic stimulation. <br/> In Chapter 3, I present my second and main original contribution formulated within the reaction-diffusion framework. A second model is proposed, fitted to the data kindly provided by our experimental collaborators (T.E. Chater, Y. Goda), and used to mechanistically explain several, apparently contradictory plasticity features, from the variable spatio-temporal dynamics, to the inverse relationship between synaptic size and tendency to potentiate, to the bivalent, dose-dependent effects of FK506 on synaptic plasticity. <br/> The thesis concludes with Chapter 4, which presents a comprehensive summary of results alongside the primary research directions that could extend these findings in future investigations.