Modeling Neural Plasticity and Learning

Neural plasticity, the brain's remarkable ability to modify its structure and function in response to experience, serves as the fundamental substrate for learning and memory. This dynamic process spans a wide array of mechanisms, from the rapid changes in synaptic strength (synaptic plasticity) to the slower reorganization of physical connections (structural plasticity) and the stabilization of firing rates (homeostatic plasticity). In computational neuroscience, capturing these processes is essential for understanding how the brain adapts to an ever-changing environment. While classic models like Spike-Timing-Dependent Plasticity (STDP) have provided a strong foundation, the real-world complexity of neural circuits involves the simultaneous interaction of multiple plasticity rules across different timescales and brain states. As the field moves toward more sophisticated brain-inspired computing and deep learning, bridging the gap between biological plasticity and functional learning algorithms remains a significant challenge.The primary goal of this Research Topic is to address the current fragmentation in the modeling of neural plasticity. Many existing models focus on isolated mechanisms, often neglecting the synergistic or antagonistic interactions between different types of plasticity, such as the interplay between Hebbian learning and homeostatic scaling. Furthermore, a growing body of evidence suggests that plasticity and learning cannot be fully understood without considering the brain’s internal states, particularly the transition between wakefulness and sleep. Sleep is closely linked to memory, learning, and neural plasticity, and these processes are increasingly being studied through computational approaches. By broadening the scope to include sleep-related processes, we aim to explore how sleep supports memory consolidation, the role of sleep rhythms (such as slow waves and spindles) in plasticity, and how the interaction between the hippocampus and cortex during sleep facilitates the stabilization of memories.We seek contributions that propose novel mathematical frameworks or simulation-based studies investigating how various plasticity mechanisms cooperate to shape circuit function. We invite contributors to submit papers that utilize computational and theoretical methods to study neural plasticity across diverse modeling approaches, ranging from molecular-level simulations to abstract network-level learning rules.Specific themes for submission include, but are not limited to:- Integrated Multi-scale Plasticity: Models combining synaptic, homeostatic, and intrinsic plasticity mechanisms.- Structural Plasticity: Computational studies of dendritic spine dynamics, axonal remodeling, and circuit rewiring.- Neuromodulation of Learning: Modeling the role of dopamine, serotonin, and other signals in gating and reward-based learning.- Biologically Plausible Credit Assignment: New theories on how the brain approximates backpropagation-like learning in deep or recurrent architectures.- Sleep and Memory Consolidation: Computational models exploring how sleep helps stabilize memories, reduce forgetting, and support continual learning.- State-Dependent Plasticity: Differences in brain plasticity between wakefulness and sleep, including the distinct roles of REM and non-REM sleep.- Neural Oscillations and Plasticity: The role of sleep rhythms, such as slow-wave activity and sleep spindles, in shaping neural circuits.- System-Level Interactions: Interactions between the hippocampus and cortex during sleep-dependent consolidation.- Inhibitory Plasticity: The role of plastic inhibitory synapses in maintaining E-I balance and shaping receptive fields.- Functional Consequences: How specific plasticity rules lead to emergent behaviors, such as pattern separation, temporal sequence learning, or the prevention of catastrophic forgetting. Topic editors: Pawan Jha, Hanan Bouaouda Source: https://www.frontiersin.org/research-topics/82111/modeling-neural-plasticity-and-learning