Stable Learning in the Presence of Synaptic Variation

Recent studies have shown that synaptic spines undergo spontaneous size fluctuations, a phenomenon hypothesized to be critical for understanding synaptic plasticity and neural network stability. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. The model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then train an artificial neural network in which synaptic strengths fluctuate according to our model. This allows us to investigate how spontaneous spine dynamics interact with learning processes. Our simulations reveal that the experimentally observed lognormal distribution of spine sizes is compatible with effective learning, enabling the network to encode and retain information even in the presence of spontaneous synaptic changes. These results offer insights into the resilience and adaptability of neural networks, shedding light on how neuronal systems maintain functionality in the face of inherent biological variability.