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A growing body of research indicates that structural plasticity mechanismsare crucial for learning and memory consolidation. Starting from a simplephenomenological model, we exploit a mean-field approach to develop atheoretical framework of learning through this kind of plasticity, capable oftaking into account several features of the connectivity and pattern ofactivity of biological neural networks, including probability distributions ofneuron firing rates, selectivity of the responses of single neurons to multiplestimuli, probabilistic connection rules and noisy stimuli. More importantly, itdescribes the effects of consolidation, pruning and reorganization of synapticconnections. This framework will be used to compute the values of some relevantquantities used to characterize the learning and memory capabilities of theneuronal network in a training and validation procedure as the number oftraining patterns and other model parameters vary. The results will then becompared with those obtained through simulations with firing-rate-basedneuronal network models.

A theoretical framework for learning through structural plasticity