A modular theoretical framework for learning through structural plasticity

Abstract

It is known that, during learning, modifications in synaptic transmission and, eventually, structural changes of the connectivity take place in our brain. This can be achieved through a mechanism known as structural plasticity. In this work, starting from a simple phenomenological model, we exploit a mean-field approach to develop a modular theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules and noisy stimuli. More importantly, it describes the effects of consolidation, pruning and reorganization of synaptic connections. This framework will be used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in a training and validation procedure as the number of training patterns and other model parameters vary. The results will then be compared with those obtained through simulations with firing-rate-based neuronal network models