  The Hopfield network model and its generalizations were introduced as a model
of associative, or content-addressable, memory. They were widely investigated
both as a unsupervised learning method in artificial intelligence and as a
model of biological neural dynamics in computational neuroscience. The
complexity features of biological neural networks are attracting the interest
of scientific community since the last two decades. More recently, concepts and
tools borrowed from complex network theory were applied to artificial neural
networks and learning, thus focusing on the topological aspects. However, the
temporal structure is also a crucial property displayed by biological neural
networks and investigated in the framework of systems displaying complex
intermittency. The Intermittency-Driven Complexity (IDC) approach indeed
focuses on the metastability of self-organized states, whose signature is a
power-decay in the inter-event time distribution or a scaling behavior in the
related event-driven diffusion processes. The investigation of IDC in neural
dynamics and its relationship with network topology is still in its early
stages. In this work we present the preliminary results of a IDC analysis
carried out on a bio-inspired Hopfield-type neural network comparing two
different connectivities, i.e., scale-free vs. random network topology. We
found that random networks can trigger complexity features similar to that of
scale-free networks, even if with some differences and for different parameter
values, in particular for different noise levels.
