1. What are the different structural and spectral characteristics emergent from networks with differing synaptic plasticity paradigms?
2. Given specific probability distributions of network activity, what is the range of possible architectural qualities achieveable with given plasticity rules?
3. What are the optimal parameters of the graph-theory-plasticity framework (e.g. of the probability distribution of network activity, learning rates, node proximities etc.) in order to obtain specific functional characteristics such as clustering, average shortest path length, sparseness, etc.?
4. Can plasticity rules in this framework replicate the structures observed in brain connectome and optimised artificial recurrent neural networks in computing?
5. How do the structural and spectral qualities of heterosynaptic plasticity induced networks impact on network capacity, efficiency, robustness, and dynamics?