In a sparse random network, one may vary either a synaptic strength or a sparseness parameter in order to change a single connectivity parameter in the Wilson-Cowan equations.
In this paper, we propose a multiscale regression framework for image reconstruction from sparse random samples.
The synchronization transition of correlated ensembles of coupled Kuramoto oscillators on sparse random networks is investigated.
More concretely, the matrix of sparse random projections (Phi in mathbb {R}^{mtimes n}) contains i.i.d.i.d
Figure 1 The MSEs comparison for sparse random projections with different degrees of sparsity.
We evaluate the performance of the proposed adaptive sparse random projections.
Figure 6 Performance comparison of some reconstruction techniques for sparse random trasnformations.
Therefore, for future work, we will study adaptive sparse random projections with partial channel information.
Section 2 gives the system model and overview previously known results on sparse random projections.
Sparse, random labelling of individual cells is a key approach to study brain circuit organisation and development.
It introduces ensembles of codes defined by sparse random graphs and discusses their most basic property – the weight distribution.
