  We assume that, within the dense clusters of neurons that can be found in
nuclei, cells may interconnect via soma-to-soma interactions, in addition to
conventional synaptic connections. We illustrate this idea with a multi-layer
architecture (MLA) composed of multiple clusters of recurrent sub-networks of
spiking Random Neural Networks (RNN) with dense soma-to-soma interactions, and
use this RNN-MLA architecture for deep learning. The inputs to the clusters are
first normalised by adjusting the external arrival rates of spikes to each
cluster. Then we apply this architecture to learning from multi-channel
datasets. Numerical results based on both images and sensor based data, show
the value of this novel architecture for deep learning.
