Deep Learning in Multi-Layer Architectures of Dense Nuclei

In dense clusters of neurons 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. We use this RNN-MLA architecture for deep learning. The inputs to the clusters are normalised by adjusting the external arrival rates of spikes to each cluster, and then apply this architectures to learning from multi-channel datasets. We present numerical results based on both images and sensor based data that show the value of this RNN-MLA for deep learning.