The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understanding the remarkable efficacy of biological neural networks. Curiously, the deep learning revolution that revived neural networks has not paid much attention to topological aspects. The architectures of deep neural networks (DNNs) do not resemble their biological counterparts in the topological sense. We bridge this gap by presenting initial results of Deep Connectomics Networks (DCNs) as DNNs with topologies inspired by real-world neuronal networks. We show high classification accuracy obtained by DCNs whose architecture was inspired by the biological neuronal networks of C. Elegans and the mouse visual cortex. Recent advancements in neural network models have emerged through research in network architectures (He et al., 2016; Krizhevsky et al., 2012; Simonyan & Zisserman, 2014) , optimization (Kingma & Ba, 2014; Liu et al., 2019; Luo et al., 2019) , and generalization techniques (Srivastava et al., 2014; Ioffe & Szegedy, 2015; Zhang et al., 2019) , with convolutional layers inspired from receptive fields and functional architecture in cats' visual cortex. However, the field of deep neural networks, with all its neuro-biologically inspired building blocks, has mostly left the topology story out. 1 Curiously, in the Cambrian explosion of neural network architectures in the post-AlexNet era, none seem to be inspired by the ideas prevalent in the domain of brain connectomics. We demonstrated initial findings from applying networks inspired by real-world neuronal network topologies to deep learning. Our experiments show the trainability of a DNN based on the neuronal network C.Elegans and the visual cortex of a mouse with and without freezing the parameters of the graph modules, which outperforms WS graphs with good theoretical small-world properties. In future work, we will examine more principled methods for constructing a DAG from these networks and examine the impact of spectral properties of the graph topologies used both in the architectures we proposed and in the architecture proposed by (Xie et al., 2019) , while extending to other connectomes.
Initial findings in the intersection of network neuroscience and deep learning. C. Elegans and a mouse visual cortex learn to recognize handwritten digits.
{"src": "scitldr-AIC_train", "paper_id": "BJg6EmYL8B"}