Computer architectureBiological neural networksOptimizationThis work addresses a new structure optimization of neuromorphic computing architectures. This enables to speed up the DNN (deep neural network) computation twice as fast as, theoretically, that of the existing architectures. Precisely, ...
We first describe the organizing principles of brain network architecture instantiated in structural wiring under constraints of spatial embedding and energy minimization. We then survey models of brain network function that stipulate how neural activity propagates along structural connections. Finally, we ...
Figure 3: The overall architecture of BRCNN. Two-Channel recurrentneural networkswith LSTM units pick up information along the shortest dependency path, and inversely at the same time. Convolution layers are applied to extract local features from the dependency units. In the example, we conduct t...
TLDR: We study the architecture of neural networks through the lens of network science, and discover thatgood neural networks are alikein terms of their underlying graph structure. We define a novel graph-based representation of neural networks calledrelational graph, as opposed to the commonly used...
A growing number of studies have used stylized network models of communication to predict brain function from structure. Most have focused on a small set of models applied globally. Here, we compare a large number of models at both global and regional le
An interesting question one may ask is whether the network architecture and input data statistics may guide the choices of learning parameters and vice versa. In this work, we explore the association between such structural, distributional and learnability aspects vis-à-vis their interaction with...
On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural ...
Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective convolutional neural networksembedded edge devicesBrain-computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive... H Sultan,H Ijaz,Wa...
Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems. Despite a great deal of general methodological developments, representing fermionic matter is however still early research activity. He
by identifying a smooth and monotonous relationship between structural and functional neural network architecture it was possible to devise a network fitting algorithm that allows to simultaneously and precisely control the state of synchronization between every pair of network nodes, allowing to tune each...