Abstract: We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an ...
Abstract: Deep learning (DL)-based fully supervised approaches have demonstrated remarkable performance in sea ice classification, showcasing their potential for highly accurate results. However, ...
Please refer our paper for more details. Works for Python version < 3.8 Note: For faster installation, if you don't plan to use neural networks, you can skip ...
It remains poorly understood how different cells in a tissue organize and coordinate with each other to support tissue functions. To better understand the structure-function relationship of a tissue, ...
In deep neural networks, representational learning in the middle layer is essential for achieving efficient learning. However, the currently prevailing backpropagation learning rules (BP) are not ...
Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored ...
Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to ...
Supervised learning algorithms can learn subtle features that distinguish one class of input examples from another. We explore a supervised training framework in which mechanical metamaterials ...
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