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Found 4 papers, 0 papers with code
Biologically-Informed Excitatory and Inhibitory Balance for Robust Spiking Neural Network Training
Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence.
A Novel Method for Clustering Cellular Data to Improve Classification
Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring the need for organizing and interpreting large datasets.
Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning
However, since forgetting is inevitable given bounded memory and unbounded task loads, 'how to reasonably forget' is a problem continual learning must address in order to reduce the performance gap between AIs and humans, in terms of 1) memory efficiency, 2) generalizability, and 3) robustness when dealing with noisy data.
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization
Deep neural networks (DNNs) are known for extracting useful information from large amounts of data.
