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Found 5 papers, 2 papers with code
Tailoring Artificial Neural Networks for Optimal Learning
As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing.
Credit Assignment in Neural Networks through Deep Feedback Control
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output.
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Identifying unfamiliar inputs, also known as out-of-distribution (OOD) detection, is a crucial property of any decision making process.
Bio-Inspired, Task-Free Continual Learning through Activity Regularization
We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation.
Exploring the functional hierarchy of different pyramidal cell types in temporal processing
Recent research has revealed the unique functionality of cortical pyramidal cell subtypes, namely intratelencephalic neurons (IT) and pyramidal-tract neurons (PT).
