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Found 9 papers, 4 papers with code
Aligned and oblique dynamics in recurrent neural networks
Here, we utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view.
Charting and navigating the space of solutions for recurrent neural networks
Observations in both neuroscience and machine learning challenge this assumption.
Cancer Progression as a Learning Process
We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory adaptation is feasible in a high dimensional system as the cell.
The interplay between randomness and structure during learning in RNNs
Recurrent neural networks (RNNs) trained on low-dimensional tasks have been widely used to model functional biological networks.
Implementing Inductive bias for different navigation tasks through diverse RNN attrractors
Navigation is crucial for animal behavior and is assumed to require an internal representation of the external environment, termed a cognitive map.
Repeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network
Memories in neural system are shaped through the interplay of neural and learning dynamics under external inputs.
DON’T JUDGE A BOOK BY ITS COVER - ON THE DYNAMICS OF RECURRENT NEURAL NETWORKS
To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories.
Understanding and Controlling Memory in Recurrent Neural Networks
Finally, we propose a novel regularization technique that is based on the relation between hidden state speeds and memory longevity.
One step back, two steps forward: interference and learning in recurrent neural networks
Our results show that interference between trials can greatly affect learning, in a learning rule dependent manner.
