Summary of Geometry Of Naturalistic Object Representations in Recurrent Neural Network Models Of Working Memory, by Xiaoxuan Lei et al.
Geometry of naturalistic object representations in recurrent neural network models of working memory
by Xiaoxuan Lei, Takuya Ito, Pouya Bashivan
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Geometry (cs.CG); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the lack of understanding on how naturalistic object information is maintained in working memory in neural networks. Researchers developed sensory-cognitive models combining convolutional and recurrent neural networks (CNN-RNN) trained on nine distinct N-back tasks using naturalistic stimuli. The study found that multi-task RNNs represent both task-relevant and irrelevant information, while gated RNNs like GRU and LSTM exhibit highly task-specific latent subspaces. Surprisingly, RNNs embed objects in new representational spaces with less orthogonalization relative to the perceptual space. Furthermore, the transformation of working memory encodings into memory is shared across stimuli, but distinct across time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Working memory is important for making good decisions. Scientists have been studying how it works, but most studies have used simple inputs that aren’t very realistic. They also only looked at what happens when you’re doing one thing or a few things at once. This paper tries to fix this by using more realistic inputs and looking at working memory during multiple tasks. The researchers developed a special kind of neural network called a CNN-RNN and trained it on nine different tasks. They found that these networks can represent both important and unimportant information, but in different ways depending on the task. They also discovered that the way the network stores memories is unique for each object and changes over time. |
Keywords
» Artificial intelligence » Cnn » Lstm » Multi task » Neural network » Rnn
