xiaoxuanlei.bsky.social
CompNeuro & AI ❤️
FutureToBeBlackBoxBreaker👻
PhD candidate @McGill et @Mila
11 posts
83 followers
65 following
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📌 Poster Session:
⏰ When: TODAY, Thu, Dec 12, 4:30 p.m. – 7:30 p.m. PST
📍 Where: East Exhibit Hall A-C, #3705
📄 What: Geometry of Naturalistic Object Representations in Recurrent Neural Network Models of Working Memory
Hope to see you there!
@bashivan.bsky.social @takuito.bsky.social
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👉 Check it out: arxiv.org/abs/2411.02685
📅 We’ll be at NeurIPS! Join us for our poster presentation on Thu 12 Dec, 7:30 p.m. EST — 10:30 p.m. EST.
#AI #CognitiveScience #WorkingMemory #DeepLearning #RepresentationGeometry #MultiTask
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Our findings bridge cognitive science & AI, revealing how high-dimensional object information is encoded, retained, and recalled in recurrent models of working memory.
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🎯 With training, RNNs implemented chronological memory subspaces allowing them to track object information using rotational dynamics—supporting resource-based models of working memory.
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📐 Surprisingly, object features are less orthogonalized in RNN representations compared to perceptual space.
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🧠 We found that multi-task RNNs (unlike single-task ones) retain both task-relevant & irrelevant info but reusable representations only emerged in simple gateless architectures.
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🖥️ To answer this question, we trained multi-task RNNs (vanilla, GRU, LSTM) on 9 N-back tasks using naturalistic 3D object stimuli to study encoding, retention, & retrieval dynamics.
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It’s unclear how high-dimensional naturalistic sensory information is encoded, retained and recalled in these models to accommodate various task demands.
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Previous models of working memory have mainly focused on using abstract stimuli (Mante et al., 2013, Yang et al., 2019, Driscoll et al., 2024, Fascianelli et al., 2024, Piwek & Stokes, 2023 etc)