7 citations · 13 across the 6 of their papers we have counts for
7 papers
Inhibitory normalization of error signals improves learning in neural circuits
Roy Henha Eyono, Daniel Levenstein, Arna Ghosh +2
Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural population…
Tracing the Representation Geometry of Language Models from Pretraining to Post-training
Melody Zixuan Li, Kumar Krishna Agrawal, Arna Ghosh +4
Standard training metrics like loss fail to explain the emergence of complex capabilities in large language models. We take a spectral approach to investigate the geometry of learn…
Why all roads don't lead to Rome: Representation geometry varies across the human visual cortical hierarchy
Arna Ghosh, Zahraa Chorghay, Shahab Bakhtiari +1
Biological and artificial intelligence systems navigate the fundamental efficiency-robustness tradeoff for optimal encoding, i.e., they must efficiently encode numerous attributes…
Learning Successor Features the Simple Way
Raymond Chua, Arna Ghosh, Christos Kaplanis +2
In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successo…
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Roman Pogodin, Jonathan Cornford, Arna Ghosh +3
A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic plasticity in the brain. However, the va…
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Yuhan Helena Liu, Arna Ghosh, Blake A. Richards +2
To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond t…
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince, Roy Henha Eyono, Ellen Boven +9
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based l…