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Found 7 papers, 2 papers with code
Is All Learning (Natural) Gradient Descent?
This paper shows that a wide class of effective learning rules -- those that improve a scalar performance measure over a given time window -- can be rewritten as natural gradient descent with respect to a suitably defined loss function and metric.
Neuron-Astrocyte Associative Memory
In their known biological implementations the ratio of stored memories to the number of neurons remains constant, despite the growth of the network size.
Contraction Properties of the Global Workspace Primitive
To push forward the important emerging research field surrounding multi-area recurrent neural networks (RNNs), we expand theoretically and empirically on the provably stable RNNs of RNNs introduced by Kozachkov et al. in "RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks".
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
To bridge this gap, we introduce a novel similarity metric that compares two systems at the level of their dynamics, called Dynamical Similarity Analysis (DSA).
Generalization in Supervised Learning Through Riemannian Contraction
We prove that Riemannian contraction in a supervised learning setting implies generalization.
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity.
The Causal Role of Astrocytes in Slow-Wave Rhythmogenesis: A Computational Modelling Study
Finding the origin of slow and infra-slow oscillations could reveal or explain brain mechanisms in health and disease.
