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Found 6 papers, 2 papers with code
Neuron-Astrocyte Associative Memory
Such multi-neuron synapses are ubiquitous in models of Dense Associative Memory (also known as Modern Hopfield Networks) and are known to lead to superlinear memory storage capacity, which is a desirable computational feature.
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.
