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Found 6 papers, 2 papers with code
convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data
Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns.
An Inherent Trade-Off in Noisy Neural Communication with Rank-Order Coding
Rank-order coding, a form of temporal coding, has emerged as a promising scheme to explain the rapid ability of the mammalian brain.
Simplicial Hopfield networks
Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories.
Goal-oriented inference of environment from redundant observations
We emphasize that our model only containing the core states has high explainability.
An Extension of Fisher's Criterion: Theoretical Results with a Neural Network Realization
Fisher's criterion is a widely used tool in machine learning for feature selection.
Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons
Furthermore, we extend the study to investigate the properties of segregation on task settings not yet explored with human subjects, namely natural sounds and images.
