Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the article "Efficient neural decoding of self-location with a deep recurrent network". This data should be used with the code found in https://github.com/NeuroCSUT/RatGPS and should be placed in the Bayesian/Data folder of the codebase
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still u...
Data supporting Mamun KA, Mace M, Lutman ME, Stein J, Liu X, Aziz T, Vaidyanathan R and Wang S (2015...
International audienceThis article presents an approximate data encoding scheme called Significant P...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fi...
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fi...
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
Neural decoding is an important approach for extracting information from population codes. We previo...
Location decoding errors based on CA1 neural data recorded from 1m square open field environment as ...
This study investigates a population decoding paradigm, in which the estimation of stimulus in the p...
Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing...
AbstractTo gain a better understanding of how neural ensembles communicate and process information, ...
To gain a better understanding of how neural ensembles communicate and process information, neural d...
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still u...
Data supporting Mamun KA, Mace M, Lutman ME, Stein J, Liu X, Aziz T, Vaidyanathan R and Wang S (2015...
International audienceThis article presents an approximate data encoding scheme called Significant P...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fi...
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fi...
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
Neural decoding is an important approach for extracting information from population codes. We previo...
Location decoding errors based on CA1 neural data recorded from 1m square open field environment as ...
This study investigates a population decoding paradigm, in which the estimation of stimulus in the p...
Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing...
AbstractTo gain a better understanding of how neural ensembles communicate and process information, ...
To gain a better understanding of how neural ensembles communicate and process information, neural d...
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still u...
Data supporting Mamun KA, Mace M, Lutman ME, Stein J, Liu X, Aziz T, Vaidyanathan R and Wang S (2015...
International audienceThis article presents an approximate data encoding scheme called Significant P...