Location decoding errors based on CA1 neural data recorded from 1m square open field environment as a function of time window size. (a) shows mean error and (b) median error. Blue lines represent errors from the RNN decoder and red lines from Bayesian approaches. Results for the RNN approach are averaged over different independent realizations of the training algorithm. Black dots depict the mean/median error of each individual model. Results shown are for animal R2192.</p
Neural decoding is an important approach for extracting information from population codes. We previo...
Decoding neuronal information is important in neuroscience, both as a basic means to understand how ...
Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing...
(a-b) Decoding results in a 1m square environment. The RNN consistently outperforms the two Bayesian...
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...
A. Schematic description of Jezek et al’s experiment. As a rodent is moving in an environment, its p...
<p>(A) Example model population activities plotted together with the true source direction (pink), t...
<p>The training data set “MG” is used. Neuron 1 (output neuron): (A) Initial input distribution. (B)...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
Neural prosthetic technology has moved from the laboratory to clinical settings with human trials. T...
<p><b>A:</b> Schematic of the linear decoding method, here for 4 cells. A temporal filter is associa...
<p>Simulated decoding performance of grip type and spatial factors for different frequency bands (sl...
A. Recorded data. Positional error computed as a function of time from the teleportation (in units o...
Decoding neuronal information is important in neuroscience, both as a basic means to understand how ...
Neural decoding is an important approach for extracting information from population codes. We previo...
Decoding neuronal information is important in neuroscience, both as a basic means to understand how ...
Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing...
(a-b) Decoding results in a 1m square environment. The RNN consistently outperforms the two Bayesian...
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...
A. Schematic description of Jezek et al’s experiment. As a rodent is moving in an environment, its p...
<p>(A) Example model population activities plotted together with the true source direction (pink), t...
<p>The training data set “MG” is used. Neuron 1 (output neuron): (A) Initial input distribution. (B)...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
Neural prosthetic technology has moved from the laboratory to clinical settings with human trials. T...
<p><b>A:</b> Schematic of the linear decoding method, here for 4 cells. A temporal filter is associa...
<p>Simulated decoding performance of grip type and spatial factors for different frequency bands (sl...
A. Recorded data. Positional error computed as a function of time from the teleportation (in units o...
Decoding neuronal information is important in neuroscience, both as a basic means to understand how ...
Neural decoding is an important approach for extracting information from population codes. We previo...
Decoding neuronal information is important in neuroscience, both as a basic means to understand how ...
Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learn-ing...