Loss over epochs for three models, each trained with LFP data from a separate rat. An epoch denotes one pass through all trials in the training or validation set. The validation trials are defined before training and are not used for calculating gradients. However, the training and validation error are almost identical throughout training. This is expected, as the validation and training trials only differ in the portion of the local field potential that was used as reference signal for the network, and in the seed used for generating the randomised stimulus onsets and offsets. (TIF)</p
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).</p
Understanding how an animal’s ability to learn relates to neural activity or is altered by lesions, ...
Training and testing curves of proposed model for, (a) Accuracy, and (b) Loss.</p
Estimation of learning curves is ubiquitously based on proportions of correct responses within movin...
A: Comparison of true and predicted long and short axes for the best 2-conv model (blue) and transfe...
<p>We set a maximum of ten epochs since a prolongation to more training epochs yields no gain in per...
We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...
<p>The examples of sigmoidal curves are based on the rats’ individual memory performance. Different ...
<p>(a) <i>Left</i>: time changes in the correct rates for 22 individual rats (gray) were aligned to ...
A) In order to study the connectivity of trained models, we fitted a mixture of Gaussians with 1 to ...
<p>Training gain for each predictor variable alone (black) and the loss in training gain when the va...
<p><b>A,</b> Perievent raster plots of representative LFP recording. Both examples display depolariz...
<p>Graphical analysis of model fit comparing behavioural (empirical) and model (predicted) data. Dat...
The positioning loss, confidence loss and classification loss data obtained from the experimental tr...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).</p
Understanding how an animal’s ability to learn relates to neural activity or is altered by lesions, ...
Training and testing curves of proposed model for, (a) Accuracy, and (b) Loss.</p
Estimation of learning curves is ubiquitously based on proportions of correct responses within movin...
A: Comparison of true and predicted long and short axes for the best 2-conv model (blue) and transfe...
<p>We set a maximum of ten epochs since a prolongation to more training epochs yields no gain in per...
We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...
<p>The examples of sigmoidal curves are based on the rats’ individual memory performance. Different ...
<p>(a) <i>Left</i>: time changes in the correct rates for 22 individual rats (gray) were aligned to ...
A) In order to study the connectivity of trained models, we fitted a mixture of Gaussians with 1 to ...
<p>Training gain for each predictor variable alone (black) and the loss in training gain when the va...
<p><b>A,</b> Perievent raster plots of representative LFP recording. Both examples display depolariz...
<p>Graphical analysis of model fit comparing behavioural (empirical) and model (predicted) data. Dat...
The positioning loss, confidence loss and classification loss data obtained from the experimental tr...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
<p>Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).</p
Understanding how an animal’s ability to learn relates to neural activity or is altered by lesions, ...