<p>Each row corresponds to the learning of one tree by the algorithm. The tuning curve is sequentially split as shown on the left figures (vertical lines; blue line displays the actual tuning curve and black lines correspond to the prediction). Thus, intervals between each pair of splits are assigned a different target value. The first two trees are shown on the right and the exact values of each leaf are indicated in the square boxes. Note that the predicted firing rates are the sum over all the leaves (i.e. the value of a single leaf can not be directly interpreted.</p
Understanding how "black-box" models arrive at their predictions has sparked significant interest fr...
<p>Simulations for Case 2 of the learned spatial fields and synaptic weights from stripe cells of tw...
<p>Parameter is the learning rate, turns the model from a strict policy gradient rule to naive Heb...
<p><b>A</b> Using the angle as the input feature (red), the Machine Learning algorithm is trained to...
<p>Thus, the use of gradient boosted trees requires a careful tuning of the parameters of the algori...
<p><b>A</b>) The relative change (color scale, 1 = the firing rate of the cell is equal to the mea...
<p><b>A</b> Tuning-curve splitting for one neuron of the antero-dorsal nucleus (ADn) and one neuron ...
<p>(A) Grid cell firing maps for 400 grid cells with width constant on a 1 meter linear track (, , ...
International audienceGradient tree boosting is a prediction algorithm that sequentially produces a ...
<p>The figure depicts (from top to bottom): a counter-FF cell recorded under the L-rate (nPD = −84°)...
Prediction metrics on held out data for the best performing gradient boosted decision trees model.</...
<p>Results for the probability that the cell’s prediction of the gradient is accurate within three t...
<p>(<b>A</b>) Probability per unit time (spike rate) of a single neuron. Top, in red, experimental d...
Function that outputs the median scaled tree (a tree with branches scaled by the rate of evolution) ...
In A) and B) we predict the rate of learning (λ; y-axis) after varying motor (σm, deg; x-axis) and e...
Understanding how "black-box" models arrive at their predictions has sparked significant interest fr...
<p>Simulations for Case 2 of the learned spatial fields and synaptic weights from stripe cells of tw...
<p>Parameter is the learning rate, turns the model from a strict policy gradient rule to naive Heb...
<p><b>A</b> Using the angle as the input feature (red), the Machine Learning algorithm is trained to...
<p>Thus, the use of gradient boosted trees requires a careful tuning of the parameters of the algori...
<p><b>A</b>) The relative change (color scale, 1 = the firing rate of the cell is equal to the mea...
<p><b>A</b> Tuning-curve splitting for one neuron of the antero-dorsal nucleus (ADn) and one neuron ...
<p>(A) Grid cell firing maps for 400 grid cells with width constant on a 1 meter linear track (, , ...
International audienceGradient tree boosting is a prediction algorithm that sequentially produces a ...
<p>The figure depicts (from top to bottom): a counter-FF cell recorded under the L-rate (nPD = −84°)...
Prediction metrics on held out data for the best performing gradient boosted decision trees model.</...
<p>Results for the probability that the cell’s prediction of the gradient is accurate within three t...
<p>(<b>A</b>) Probability per unit time (spike rate) of a single neuron. Top, in red, experimental d...
Function that outputs the median scaled tree (a tree with branches scaled by the rate of evolution) ...
In A) and B) we predict the rate of learning (λ; y-axis) after varying motor (σm, deg; x-axis) and e...
Understanding how "black-box" models arrive at their predictions has sparked significant interest fr...
<p>Simulations for Case 2 of the learned spatial fields and synaptic weights from stripe cells of tw...
<p>Parameter is the learning rate, turns the model from a strict policy gradient rule to naive Heb...