We killed mice of both cohorts after 4 days of treatment and measured spleen weight as readout of its degree of infiltration with leukemic cells, to quantify response to treatment.
The readout weight of the RC network is trained and updated by support vector regression (SVR) with a Gaussian insensitive loss function.
In addition, a readout weight was assigned to the opposite cells, as in the selective decoding model.
Under this view, our data point to LO as the locus for these readout weights.
With appropriate synaptic weights, these downstream readout circuits would have a stimulus tuning profile that matches behavioral response rates.
However, following training, readout weights are optimized [ 10]; in consequence, feature representations in ventral cortex become the limiting stage that determines task performance.
This process may involve augmented Hebbian reweighting, where a single set of readout weights is modified through training on feature differences [ 36].
In such cases, there would be correlated noise between pairs of neurons drawn from the two pools, and the CPs of opposite cells would depend on both readout weights and noise correlations.
Upon first glance, this is just a high-end scale with a digital readout for weight and body fat.
For the selective decoding model, we examined how performance changes as a function of the weighting applied to opposite cells (Readout Index, Figure 6B, 'Materials and methods').
For our data, each weight is associated with a single readout of an independent siRNA.
