Fig. 8.

Decode generalization when classifying neural activity with low and high cumulative dimensionality. The decode accuracy of a binary classifier (colored in black for data and blue for RNN models) is shown as the number of time points used during training is varied. The chance level is shown in red. The neural activity with low cumulative dimensionality allows a classifier trained at a single time point to perform with high accuracy when tested at other times. This is in contrast to neural activity with high cumulative dimensionality (bottom row) where a decoder trained at a single time point performs at chance level when tested at other time points. To assess generalization performance, the classifier is always tested on trials that were not used during training and tested on time points from the entire delay interval with the exception of the first 500 ms after stimulus offset for the neural datasets. In the top row, the decoder classifies rewarded versus nonrewarded trials. In the middle row, the decoder classifies high versus low frequencies. In the bottom row, the decoder classifies trials from the two patterns the network is trained to produce in the study by Laje and Buonomano (17). The plotted decode accuracy is the mean of the classifier performance across this interval, when tested on trials that were not used during training. Error bars show two SDs.