Recurrent Neural Networks (RNNs) are popular models of brain function. The
typical training strategy is to adjust their input-output behavior so that it
matches that of the biological circuit of interest. Even though this strategy
ensures that the biological and artificial networks perform the same
computational task, it does not guarantee that their internal activity dynamics
match. This suggests that the trained RNNs might end up performing the task
employing a different internal computational mechanism, which would make them a
suboptimal model of the biological circuit. In this work, we introduce a novel
training strategy that allows learning not only the input-output behavior of an
RNN but also its internal network dynamics, based on sparse neural recordings.
We test the proposed method by training an RNN to simultaneously reproduce
internal dynamics and output signals of a physiologically-inspired neural
model. Specifically, this model generates the multiphasic muscle-like activity
patterns typically observed during the execution of reaching movements, based
on the oscillatory activation patterns concurrently observed in the motor
cortex. Remarkably, we show that the reproduction of the internal dynamics is
successful even when the training algorithm relies on the activities of a small
subset of neurons sampled from the biological network. Furthermore, we show
that training the RNNs with this method significantly improves their
generalization performance. Overall, our results suggest that the proposed
method is suitable for building powerful functional RNN models, which
automatically capture important computational properties of the biological
circuit of interest from sparse neural recordings
