  Artificial neural networks, trained to perform cognitive tasks, have recently
been used as models for neural recordings from animals performing these tasks.
While some progress has been made in performing such comparisons, the evolution
of network dynamics throughout learning remains unexplored. This is paralleled
by an experimental focus on recording from trained animals, with few studies
following neural activity throughout training. In this work, we address this
gap in the realm of artificial networks by analyzing networks that are trained
to perform memory and pattern generation tasks. The functional aspect of these
tasks corresponds to dynamical objects in the fully trained network - a line
attractor or a set of limit cycles for the two respective tasks. We use these
dynamical objects as anchors to study the effect of learning on their
emergence. We find that the sequential nature of learning has major
consequences for the learning trajectory and its final outcome. Specifically,
we show that Least Mean Squares (LMS), a simple gradient descent suggested as a
biologically plausible version of the FORCE algorithm, is constantly obstructed
by forgetting, which is manifested as the destruction of dynamical objects from
previous trials. The degree of interference is determined by the correlation
between different trials. We show which specific ingredients of FORCE avoid
this phenomenon. Overall, this difference results in convergence that is orders
of magnitude slower for LMS. Learning implies accumulating information across
multiple trials to form the overall concept of the task. Our results show that
interference between trials can greatly affect learning, in a learning rule
dependent manner. These insights can help design experimental protocols that
minimize such interference, and possibly infer underlying learning rules by
observing behavior and neural activity throughout learning.
