Trained recurrent neural networksare commonly used both in neuroscience and in machine learning. Recently, several attempts were made to understand the dynamics of such trained networks. Understanding how these dynamics arise through learning , however, is an uncharted territory. Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of a line attractor  . We show that problems associated with sequential learning of different tasks also occur in this setting. Specifically, catastrophic URLetting dramatically hinders a local learning rule from converging. In contrast, we show that the FORCE algorithm avoids this issue, and analyze the underlying causes. Using these insights, we suggest steps towards a more biologically plausible version of FORCE, as well as an approach to design optimal training protocols .
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 URLetting, 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 .
R
Trained recurrent neural networksare commonly used both in neuroscience and in machine learning. Recently, several attempts were made to understand the dynamics of such trained networks. Understanding how these dynamics arise through learning , however, is an uncharted territory. Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of
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
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
D
We show that problems associated with sequential learning of different tasks also occur in this setting.
D
catastrophic URLetting dramatically hinders a local learning rule from converging. In contrast,
R
the FORCE algorithm avoids this issue, and analyze the underlying causes. Using these insights, we suggest steps towards a more
Least Mean Squares (LMS), a simple gradient descent suggested as a
R
FORCE, as well as an approach to design optimal training protocols
the FORCE algorithm, is constantly obstructed by URLetting, 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