One Step Back, Two Steps Forward: Interference and Learning in Recurrent Neural Networks
One Step Back, Two Steps Forward: Interference and Learning in Recurrent Neural Networks
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 …