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This paper considers the current state of the art regarding the avoidance of catastrophic interference, a phenomena experienced by neural network architectures which share weights. The paper compares and contrasts the authors Biologically plausible neural network architecture (termed Biologically plausible model, or BPM) to the pseudo recurrent neural networks developed by Robert French using two real world data sets which have been used in previous studies of catastrophic interference. The results show that although the pseudo recurrent techniques do offer significant advantages in recall performance, the authors model does

Two Biologically Plausible Network Architectures for the Avoidance of Catastrophic Interference