Neural network models of list learning

By Neil Burgess, J. Shapiro, M. Moore. 1991

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “erence have been widely studied for over two decades, especially in networks learned using back-propagation (Ratcliff, 1990; Lewandowsky and Li, 1994) and in the Hopﬁeld networks (Nadal et al., 1986; Burgess et al., 1991). 7 Early attempts to mitigate catastrophic interference often consisted of mem”
Catastrophic forgetting in simple networks: an analysis of the pseudorehearsal solution. (1999, unknown): “Similar issues have been explored in Hopfield networks [15, 1, 20]§.”
The Persistence and Transience of Memory. (2017, background): “New learning might overwrite previous memories (i.e., catastrophic interference), and in turn, new learning is impeded by existing, stored memories (i.e., pro-active interference) (Burgess et al., 1991; McCloskey and Cohen, 1989; Palm, 2013; Siegle and Hasselmo, 2002).”
Sequential learning in neural networks: A review and a discussion of pseudorehearsal based methods (2004, unknown): “Early studies include [5,10,33,47].”