Lansner, A., Sandberg, A., Petersson, K. M., & Ingvar, M.
(2000). On forgetful attractor network memories. In H. Malmgren, M. Borga, & L. Niklasson (Eds.), Artificial neural networks in medicine and biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13-16 May 2000 (pp. 54-62). Heidelberg: Springer Verlag.
A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuromodulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas. Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.