The stability-plasticity dilemma is a well-know constraint for artificial and biological neural systems. The basic idea is that learning in aparallel and distributed system requires plasticity for the integration of new knowledge, but also stability in order to prevent the forgetting of previous knowledge. Too much plasticity will result in previously encoded data being constantly forgotten, whereas too much stability will impede the efficient coding of this data at the level of the synapses. However, for the most part, neural computation has addressed the problems related to excessive plasticity or excessive stability as two different fields in the literature.
The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects
