Also, The Stability-Plasticity Dilemma - is a name used to describe a problem encountered in neural network simulations. Many of these systems, once trained on a given set of exemplar responses, are simply not capable of learning anything new. This prevents the network from being able to continuously learn while it interacts with its surroundings.

The term is a bit of a misnomer, in that stability-plasticity merely highlights a problem (or dilemma) with conventional artificial neural network learning models. The general behavior of achieving stability and plasticity simultaneously in an adaptive system is not really a dilemma at all. The human brain is a perfect example of a system that quite handily achieves that goal. For that matter, so is the mouse brain. Since it involves asking the question: "How is simultaneous stability and plasticity facilitated within biological learning systems?" perhaps a better label might be, "The Stability-Plasticity Question."

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In other words the real crux of the question has been in how to design an artificial system that—like the mouse brain—is simultaneously sensitive to, but not radically disrupted by, new learning.

This problem manifests in conventional ANNs as catastrophic forgetting. That is, the radical loss of most existing training when an attempt is made to add a single new item to the network's existing (i.e., pre-trained) response repertoire.




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Enter: Multitemporal Synapses

A new learning method and mechanism, called multitemporal synapses fully solves this problem. In fact, it is capable of providing a considerably greater range of stability-plasticity than is normally associated with human brains. Like natural neural networks, this new method permits continuous (not merely continual) learning and adapting, while the network-driven system interacts with its complex environment.




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Resources

• MicroRNA Regulation of the Synaptic Plasticity-Related Gene Arc