Described is a system for adapting neural networks. The system receives inputs to be learned by a multi-layered spiking neural network. A first mechanism is implemented to adapt weights on the connections via competition among neurons using Hebbian learning. Activity levels of the neurons are stabilized to allow the multi-layered spiking neural network to learn the inputs. A second mechanism is implemented to increase a learning rate of a neuron over time using Hebbian learning. A third mechanism is implemented, wherein newly created neurons, representing new inputs, copy at least one synaptic structure of older neurons in the multi-layered spiking neural network. The mechanisms are used for continuous, online learning of the inputs to the multi-layered spiking neural network. An autonomous system, such as an autonomous vehicle, can use the learned inputs to learn from its environment and perform tasks, such as classification and prediction.
Plastic neural networks