Neural network apparatus and methods for implementing reinforcement learning. In one implementation, the neural network is a spiking neural network, and the apparatus and methods may be used for example to enable an adaptive signal processing system to effect focused exploration by associative adaptation, including providing a negative reward signal to the network, which may increase excitability of the neurons in combination with decrease in excitability of active neurons. In certain implementations, the increase is gradual and of smaller magnitude, compared to the excitability decrease. In some implementations, the increase/decrease of the neuron excitability is effectuated by increasing/decreasing an efficacy of the respective synaptic connections delivering presynaptic inputs into the neuron. The focused exploration may be achieved for instance by non-associative potentiation configured based at least on the input spike rate. The non-associative potentiation may further comprise depression of connections that provide input in excess of a desired limit.
Apparatus and methods for reinforcement learning in artificial neural networks