Most generally, learning is the process of acquiring new memory.



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In Biological Neural Networks

We are just beginning to understand some of the underlying biological mechanisms of learning. These include PTP, LTP, a silent-synapse concept has also been offered.

Various interactions between proteins and other factors seem to be responsible for a broad range of different memory effects. Each effect has it's own envelope of temporal characteristics, that include onset time, rise-time, and decay time. Many of these mechanisms have been discovered relatively recently, but general knowledge of them, along with an understanding of their diversity, has been forming for over three decades.



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In Artificial Neural Networks

Learning, in artificial neural networks (ANNs), is (traditionally) the process of adjusting a set of weight values to bring a neuron's output response closer to a desired response for a given set of inputs. The weight values represent connection strengths between neurons.



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Learning Is Ubiquitous

Learning seems to be always occurring. That is, it is a phenomenon which can be counted on to occur, like chemical interactions, or gravity.



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Further Resources

• Multitemporal Synapses and Our Perception of a Present Moment