Key to this process is the concept of feedback, without which a net of neurons would be unable to learn. The relative strength of each synapse may be increased or decreased to better reflect the desired response. There appear to be specific modulatory neurons that facilitate this process. There are a variety of neural learning techniques that have been hyped, and the exact mechanisms involved is an area of particularly intense contemporary research. Regardless of the means, however, the performance of each neuron is constantly being monitored, and the synaptic strengths are continually readjusted to improve the overall performance of each layer of the network.
Neurons can also grow new dendritic and axon branches and create new synapses with other neurons.
You may be doing this right now as you read this month's Futurecast. In essence, a neuron takes the outputs of other neurons, weights each of these values according to a set of changing synaptic weights, takes the sum of these weighted values, and then compares the sum to a threshold value. Periodically, the synaptic weights are adjusted to improve the ability of a net of neurons to perform their intended function. The output of each neuron is, therefore, a very simple summary of its inputs. Through elaborate timed cascades and hierarchies of such simplification functions, percepts and concepts are realized and decisions made.