Here's my attempt; not a huge number of changes because it was not too bad to begin with, but with slightly less self-indulgent language, and a lot of the jargo

Here's my attempt; not a huge number of changes because it was not too bad to begin with, but with slightly less self-indulgent language, and a lot of the jargon has to stay (partly because I don't know the field): Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - areas where humans have always performed better than conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information after a network has been trained. As a result, continuous learning methods could be very helpful in allowing deep networks to handle data sets which change over time. Here, inspired by the process of adult neurogenesis in the hippocampus, we investigate how adding new neurons to artificial neural networks can allow them to acquire new information, while preserving what they have already learned. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, show that neurogenesis looks like a good approach for tackling the "stability-plasticity dilemma" that has been a problem for adaptive machine learning algorithms for some time. As an academic, I tend to agree that we frequently feel compelled to apply more verbosity than is strictly required in order to communicate the intended semantic constructs.