The recent developments in soft computing cannot be complete without noting
the contributions of artificial neural machine learning systems that draw
inspiration from real cortical tissue or processes that occur in human brain.
The universal approximability of such neural systems has led to its wide spread
use, and novel developments in this evolving technology has shown that there is
a bright future for such Artificial Intelligent (AI) techniques in the soft
computing field. Indeed, the proliferation of large and very deep networks of
artificial neural systems and the corresponding enhancement and development of
neural machine learning algorithms have contributed immensely to the
development of the modern field of Deep Learning as may be found in the well
documented research works of Lecun, Bengio and Hinton. However, the key
requirements of end user affordability in addition to reduced complexity and
reduced data learning size requirement means there still remains a need for the
synthesis of more cost-efficient and less data-hungry artificial neural
systems. In this report, we present an overview of a new competing bio-inspired
continual learning neural tool Neuronal Auditory Machine Intelligence
(Neuro-AMI) as a predictor detailing its functional and structural details,
important aspects on right applicability, some recent application use cases and
future research directions for current and prospective machine learning experts
and data scientists.

现代深度学习领域的发展需要成本高效且对数据需求量相对较小的人工神经系统，本研究概述了一种新的竞争性生物启发式持续学习神经工具 Neuro-AMI，作为预测模型，并详细描述了其功能和结构细节，适用性，最新的应用案例以及现有和未来的研究方向。