This paper proposes a new approach to Machine Learning (ML) that focuses on
unsupervised continuous context-dependent learning of complex patterns.
Although the proposal is partly inspired by some of the current knowledge about
the structural and functional properties of the mammalian brain, we do not
claim that biological systems work in an analogous way (nor the opposite).
Based on some properties of the cerebellar cortex and adjacent structures, a
proposal suitable for practical problems is presented. A synthetic structure
capable of identifying and predicting complex temporal series will be defined
and experimentally tested. The system relies heavily on prediction to help
identify and learn patterns based on previously acquired contextual knowledge.
As a proof of concept, the proposed system is shown to be able to learn,
identify and predict a remarkably complex temporal series such as human speech,
with no prior knowledge. From raw data, without any adaptation in the core
algorithm, the system is able to identify certain speech structures from a set
of Spanish sentences. Unlike conventional ML, the proposal can learn with a
reduced training set. Although the idea can be applied to a constrained
problem, such as the detection of unknown vocabulary in a speech, it could be
used in more applications, such as vision, or (by incorporating the missing
biological periphery) fit into other ML techniques. Given the trivial
computational primitives used, a potential hardware implementation will be
remarkably frugal. Coincidentally, the proposed model not only conforms to a
plausible functional framework for biological systems but may also explain many
elusive cognitive phenomena.

该研究提出了一种新的机器学习方法，专注于复杂模式的无监督连续上下文依赖学习。论文的证明了该系统能够从原始数据中学习、识别和预测人类言语等复杂时间序列。