  Tsetlin Machine (TM) has been gaining popularity as an inherently
interpretable machine leaning method that is able to achieve promising
performance with low computational complexity on a variety of applications. The
interpretability and the low computational complexity of the TM are inherited
from the Boolean expressions for representing various sub-patterns. Although
possessing favorable properties, TM has not been the go-to method for AI
applications, mainly due to its conceptual and theoretical differences compared
with perceptrons and neural networks, which are more widely known and well
understood. In this paper, we provide detailed insights for the operational
concept of the TM, and try to bridge the gap in the theoretical understanding
between the perceptron and the TM. More specifically, we study the operational
concept of the TM following the analytical structure of perceptrons, showing
the resemblance between the perceptrons and the TM. Through the analysis, we
indicated that the TM's weight update can be considered as a special case of
the gradient weight update. We also perform an empirical analysis of TM by
showing the flexibility in determining the clause length, visualization of
decision boundaries and obtaining interpretable boolean expressions from TM. In
addition, we also discuss the advantages of TM in terms of its structure and
its ability to solve more complex problems.
