  Assembly Calculus (AC), proposed by Papadimitriou et al., aims to reproduce
advanced cognitive functions through simulating neural activities, with several
applications based on AC having been developed, including a natural language
parser proposed by Mitropolsky et al. However, this parser lacks the ability to
handle Kleene closures, preventing it from parsing all regular languages and
rendering it weaker than Finite Automata (FA). In this paper, we propose a new
bionic natural language parser (BNLP) based on AC and integrates two new
biologically rational structures, Recurrent Circuit and Stack Circuit which are
inspired by RNN and short-term memory mechanism. In contrast to the original
parser, the BNLP can fully handle all regular languages and Dyck languages.
Therefore, leveraging the Chomsky-Sch \H{u}tzenberger theorem, the BNLP which
can parse all Context-Free Languages can be constructed. We also formally prove
that for any PDA, a Parser Automaton corresponding to BNLP can always be
formed, ensuring that BNLP has a description ability equal to that of PDA and
addressing the deficiencies of the original parser.
