A Pyscholinguistic Model of Natural Language Parsing Implemented in Simulated Neurons by Chris Huyck (www.cwa.mdx.ac.uk/chris/chrisroot.html, c.huyck@mdx.ac.uk) One of the central activities in natural language processing is parsing. There are a wide range of engineering solutions to parsing but none perform at human levels. The understanding of how humans process language is far from complete, but there is little doubt that humans use their neurons for all mental activities including parsing. There are several psychological models of parsing, but this talk will describe the first neuro-psychological model of parsing. That is, the parser is implemented entirely in simulated neurons. It makes use of Hebb's Cell Assembly hypothesis to form the basis of memories including words, clauses and sentences. Neural parsers require variable binding, and this parser binds via short-term potentiation. The parser produces correct semantic output. As neural cycles have an associated time, time can be measured, and the parser parses in times similar to humans. Prepositional phrase attachment ambiguities are resolved based on the semantics of the sentence. Finally, the parser is embedded in a functioning agent.