The Construction of Semantic Memory: Grammar-Based Representations Learned from Relational Episodic Information

After acquisition, memories underlie a process of consolidation, making them more resistant to interference and brain injury. Memory consolidation involves systems-level interactions, most importantly between the hippocampus and associated structures, which takes part in the initial encoding of memory, and the neocortex, which supports long-term storage. This dichotomy parallels the contrast between episodic memory (tied to the hippocampal formation), collecting an autobiographical stream of experiences, and semantic memory, a repertoire of facts and statistical regularities about the world, involving the neocortex at large. Experimental evidence points to a gradual transformation of memories, following encoding, from an episodic to a semantic character. This may require an exchange of information between different memory modules during inactive periods. We propose a theory for such interactions and for the formation of semantic memory, in which episodic memory is encoded as relational data. Semantic memory is modeled as a modified stochastic grammar, which learns to parse episodic configurations expressed as an association matrix. The grammar produces tree-like representations of episodes, describing the relationships between its main constituents at multiple levels of categorization, based on its current knowledge of world regularities. These regularities are learned by the grammar from episodic memory information, through an expectation-maximization procedure, analogous to the inside–outside algorithm for stochastic context-free grammars. We propose that a Monte-Carlo sampling version of this algorithm can be mapped on the dynamics of “sleep replay” of previously acquired information in the hippocampus and neocortex. We propose that the model can reproduce several properties of semantic memory such as decontextualization, top-down processing, and creation of schemata

A neural blackboard architecture of sentence structure

We present a neural architecture for sentence representation. Sentences are represented in terms of word representations as constituents. A word representation consists of a neural assembly distributed over the brain. Sentence representation does not result from associations between neural word assemblies. Instead, word assemblies are embedded in a neural architecture, in which the structural (thematic) relations between words can be represented. Arbitrary thematic relations between arguments and verbs can be represented. Arguments can consist of nouns and phrases, as in sentences with relative clauses. A number of sentences can be stored simultaneously in this architecture. We simulate how probe questions about thematic relations can be answered. We discuss how differences in sentence complexity, such as the difference between subject-extracted versus object-extracted relative clauses and the difference between right-branching versus center-embedded structures, can be related to the underlying neural dynamics of the model. Finally, we illustrate how memory capacity for sentence representation can be related to the nature of reverberating neural activity, which is used to store information temporarily in this architecture