title: Modeling spatial-temporal operations with context-dependent associative memories.
abstract: We organize our behavior and store structured information with many procedures that require the coding of spatial and temporal order in specific neural modules. In the simplest cases, spatial and temporal relations are condensed in prepositions like "below" and "above", "behind" and "in front of", or "before" and "after", etc. Neural operators lie beneath these words, sharing some similarities with logical gates that compute spatial and temporal asymmetric relations. We show how these operators can be modeled by means of neural matrix memories acting on Kronecker tensor products of vectors. The complexity of these memories is further enhanced by their ability to store episodes unfolding in space and time. How does the brain scale up from the raw plasticity of contingent episodic memories to the apparent stable connectivity of large neural networks? We clarify this transition by analyzing a model that flexibly codes episodic spatial and temporal structures into contextual markers capable of linking different memory modules.
authors.affiliation: Group of Cognitive Systems Modeling, Biophysics Section, Facultad de Ciencias, Universidad de la República, Iguá 4225, 11400 Montevideo, Uruguay.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Mizraji
authors.affiliation: Department of Physics, Washington College, Chestertown, MD 21620 USA.
authors.forename: Juan
authors.initials: J
authors.lastname: Lin
keywords: Cognitive order relations; Context-dependent associative memories; Hierarchical models; Neural computation