CRC 1315/2: A neuronal model for the development of schemas and their role in systems memory consolidation (SP B01)

New learning of declarative memories and the consolidation of memories can be facilitated if the newly learned
information is consistent with preexisting knowledge (Tse et al., 2007). The existence of a knowledge base or “schema” accelerates new learning and enables plasticity during acquisition to happen also outside the hippocampus (Tse et al., 2011). However, the neural representation underlying a schema, how it can be used to promote new learning, and the cellular/network mechanisms that allow for the development of a schema are poorly understood. Here we hypothesize that schemas are related to extra-hippocampal neural representations of memory items with particularly high population sparseness. This idea is motivated by computational models of associative memory suggesting that single-trial learning is facilitated by population-sparse representations (e.g. Treves & Rolls, 1991; Leibold & Kempter, 2008). Experimentally, the highest levels of population sparseness are found in hippocampal networks, and such networks enable single-trial learning. In contrast, in neocortical networks the population sparseness is typically much lower, which requires much slower learning. We thus propose that the particularly significant items (e.g., objects, places, odors, facts, events or combinations thereof) that are related to a schema are represented with extraordinarily high population sparseness in networks outside of the hippocampus. The high population sparseness of an item that corresponds to a schema then facilitates the association (i.e. learning and consolidation) with a new item within the neocortical network. However, there is a lack of understanding if and to what degree high population sparseness can be achieved in heterogeneous networks such as those in the neocortex. Thus, the aim of the project presented here is to develop and test a mechanistic theory for the development of population-sparse representations of particularly significant memory items in generic two-layer networks. This theory will be a basis to study in a possible subsequent funding period the dynamics of memory acquisition and consolidation in dependence on population sparseness of representations in hierarchically organized networks spanning from hippocampal to neocortical areas.