The Importance of Modeling for the Future of Molecular Studies of Learning and Memory

Computational models are important for guiding and interpreting molecular studies of learning and memory because they provide a bridge between biological and behavioral levels of analysis. These models facilitate the identification of central underlying principles that span different levels, and they can accommodate the many complexities of each of these levels and their interrelationships. We present an overview of our computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. This framework provides insight into: (a) which behavioral paradigms are most appropriate for dissociating cortical and hippocampal learning contributions; (b) effects of selective impairments on hippocampal areas; (c) effects of synaptic modification (LTP/LTD) impairments on various pathways within the hippocampal system; (d) effects of manipulations of hippocampal activation parameters; and (e) effects of manipulations that selectively impair different components of learning (Hebbian versus error-driven). We hope that these insights provide fruitful avenues for further research using molecular techniques to inform our understanding of exactly how learning and memory phenomena emerge from their biological basis. 2 A Computational Perspective