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(Neural Computation. 1999;11:1717-1737.)
© 1999 The MIT Press

Letter

Associative Memory in a Multimodular Network

Nir Levy

School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel

David Horn

School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel

Eytan Ruppin

Departments of Computer Science and Physiology, Tel Aviv University, Tel Aviv 69978, Israel

Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multimodular associative memory network, whose functional goal is to store patterns with different coding levels—patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, synaptic inputs should be segregated into intra modular projections and intermodular projections, with the latter under going additional nonlinear dendritic processing. This segregation makes sense anatomically if the intermodular projections represent distalsynaptic connections on apical dendrites. It is then straight forward to show that memories encoded in more modules are more resilient to focal afferent damage. Further hierarchical segregation of intermodular connections on the dendritic tree improves this resilience, allowing memory retrieval from input to just one of the modules in which it is encoded.




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