Memory capacity in model of cortical layers II/III
Background: In previous work [1], a biologically detailed model of cortical layers II/III was presented. The model exhibits phenomena such as UP/DOWN-states and states with alpha- and gamma-like rythms [2]. Although the model is based on current knowledge of cortical microcircuitry, the structure of long-range connections is determined using the working hypothesis that these layers function similarly to a recurrent attractor network. More specifically, groups of pyramidal cells within minicolumns stretching through the layers constitute functional units. Long-range connections between minicolumns collectively form a set of attractors in the network dynamics, each representing a latent memory. When the dynamic state of the network approaches an attractor, the state moves towards the attractor. Close to the attractor the memory pattern has become fully recalled; the latent memory has become active. The present work is, thus, based on the simplifying assumption that memory patterns are static. To what extent can this type of model match artificial recurrent networks with regard to memory capacity?
Methods: Minicolumns in our models are further grouped into hypercolumns, each hypercolumn operating as a winner-take-all module through the mechanism of lateral inhibition. We ran simulations using two network sizes, one with 9 hypercolumns and 9 minicolumns per hypercolumn and the other with 25x25 minicolumns. Long-range connections were determined by a weight matrix obtained by training an artificial neural network. In previous models [1,2], the ANN was trained with orthogonal, non-overlapping, patterns. Here, patterns were random and overlapping. A pattern was considered correctly recalled if activation of 2/3 of the pattern resulted in recall of the full pattern with error in at most one minicolumn.
Results: Figure 1 shows the number of correctly recalled patterns for weight matrices trained with different numbers of stored patterns. Ideal performance is a straight line. Results are shown for the cortex model and for an ANN with matching connection weights. For both network sizes, memory breaks down at the same point for both types of model indicating that the cortex model can match the ANN with regard to memory capacity.
References
1. Lundqvist M, Rehn M, Djurfeldt M, Lansner A: Attractor dynamics in a modular network model of the neocortex. Network 2006, 17(3): 253 -276
2. Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg ? Brain-scale simulation of the neocortex on the IBM Blue Gene/L supercomputer IBM J Res Dev 2008, 52(1/2):31-41
Conference:
Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008.
Presentation Type:
Poster Presentation
Topic:
Large Scale Modeling
Citation:
Djurfeldt
M and
Lansner
A
(2008). Memory capacity in model of cortical layers II/III.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2008.
doi: 10.3389/conf.neuro.11.2008.01.082
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Received:
25 Jul 2008;
Published Online:
25 Jul 2008.
*
Correspondence:
Mikael Djurfeldt, CBN/CSC, KTH, Stockholm, Sweden, mikael@djurfeldt.com