Associative recall in non-randomly diluted neuronal networks

Luciano da Fontoura Costa and Dietrich Stauffer

Physica A: Statistical Mechanics and its Applications, 2003, vol. 330, issue 1, 37-45

Abstract: The potential for associative recall of diluted neuronal networks is investigated with respect to several biologically relevant configurations, more specifically the position of the cells along the input space and the spatial distribution of their connections. First, we put the asymmetric Hopfield model onto a scale-free Barabási–Albert network. Then, a geometrical diluted architecture, which maps from L-bit input patterns into N-neurons networks, with R=N/L<1 (we adopt R=0.1,0.2 and 0.3), is considered. The distribution of the connections between cells along the one-dimensional input space follows a normal distribution centered at each cell, in the sense that cells that are closer to each other have increased probability to interconnect. The models also explicitly consider the placement of the neuronal cells along the input space in such a way that denser regions of that space tend to become denser, therefore implementing a special case of the Barabási–Albert connecting scheme. The obtained results indicate that, for the case of the considered stimuli and noise, the network performance increases with the spatial uniformity of cell distribution.

Keywords: Asymmetry; Scale-free networks; Geometric networks (search for similar items in EconPapers)
Date: 2003
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:330:y:2003:i:1:p:37-45

DOI: 10.1016/j.physa.2003.08.010

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