ArticleJournal of mathematical neuroscience2013
Inhomogeneous sparseness leads to dynamic instability during sequence memory recall in a recurrent neural network model.
Article in Journal of mathematical neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Re-encoding of associations by recurrent plasticity increases memory capacity.Frontiers in synaptic neuroscience · 2014Article
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2 authors.
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Abstract
Theoretical models of associative memory generally assume most of their parameters to be homogeneous across the network. Conversely, biological neural networks exhibit high variability of structural as well as activity parameters. In this paper, we extend the classical clipped learning rule by Willshaw to networks with inhomogeneous sparseness, i.e., the number of active neurons may vary across memory items. We evaluate this learning rule for sequence memory networks with instantaneous feedback inhibition and show that little surprisingly, memory capacity degrades with increased variability in sparseness. The loss of capacity, however, is very small for short sequences of less than about 10 associations. Most interestingly, we further show that, due to feedback inhibition, too large patterns are much less detrimental for memory capacity than too small patterns.
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