3,776 research outputs found
Relevance of Dynamic Clustering to Biological Networks
Network of nonlinear dynamical elements often show clustering of
synchronization by chaotic instability. Relevance of the clustering to
ecological, immune, neural, and cellular networks is discussed, with the
emphasis of partially ordered states with chaotic itinerancy. First, clustering
with bit structures in a hypercubic lattice is studied. Spontaneous formation
and destruction of relevant bits are found, which give self-organizing, and
chaotic genetic algorithms. When spontaneous changes of effective couplings are
introduced, chaotic itinerancy of clusterings is widely seen through a feedback
mechanism, which supports dynamic stability allowing for complexity and
diversity, known as homeochaos. Second, synaptic dynamics of couplings is
studied in relation with neural dynamics. The clustering structure is formed
with a balance between external inputs and internal dynamics. Last, an
extension allowing for the growth of the number of elements is given, in
connection with cell differentiation. Effective time sharing system of
resources is formed in partially ordered states.Comment: submitted to Physica D, no figures include
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
We consider the weighted belief-propagation (WBP) decoder recently proposed
by Nachmani et al. where different weights are introduced for each Tanner graph
edge and optimized using machine learning techniques. Our focus is on
simple-scaling models that use the same weights across certain edges to reduce
the storage and computational burden. The main contribution is to show that
simple scaling with few parameters often achieves the same gain as the full
parameterization. Moreover, several training improvements for WBP are proposed.
For example, it is shown that minimizing average binary cross-entropy is
suboptimal in general in terms of bit error rate (BER) and a new "soft-BER"
loss is proposed which can lead to better performance. We also investigate
parameter adapter networks (PANs) that learn the relation between the
signal-to-noise ratio and the WBP parameters. As an example, for the (32,16)
Reed-Muller code with a highly redundant parity-check matrix, training a PAN
with soft-BER loss gives near-maximum-likelihood performance assuming simple
scaling with only three parameters.Comment: 5 pages, 5 figures, submitted to ISIT 201
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