22 research outputs found
Learning, Generalization, and Functional Entropy in Random Automata Networks
It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that
feedforward Boolean networks can learn to perform specific simple tasks and
generalize well if only a subset of the learning examples is provided for
learning. Here, we extend this body of work and show experimentally that random
Boolean networks (RBNs), where both the interconnections and the Boolean
transfer functions are chosen at random initially, can be evolved by using a
state-topology evolution to solve simple tasks. We measure the learning and
generalization performance, investigate the influence of the average node
connectivity , the system size , and introduce a new measure that allows
to better describe the network's learning and generalization behavior. We show
that the connectivity of the maximum entropy networks scales as a power-law of
the system size . Our results show that networks with higher average
connectivity (supercritical) achieve higher memorization and partial
generalization. However, near critical connectivity, the networks show a higher
perfect generalization on the even-odd task
How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations
Whereas the relationship between criticality of gene regulatory networks
(GRNs) and dynamics of GRNs at a single cell level has been vigorously studied,
the relationship between the criticality of GRNs and system properties at a
higher level has remained unexplored. Here we aim at revealing a potential role
of criticality of GRNs at a multicellular level which are hard to uncover
through the single-cell-level studies, especially from an evolutionary
viewpoint. Our model simulated the growth of a cell population from a single
seed cell. All the cells were assumed to have identical GRNs. We induced
genetic perturbations to the GRN of the seed cell by adding, deleting, or
switching a regulatory link between a pair of genes. From numerical
simulations, we found that the criticality of GRNs facilitated the formation of
nontrivial morphologies when the GRNs were critical in the presence of the
evolutionary perturbations. Moreover, the criticality of GRNs produced
topologically homogenous cell clusters by adjusting the spatial arrangements of
cells, which led to the formation of nontrivial morphogenetic patterns. Our
findings corresponded to an epigenetic viewpoint that heterogeneous and complex
features emerge from homogeneous and less complex components through the
interactions among them. Thus, our results imply that highly structured tissues
or organs in morphogenesis of multicellular organisms might stem from the
criticality of GRNs.Comment: 34 pages, 17 figures, 1 tabl
A simple method for detecting chaos in nature
Chaos, or exponential sensitivity to small perturbations, appears everywhere
in nature. Moreover, chaos is predicted to play diverse functional roles in
living systems. A method for detecting chaos from empirical measurements should
therefore be a key component of the biologist's toolkit. But, classic
chaos-detection tools are highly sensitive to measurement noise and break down
for common edge cases, making it difficult to detect chaos in domains, like
biology, where measurements are noisy. However, newer tools promise to overcome
these limitations. Here, we combine several such tools into an automated
processing pipeline, and show that our pipeline can detect the presence (or
absence) of chaos in noisy recordings, even for difficult edge cases. As a
first-pass application of our pipeline, we show that heart rate variability is
not chaotic as some have proposed, and instead reflects a stochastic process in
both health and disease. Our tool is easy-to-use and freely available