265 research outputs found
How shoud prey animals respond to uncertain threats?
A prey animal surveying its environment must decide whether there is a
dangerous predator present or not. If there is, it may flee. Flight has an
associated cost, so the animal should not flee if there is no danger. However,
the prey animal cannot know the state of its environment with certainty, and is
thus bound to make some errors. We formulate a probabilistic automaton model of
a prey animal's life and use it to compute the optimal escape decision
strategy, subject to the animal's uncertainty. The uncertainty is a major
factor in determining the decision strategy: only in the presence of
uncertainty do economic factors (like mating opportunities lost due to flight)
influence the decision. We performed computer simulations and found that
\emph{in silico} populations of animals subject to predation evolve to display
the strategies predicted by our model, confirming our choice of objective
function for our analytic calculations. To the best of our knowledge, this is
the first theoretical study of escape decisions to incorporate the effects of
uncertainty, and to demonstrate the correctness of the objective function used
in the model.Comment: 5 figures, 10 pages of tex
Hamiltonian Monte Carlo Without Detailed Balance
We present a method for performing Hamiltonian Monte Carlo that largely
eliminates sample rejection for typical hyperparameters. In situations that
would normally lead to rejection, instead a longer trajectory is computed until
a new state is reached that can be accepted. This is achieved using Markov
chain transitions that satisfy the fixed point equation, but do not satisfy
detailed balance. The resulting algorithm significantly suppresses the random
walk behavior and wasted function evaluations that are typically the
consequence of update rejection. We demonstrate a greater than factor of two
improvement in mixing time on three test problems. We release the source code
as Python and MATLAB packages.Comment: Accepted conference submission to ICML 2014 and also featured in a
special edition of JMLR. Since updated to include additional literature
citation
Optimal control of transitions between nonequilibrium steady states
Biological systems fundamentally exist out of equilibrium in order to
preserve organized structures and processes. Many changing cellular conditions
can be represented as transitions between nonequilibrium steady states, and
organisms have an interest in optimizing such transitions. Using the
Hatano-Sasa Y-value, we extend a recently developed geometrical framework for
determining optimal protocols so that it can be applied to systems driven from
nonequilibrium steady states. We calculate and numerically verify optimal
protocols for a colloidal particle dragged through solution by a translating
optical trap with two controllable parameters. We offer experimental
predictions, specifically that optimal protocols are significantly less costly
than naive ones. Optimal protocols similar to these may ultimately point to
design principles for biological energy transduction systems and guide the
design of artificial molecular machines.Comment: Accepted for publication at PLoS ON
Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus
We have developed a sparse mathematical representation of speech that
minimizes the number of active model neurons needed to represent typical speech
sounds. The model learns several well-known acoustic features of speech such as
harmonic stacks, formants, onsets and terminations, but we also find more
exotic structures in the spectrogram representation of sound such as localized
checkerboard patterns and frequency-modulated excitatory subregions flanked by
suppressive sidebands. Moreover, several of these novel features resemble
neuronal receptive fields reported in the Inferior Colliculus (IC), as well as
auditory thalamus and cortex, and our model neurons exhibit the same tradeoff
in spectrotemporal resolution as has been observed in IC. To our knowledge,
this is the first demonstration that receptive fields of neurons in the
ascending mammalian auditory pathway beyond the auditory nerve can be predicted
based on coding principles and the statistical properties of recorded sounds.Comment: For Supporting Information, see PLoS website:
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100259
- …