885 research outputs found
Predictability and hierarchy in Drosophila behavior
Even the simplest of animals exhibit behavioral sequences with complex
temporal dynamics. Prominent amongst the proposed organizing principles for
these dynamics has been the idea of a hierarchy, wherein the movements an
animal makes can be understood as a set of nested sub-clusters. Although this
type of organization holds potential advantages in terms of motion control and
neural circuitry, measurements demonstrating this for an animal's entire
behavioral repertoire have been limited in scope and temporal complexity. Here,
we use a recently developed unsupervised technique to discover and track the
occurrence of all stereotyped behaviors performed by fruit flies moving in a
shallow arena. Calculating the optimally predictive representation of the fly's
future behaviors, we show that fly behavior exhibits multiple time scales and
is organized into a hierarchical structure that is indicative of its underlying
behavioral programs and its changing internal states
Neural coding of naturalistic motion stimuli
We study a wide field motion sensitive neuron in the visual system of the
blowfly {\em Calliphora vicina}. By rotating the fly on a stepper motor outside
in a wooded area, and along an angular motion trajectory representative of
natural flight, we stimulate the fly's visual system with input that approaches
the natural situation. The neural response is analyzed in the framework of
information theory, using methods that are free from assumptions. We
demonstrate that information about the motion trajectory increases as the light
level increases over a natural range. This indicates that the fly's brain
utilizes the increase in photon flux to extract more information from the
photoreceptor array, suggesting that imprecision in neural signals is dominated
by photon shot noise in the physical input, rather than by noise generated
within the nervous system itself.Comment: 15 pages, 4 figure
The thermodynamics of prediction
A system responding to a stochastic driving signal can be interpreted as
computing, by means of its dynamics, an implicit model of the environmental
variables. The system's state retains information about past environmental
fluctuations, and a fraction of this information is predictive of future ones.
The remaining nonpredictive information reflects model complexity that does not
improve predictive power, and thus represents the ineffectiveness of the model.
We expose the fundamental equivalence between this model inefficiency and
thermodynamic inefficiency, measured by dissipation. Our results hold
arbitrarily far from thermodynamic equilibrium and are applicable to a wide
range of systems, including biomolecular machines. They highlight a profound
connection between the effective use of information and efficient thermodynamic
operation: any system constructed to keep memory about its environment and to
operate with maximal energetic efficiency has to be predictive.Comment: 5 pages, 1 figur
Long time scales, individual differences, and scale invariance in animal behavior
The explosion of data on animal behavior in more natural contexts highlights
the fact that these behaviors exhibit correlations across many time scales. But
there are major challenges in analyzing these data: records of behavior in
single animals have fewer independent samples than one might expect; in pooling
data from multiple animals, individual differences can mimic long-ranged
temporal correlations; conversely long-ranged correlations can lead to an
over-estimate of individual differences. We suggest an analysis scheme that
addresses these problems directly, apply this approach to data on the
spontaneous behavior of walking flies, and find evidence for scale invariant
correlations over nearly three decades in time, from seconds to one hour. Three
different measures of correlation are consistent with a single underlying
scaling field of dimension
Coarse--graining, fixed points, and scaling in a large population of neurons
We develop a phenomenological coarse--graining procedure for activity in a
large network of neurons, and apply this to recordings from a population of
1000+ cells in the hippocampus. Distributions of coarse--grained variables seem
to approach a fixed non--Gaussian form, and we see evidence of scaling in both
static and dynamic quantities. These results suggest that the collective
behavior of the network is described by a non--trivial fixed point
Predicting rare events in chemical reactions: application to skin cell proliferation
In a well-stirred system undergoing chemical reactions, fluctuations in the
reaction propensities are approximately captured by the corresponding chemical
Langevin equation. Within this context, we discuss in this work how the Kramers
escape theory can be used to predict rare events in chemical reactions. As an
example, we apply our approach to a recently proposed model on cell
proliferation with relevance to skin cancer [P.B. Warren, Phys. Rev. E {\bf
80}, 030903 (2009)]. In particular, we provide an analytical explanation for
the form of the exponential exponent observed in the onset rate of uncontrolled
cell proliferation.Comment: New materials and references added. To appear in Physical Review
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