otherpubs.bib

@phdthesis{rozell.07,
  author = {Rozell, C.J.},
  title = {Distributed redundant representations in man-made and
biological sensing systems},
  school = {Rice University},
  year = 2007,
  month = {May},
  address = {Houston, TX},
  url = {http://www.ece.rice.edu/~crozell/pubs/crozell_PhDThesis_final.pdf},
  abstract = {The ability of a man-made or biological system to understand its
environment is limited by the methods used to process sensory
information. In particular, the data representation is often a
critical component of such systems. Neural systems represent sensory
information using distributed populations of neurons that are highly
redundant. Understanding the role of redundancy in
distributed systems is important both to understanding neural systems
and to efficiently solving many modern signal processing problems.

This thesis makes contributions to understanding redundant
representations in distributed processing systems in three specific
areas. First, we explore the robustness of redundant representations
by generalizing existing results regarding noise-reduction to
Poisson process modulation. Additionally, we characterize how the
noise-reduction ability of redundant representation is weakened when
we enforce a distributed processing constraint on the system.

Second, we explore the task of managing redundancy in the context of
distributed settings through the specific example of wireless sensor
and actuator networks (WSANs).  Using a crayfish reflex behavior as a
guide, we develop an analytic WSAN model that implements control laws
in a completely distributed manner.  We also develop an algorithm to
optimize the system resource allocation by adjusting the number of
bits used to quantize messages on each sensor-actuator communication
link.  This optimal power scheduling yields several orders of
magnitude in power savings over uniform allocation strategies that use
a fixed number of bits on each communication link.

Finally, we explore the flexibility of redundant representations for
sparse approximation.  Neuroscience and signal processing both need a
sparse approximation algorithm (i.e., representing a signal with few
non-zero coefficients) that is physically implementable in a parallel
system and produces smooth coefficient time-series for time-varying
signals (e.g., video). We present a class of \emph{locally competitive
algorithms} (LCAs) that minimize a weighted combination of
mean-squared error and a coefficient cost function. LCAs produce
coefficients with sparsity levels comparable to centralized algorithms
while being more realistic for physical implementation. The resultant
LCA coefficients for video sequences are more regular (i.e., smoother
and more predictable) than the coefficients produced by existing
algorithms.}
}

@techreport{rozell.07d,
  author = {Rozell, C.J.},
  title = {Dynamic Systems for Sparse Coding},
  institution = {Walter Karplus Summer
    Research Grant Final Report},
  year = 2007,
  month = {February},
  abstract = {Despite evidence that neural systems may be employing sparse
stimulus coding, the mechanisms underlying this ability are not
understood.  I present a class of neurally plausible {\em locally
competitive algorithms} (LCAs) that correspond to a collection of
sparse approximation principles.  These systems minimize a combination
of MSE and a coefficient cost function.  LCAs use a combination of
thresholding and inhibitory connections to induce local (possibly
one-way) competitions.  LCAs demonstrate sparsity levels comparable to
existing sparse coding algorithms while being much more realistic for
neural implementation.  Additionally, LCAs coefficients for video
sequences demonstrate inertial properties, making them both
qualitatively and quantitatively more regular (i.e., smoother and more
predictable) than the coefficients produced by greedy algorithms.  In
addition to being a first step toward an experimentally testable
hypothesis of biological mechanisms, LCAs may provide new methods for
hardware implementations of sensing systems (e.g., digital cameras)
and new front-end representations for both video coding and computer
vision tasks.}
}

@mastersthesis{rozell.02,
  author = {Rozell, C.J.},
  title = {Analyzing dynamics and stimulus feature dependence in the information processing of crayfish sustaining fibers},
  school = {Rice University},
  year = {2002},
  address = {Houston, TX},
  month = {May},
  abstract = {The sustaining fiber (SF) stage of the crayfish
	 visual system converts analog stimulus representations to spike train
	 signals. A recent theory quantifies a system's information processing
	 capabilities and relates to statistical signal processing. To analyze
	 SF responses to light stimuli, we extend a wavelet-based algorithm
	 for separating analog input signals and spike output waveforms in
	 composite intracellular recordings. We also present a time-varying
	 RC circuit model to capture nonstationary membrane noise spectral
	 characteristics. In our SF analysis, information transfer ratios are
	 generally on the order of $10^{-4}$. The SF information processing
	 dynamics show transient peaks followed by decay to steady-state values.
	 A simple theoretical spike generator is analyzed analytically and
	 shows general dynamic and steady-state properties similar to SFs. The
	 information transfer ratios increase with spike rate and dynamic
	 properties are due to direct spike generator dependence on input changes.},
  url = {http://www.ece.rice.edu/~crozell/pubs/rozellMS2002.pdf}
}