otherpubs.bib

@inproceedings{rozell.08c,
  author = {Rozell, C.J.},
  title = {Analog and Digital Sparse Approximation with Applications to Compressed Sensing},
  booktitle = {2008 SIAM Conference on Imaging Science},
  year = 2008,
  address = {San Diego, CA},
  month = {July},
  note = {Invited talk.},
  abstract = {In the compressed sensing (CS) framework, the sampling 
phase is resource constrained, taking a small number of 
linear samples. However, the price paid for this simplicity is a computationally expensive reconstruction algorithm 
that forms a bottleneck in using the sensed data. We will 
present a sparse approximation framework that both unifies many of the recently proposed digital algorithms and 
introduces novel analog architectures that solve the same 
problems. We will demonstrate how these analog systems 
solve CS recovery problems orders of magnitude faster than 
current digital systems, at speeds limited only by the underlying hardware components.
}
}

@inproceedings{rozell.08,
  author = {Rozell, C.J. and Johnson, D.H. and Baraniuk, R.G. and Olshausen, B.A.},
  title = {Neural architectures for sparse approximation},
  booktitle = {Information Theory and Applications Workshop},
  year = 2008,
  address = {La Jolla, CA},
  month = {January},
  note = {Invited talk.},
  abstract = { Modeling naturally occurring signals as being sparsely
                  representable has seen increased utility in many
                  recent signal processing applications.
                  Interestingly, recent evidence indicates that
                  sensory neural systems may be employing sparse
                  approximations to represent stimuli.  However, the
                  sparse coding hypothesis remains unconfirmed and the
                  computational mechanisms that would enable neural
                  systems to solve this challenging mathematical
                  problem have been unclear.  In this talk I will
                  describe our development of a dynamical system
                  capable of solving a family of sparse approximation
                  problems using a network of simple
                  neurally-plausible analog components.  In addition
                  to providing a proposed mechanism that can be used
                  by experimentalists to test the sparse coding
                  hypothesis in neural systems, this architecture is
                  also amenable to analog hardware implementations
                  that are much faster than the digital counterparts.
                  I will describe this dynamical system architecture,
                  discuss its properties, and demonstrate its
                  performance in several relevant sensing and signal
                  processing tasks.  }
}

@phdthesis{rozell.07,
  author = {Rozell, C.J.},
  title = {Distributed redundant representations in man-made and
biological sensing systems},
  school = {Rice University},
  year = 2007,
  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.}
}

@inproceedings{johnson.06b,
  author = {Johnson, D.H. and Rozell, C.J. and Goodman, I.N.},
  title = {Information Theory and Neuroscience: {A} Tutorial},
  booktitle = {Gulf Coast Consortium Conference
on Theoretical \& Computational Neuroscience},
  year = 2006,
  address = {Houston, TX},
  month = {November},
  note = {Invited talk.},
  abstract = {When Shannon developed information theory, he envisioned
  a systematic way to determine how much "information" could be
  transmitted over an arbitrary communications channel. While this
  classic work embraces many of the key aspects of neural
  communication (e.g., stochastic stimuli and communication signals,
  multiple-neuron populations, etc.), there are difficulties in
  applying his concepts meaningfully to neuroscience applications. We
  describe the classic information theoretic quantities---entropy,
  mutual information, and capacity---and how they can be used to
  assess the ultimate fidelity of the neural stimulus
  representation. We also discuss some of the problems that accompany
  using and interpreting these quantities in a neuroscience
  context. We also present an overview of post-Shannon research areas
  that leverage his work in rate-distortion theory that are extremely
  relevant to neuroscientists looking to understand the neural
  code. The presentation is meant to be mostly tutorial in nature,
  setting the stage for succeeding presentations.}
}

@inproceedings{olshausen.06,
  author = {Olshausen, B.A. and Rozell, C.J. and Johnson, D.H. and Baraniuk, R.G.},
  title = {Sparse Coding via Thresholding and Local Competition},
  booktitle = {Gordon Research Conference on Sensory Coding and the Natural Environment},
  year = 2006,
  address = {Big Sky, MT},
  month = {August},
  note = {Contributed poster.}
}

@inproceedings{johnson.06,
  author = {Johnson, D.H. and Rozell, C.J.},
  title = {Information Theory and Neuroscience},
  booktitle = {Computational Neuroscience
 Meeting Workshop on Methods of Information Theory in
 Computational Neuroscience},
  year = 2006,
  address = {Edinburgh, UK},
  month = {July},
  note = {Invited talk.},
  abstract = {When Shannon developed information theory, he envisioned
a systematic way to determine how much "information" could be
transmitted over an arbitrary communications channel. While this
classic work embraces many of the key aspects of neural communication
(e.g., stochastic stimuli and communication signals, multiple-neuron
populations, etc.), there are difficulties in applying his concepts
meaningfully to neuroscience applications. We describe the classic
information theoretic quantities---entropy, mutual information, and
capacity---and how they can be used to assess the ultimate fidelity of
the neural stimulus representation. We also discuss some of the
problems that accompany using and interpreting these quantities in a
neuroscience context. We also present an overview of post-Shannon
research areas that leverage his work in rate-distortion theory that
are extremely relevant to neuroscientists looking to understand the
neural code. The presentation is meant to be mostly tutorial in
nature, setting the stage for other workshop presentations.}
}

@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}
}