jourpubs.bib

@article{rozell.06d,
  author = {Rozell, C.J. and Johnson, D.H.},
  title = {Resource allocation and control in redundant wireless sensor and actuator networks},
  note = {In preparation.}
}
@article{rozell.06c,
  author = {Rozell, C.J. and Johnson, D.H and Baraniuk, R.G. and Olshausen, B.A.},
  title = {Sparse coding via thresholding and local competition in neural circuits},
  journal = {Neural Computation},
  year = {2008},
  volume = {20},
  number = {10},
  pages = {2526--2563},
  month = {October},
  abstract = {While evidence indicates that neural systems may be
                  employing sparse approximations to represent sensed
                  stimuli, the mechanisms underlying this ability are
                  not understood. We describe a local ly competitive
                  algorithm (LCA) that solves a collection of sparse
                  coding principles minimizing a weighted combination
                  of mean-squared error (MSE) and a coefficient cost
                  function. LCAs are designed to be implemented in a
                  dynamical system composed of many neuron-like
                  elements operating in parallel. These algorithms use
                  thresholding functions to induce local (usually
                  one-way) inhibitory competitions between nodes to
                  produce sparse representations. LCAs produce
                  coefficients with sparsity levels comparable to the
                  most popular centralized sparse coding algorithms
                  while being readily suited for neural
                  implementation. Addi- tionally, LCA coefficients for
                  video sequences demonstrate inertial properties that
                  are both qualitatively and quantitatively more
                  regular (i.e., smoother and more predictable) than
                  the coefficients produced by greedy algorithms. },
  url = {http://www.ece.rice.edu/~crozell/pubs/rozellNeuralComp2008.pdf}
}
@article{bishnoi.06,
  author = {Bishnoi,S.W. and Rozell,C.J. and Levin,C.S. and Gheith,M.K. and Johnson,B.R. and Johnson,D.H. and Halas, N.J},
  title = {All-optical nanoscale {pH} meter},
  journal = {Nano Letters},
  year = {2006},
  volume = {6},
  number = {8},
  pages = {1687--1692},
  month = {August},
  abstract = {We show that an Au nanoshell with a pH sensitive
         molecular adsorbate functions as a standalone, all-optical nanoscale
         pH meter that monitors its local environment through the
         pH-dependent surface enhanced Raman scattering (SERS) spectra of the
         adsorbate molecules.  Moreover, we also show how the performance of
         such a functional nanodevice can be quantitatively assessed. The
         complex spectral output is reduced to a simple device characteristic
         by application of a locally linear manifold approximation
         algorithm. The average accuracy of the nano-``meter'' was found to
         be ± 0.10 pH units across its operating range.}
}
@article{rozell.05,
  author = {Rozell, C.J. and Johnson, D.H.},
  title = {Analyzing the robustness of redundant population codes in sensory and feature extraction systems},
  journal = {Neurocomputing},
  year = {2006},
  volume = {69},
  number = {10--12},
  pages = {1215--1218},
  month = {June},
  note = {Also appears in {\it Proceedings of the Computational Neuroscience Meeting (CNS)}, Madison, WI, July 2005.},
  abstract = {Sensory systems often use groups of redundant neurons to represent
         stimulus information both during transduction and population coding of
         features. This redundancy makes the system more robust to corruption
         in the representation. We approximate neural coding as a projection of
         the stimulus onto a set of vectors, with the result encoded by spike
         trains. We use the formalism of frame theory to quantify the inherent
         noise reduction properties of such population codes. Additionally,
         computing features from the stimulus signal can also be thought of as
         projecting the coefficients of a sensory representation onto another
         set of vectors specific to the feature of interest. The conditions
         under which a combination of different features form a complete
         representation for the stimulus signal can be found through a recent
         extension to frame theory called ``frames of subspaces.'' We extend
         the frame of subspaces theory to quantify the noise reduction
         properties of a collection of redundant feature spaces.},
  url = {http://www.ece.rice.edu/~crozell/pubs/rozellCNS2005.pdf}
}
@article{rozell.04c,
  author = {Rozell, C.J. and Johnson, D.H.},
  title = {Examining methods for estimating mutual information in spiking neural systems},
  journal = {Neurocomputing},
  year = {2005},
  volume = {65--66C},
  pages = {429--434},
  month = {June},
  note = {Also appears in {\it Proceedings of the Computational Neuroscience Meeting (CNS)}, Baltimore, MD, July 2004.},
  abstract = {Mutual information enjoys wide use in the computational neuroscience community for
	 analyzing spiking neural systems. Its direct calculation is difficult
	 because estimating the joint stimulus-response distribution requires a
	 prohibitive amount of data. Consequently, several techniques have
	 appeared for bounding mutual information that rely on less data. We
	 examine two upper bound techniques and find that they are either
	 unreliable or introduce strong assumptions about the neural code. We also
	 examine two lower bounds, showing that they can be very loose and
	 possibly bear little relation to the mutual information's actual value.},
  url = {http://www.ece.rice.edu/~crozell/pubs/rozellCNS2004.pdf}
}
@article{rozell.04,
  author = {Rozell, C.J. and Johnson, D.H. and Glantz, R.M.},
  title = {Measuring information transfer in crayfish sustaining fiber spike generators},
  journal = {Biological Cybernetics},
  year = {2004},
  volume = {90},
  number = {2},
  pages = {89--97},
  month = {February},
  webnote = {Copyright held by Springer-Verlag. The original
	 publication is available at springerlink.com. DOI:10.1007/s00422-003-0458-y},
  abstract = {We present a method based
	 on information-theoretic distances for measuring the information
	 transfer efficiency of voltage to impulse encoders. In response to light
	 pulses, we simultaneously recorded the EPSP and spiking output of
	 crayfish sustaining fibers. To measure the distance between analog
	 EPSP responses, we developed a membrane noise model that accurately
	 captures stimulus-induced nonstationarities. By comparing the EPSP
	 and spike responses, we found encoding efficiencies on the order
	 of $10^{-4}$, with interesting dynamics occurring during initial
	 transients. A simple analog to point-process converter predicted the small
	 information transfer efficiencies and dynamic properties we measured.},
  url = {http://www.ece.rice.edu/~crozell/pubs/rozellBC2004.pdf}
}
@article{rozell.03,
  author = {Rozell, C.J. and Johnson, D.H. and Glantz, R.M.},
  title = {Information processing during transient responses in the crayfish visual system},
  journal = {Neurocomputing},
  year = {2003},
  volume = {52--54},
  pages = {53--58},
  month = {June},
  note = {Also appears in {\it Proceedings of the Computational Neuroscience Meeting (CNS)}, Chicago, IL, July 2002.},
  abstract = {We analyzed sustaining fiber
	 responses in the crayfish visual system to light pulses using information
	 processing techniques. The light pulse stimuli elicited a transient and
	 a steady-state component in the EPSP input and in the firing rate
	 of the spike train output. The overall information transfer of the
	 system was very low ($10^{-4}$), with a sharp increase during the
	 transient portion of the response followed by a steady decrease. The
	 information transfer dynamics are consistent with a simple spike
	 generator model that depends explicitly on stimulus changes. The
	 present analysis also corroborates the observed light reflex behavior.},
  url = {http://www.ece.rice.edu/~crozell/pubs/rozellCNS2002.pdf}
}