New vision & pattern recognition papers available

[Jonathan A. Marshall]

I would like to announce the availability of several new papers on vision,
pattern recognition, and neural systems.  These papers may be obtained from

  http://www.cs.unc.edu/~marshall

--Jonathan A. Marshall       marshall at computer.org
    Dept of Computer Science, Univ of North Carolina, Chapel Hill, NC, USA.
    Visionics Corp., Jersey City, NJ, USA.

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  Gupta VS, Alley RK, Marshall JA, "Development of triadic neural circuits
  for visual image stabilization under eye movements."  Submitted for
  journal publication, July 1998.

    Human visual systems maintain a stable internal representation of a
    scene even though the image on the retina is constantly changing because
    of eye movements.  Such stabilization can theoretically be effected by
    dynamic shifts in the receptive field (RF) of neurons in the visual
    system.  This paper examines how a neural circuit can learn to generate
    such shifts.  The shifts are controlled by eye position signals and
    compensate for the movement in the retinal image caused by eye
    movements.  The development of a neural shifter circuit (Olshausen,
    Anderson, & Van Essen, 1992) is modeled using triadic connections.
    These connections are gated by signals that indicate the direction of
    gaze (eye position signals).  In simulations, a neural model is exposed
    to sequences of stimuli paired with appropriate eye position signals.
    The initially nonspecific gating weights change, using a triadic
    learning rule.  The pattern of gating develops so that different eye
    position signals selectively gate pathways from different positions
    within the visual field.  Neurons then exhibit dynamic RF shifts,
    responding to the preferred stimulus within the RF and continuing to
    respond when the stimulus moves because of a shift in eye position.  The
    triadic learning rule thus produces a shifter circuit that exhibits
    visual image stabilization.  Traditional dyadic networks and learning
    rules do not produce such behavior.  The self-organization capability of
    the model reduces the need for detailed pre-wiring or specific genetic
    programming of development.  This shifter circuit model may also help in
    analyzing the behavior and formation of anticipatory RF shifts, which
    can reduce latency of visual response after eye movements, and
    attention-modulated changes in visual processing.

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  Marshall JA, Gupta VS, "Generalization and exclusive allocation of
  credit in unsupervised category learning."  Network: Computation in
  Neural Systems 9:279-302, May 1998.

    A new way of measuring generalization in unsupervised learning is
    presented.  The measure is based on an exclusive allocation, or credit
    assignment, criterion.  In a classifier that satisfies the criterion,
    input patterns are parsed so that the credit for each input feature is
    assigned exclusively to one of multiple, possibly overlapping, output
    categories.  Such a classifier achieves context-sensitive, global
    representations of pattern data.  Two additional constraints, sequence
    masking and uncertainty multiplexing, are described; these can be used
    to refine the measure of generalization.  The generalization performance
    of EXIN networks, winner-take-all competitive learning networks, linear
    decorrelator networks, and Nigrin's SONNET-2 network is compared.

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  Marshall JA, Schmitt CP, Kalarickal GJ, Alley RK, "Neural model of
  transfer-of-binding in visual relative motion perception."  To appear
  in Computational Neuroscience: Trends in Research, 1998.  January 1998.

    How can a visual system or cognitive system use the changing
    relationships between moving visual elements to decide which elements
    belong together as groups (or objects)?  We have constructed a neural
    circuit model that selects object groupings based on global Gestalt
    common-fate evidence and uses information about the behavior of each
    group to predict the behavior of elements of the group.  A simple
    competitive neural circuit binds elements into a representation of an
    object.  Information about the spiking pattern of neurons allows
    transfer of the bindings of an object representation from location to
    location in the neural circuit as the object moves.  The model exhibits
    characteristics of human object grouping and solves some key neural
    circuit design problems in visual relative motion perception.

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  Marshall JA, Srikanth V, "Curved trajectory prediction using a
  self-organizing neural network."  Submitted for journal publication,
  September 1997.

    Existing neural network models are capable of tracking linear
    trajectories of moving visual objects.  This paper describes an
    additional neural mechanism, disfacilitation, that enhances the ability
    of a visual system to track curved trajectories.  The added mechanism
    combines information about an object's trajectory with information about
    changes in the object's trajectory, to improve the estimates for the
    object's next probable location.  Computational simulations are
    presented that show how the neural mechanism can learn to track the
    speed of objects and how the network operates to predict the
    trajectories of accelerating and decelerating objects.

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These five papers form part of Dr. George Kalarickal's recent dissertation:

Kalarickal GJ, Theory of Cortical Plasticity in Vision.  PhD Dissertation,
Department of Computer Science, University of North Carolina at Chapel Hill,
1998.

  Kalarickal GJ, Marshall JA, "Comparison of generalized Hebbian rules
  for long-term synaptic plasticity."  Submitted for journal
  publication, July 1998.

  Kalarickal GJ, Marshall JA, "The role of afferent excitatory and
  lateral inhibitory synaptic plasticity in visual cortical ocular
  dominance plasticity."  Submitted for journal publication, July 1998.

  Kalarickal GJ, Marshall JA, "Plasticity in cortical neuron properties:
  Modeling the effects of an NMDA antagonist and a GABA agonist during
  visual deprivation."  Submitted for journal publication, July 1998.

  Kalarickal GJ, Marshall JA, "Models of receptive field dynamics in
  visual cortex."  Submitted for journal publication, May 1998.

  Kalarickal GJ, Marshall JA, "Rearrangement of receptive field
  topography after intracortical and peripheral stimulation: The role of
  plasticity in inhibitory pathways."  Submitted for journal
  publication, July 1998.

    A theory of postnatal activity-dependent neural plasticity based on
    synaptic weight modification is presented.  Synaptic weight
    modifications are governed by simple variants of a Hebbian rule for
    excitatory pathways and an anti-Hebbian rule for inhibitory pathways.
    The dissertation focuses on modeling the following cortical phenomena:
    long-term potentiation and depression (LTP and LTD); dynamic receptive
    field changes during artificial scotoma conditioning in adult animals;
    adult cortical plasticity induced by bilateral retinal lesions,
    intracortical microstimulation (ICMS), and repetitive peripheral
    stimulation; changes in ocular dominance during "classical" rearing
    conditioning; and the effect of neuropharmacological manipulations on
    plasticity.  Novel experiments are proposed to test the predictions of
    the proposed models, and the models are compared with other models of
    cortical properties.

    The models presented in the dissertation provide insights into the
    neural basis of perceptual learning.  In perceptual learning, persistent
    changes in cortical neuronal receptive fields are produced by
    conditioning procedures that manipulate the activation of cortical
    neurons by repeated stimulation of localized regions.  Thus, the
    analysis of synaptic plasticity rules for receptive field changes
    produced by conditioning procedures that activate small groups of
    neurons can also elucidate the neural basis of perceptual learning.

    Previous experimental and theoretical work on cortical plasticity
    focused mainly on afferent excitatory synaptic plasticity.  The novel
    and unifying theme in this work is self-organization and the use of the
    lateral inhibitory synaptic plasticity rule.  Many cortical properties,
    e.g., orientation selectivity, motion selectivity, spatial frequency
    selectivity, etc. are produced or strongly influenced by inhibitory
    interactions.  Thus, changes in these properties could be produced by
    lateral inhibitory synaptic plasticity.

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