Published Abstracts

EFFECTS OF EXCITATORY AMINO ACID ANTAGONISTS NBQX AND MK-801 ON THE DEVELOPING BRAIN.  Suzanne J. Werner, Gregory L. Holmes, Zhao Liu, Rainer Paine, L. Carmant, Mohamad Mikati.  Department of Neurology, Children's Hospital, Harvard Medical School, Boston, Massachusetts.

     In the ongoing search for pharmacological treatment of epilepsy Excitatory Amino Acid (EAA) antagonists are being considered due to EAAs' role in seizure-induced brain damage.  To determine the long-term effects of EAA antagonists on the developing brain we administered EAA antagonists via osmotic pumps to 20 day old rats and studied their effects.  Non-NMDA antagonist NBQX  or NMDA antagonist MK-801 was administered over seven days by osmotic pumps stereotactically placed in the left lateral ventricle.  Three groups were studied:  Group 1 (N=23) received 1.5M or 3M NBQX pumped at a rate of 1microL/h, Group II (N=19) received 0.7M MK-801 at a rate of 1 microL/h, Group III (N=18) received vehicle at a rate of 1 microL/h.  Thirty days after pump placement each animal underwent handling and open-field testing, Morris water maze, and flurothyl testing.
     Handling test showed no difference between test animals and controls, while open-field showed that both Group I and Group II were more active than controls (p=0.0049).  Water maze data showed that, although NBQX animals were slower to find the platform than controls, the difference was not significant.  The NBQX group showed increased seizure susceptibility with reduced latency to onset of seizure (p=0.0049) when compared to controls.  A higher mortality rate of both drug groups, NBQX (65%) and MK-801 (54%), was found compared to controls (27%).
     These studies show that manipulation of EEA levels in the developing brain, even for short periods, may have significant detrimental effects.  These possible effects should be considered when preparing therapies for seizure treatment in children.

     Status epilepticus (SE) may cause death, mental retardation, and epilepsy.  Phenobarbital may stop SE at sufficiently high doses, but it may be neurotoxic in the presence of epileptic activity at low doses.  We hypothesized that a low dose of phenobarbital plus nimodipine may have a beneficial combined effect.  We induced SE in rats on postnatal day 35 with kainic acid and compared phenobarbital plus nimodipine on auditory quality discrimination.  Rats were trained to press a lever for food reinforcement.  An S+ alternated randomly an S- in a discrete trial procedure.  Saline animals acquired the discrimination in 1 to 4 sessions.  SE plus phenobarbital animals were markedly impaired.  SE plus nimodipine animals were also impaired.  Animals with SE, phenobarbital and nimodipine treatment were largely protected from long term impairment.  We conclude that nimodipine plus phenobarbital provides neuroprotection from the effects of status epilepticus on performance of simple discrimination.

   Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children.  Such complex sequential acts are broken down into distinct motor control synergies which overlap in time to generate continuous, curved movements that obey an inverse relation
between curvature and speed (Morasso, 1981).  How are such complex movements learned through attentive imitation?  Novel movements may be made as a series of distinct segments, but a practiced movement can be made smoothly, with a continuous, often bell-shaped, velocity profile (Abend et al., 1982).   How does learning transform reactive imitation into predictive and automatic performance?  A neural model is presented which suggests how parietal and motor cortical mechanisms, such as directional vector encoding (Bullock & Grossberg, 1988; Georgopoulos et al., 1982), interact with adaptively-timed, predictive cerebellar learning (Fiala et al., 1996; Perrett et al., 1993) during movement imitation and predictive performance.  To initiate movement, visual attention shifts along the shape to be imitated and generates vector movement using motor cortical cells.  During such an imitative movement, cerebellar cells with a spectrum of delayed response profiles sample and learn the changing directional information and, in turn, send that learned information back to the cortex and eventually to the muscle synergies involved.  If the
imitative movement deviates from an attentional focus around a shape to be imitated, the visual system shifts attention, and may saccade, back to the shape, thereby providing corrective directional information to the arm movement system.  This imitative movement cycle repeats until memory alone can accurately drive the movement.  The model is used to simulate key psychophysical and neural data about imitative and predictive control of curved movements.

  Much sensory-motor behavior develops through imitation, as during the
  learning of handwriting by children. Such complex sequential acts are
  broken down into distinct motor control synergies which overlap in time to
  generate continuous, curved movements that obey an inverse relation
  between curvature and speed (Morasso, 1981). How does learning of
  complex movements transform reactive imitation into predictive,
  automatic performance? A neural model is presented which suggests how
  parietal and motor cortical mechanisms, such as directional vector
  encoding (Bullock & Grossberg, 1988; Georgopoulos et al., 1982),
  interact with adaptively-timed, predictive cerebellar learning (Fiala et al.,
  1996; Perrett et al., 1993) during movement imitation and predictive
  performance. To initiate movement, visual attention shifts along the shape
  to be imitated and generates vector movement using motor cortical cells.
  Cerebellar cells with a spectrum of delayed response profiles sample and
  learn the changing directional information and send that learned
  information back to the cortex. If the imitative movement deviates from an
  attentional focus around a shape, the visual system shifts attention back to
  it, thereby providing corrective directional information. This imitative
  movement cycle repeats until memory alone can accurately drive the
  movement. A cortical working memory buffer transiently stores the
  cerebellar output, allowing speed scaling of learned movements, limited by
  the rate of cerebellar readout. Movements can be learned at variable speeds
  if cerebellar spectral density varies with speed. Learning at slower speeds
  facilitates learning at faster speeds. Size can be varied after learning while
  maintaining isochronicity. Context effects arise from the overlap of
  cerebellar spectral outputs. The model is used to simulate key
  psychophysical and neural data about learning curved movements.
  Supported by: DARPA/ONR N00014-95-10409^1,2,NIH
  1-R29-DC02952-01¹, ONR  N00014-92-J-1309^1,2,and NSF
  IRI-97-20333².

Much sensory­motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. The Adaptive Vector Integration to Endpoint Handwriting (AVITEWRITE) model of Grossberg and Paine (2000) addressed how such complex movements may be learned through attentive imitation. The model suggested how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively­timed, predictive cerebellar learning during movement imitation and predictive performance. Key psychophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell­shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two­Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing the human trajectories. The results show that model performance was variable across the subjects, with an average correlation between the model and human data of 0.89 +/-0.10. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and the learning of other complex sensory-motor skills would benefit from further research.

Paine, R.W., Tani, J.  (2004)

This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of dynamic structures self-organize in the lower and higher levels of the network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activation states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems. The biological plausibility of the model's essential principles is discussed.
 

Paine, R.W., Tani, J. (2004a)

This study describes how complex goal-directed behavior can evolve in a hierarchically organized recurrent neural network controlling a simulated Khepera robot.  Different types of dynamic structures self-organize in the lower and higher levels of a network for the purpose of achieving complex navigation tasks.  The parametric bifurcation structures that appear in the lower level ex-plain the mechanism of how behavior primitives are switched in a top-down way.  In the higher level, a topologically ordered mapping of initial cell activa-tion states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems.  A further experi-ment tests the evolved controller's adaptability to changes in its environment.  The biological plausibility of the model's essential principles is discussed.

Paine, R.W., Tani, J. (2004b)

This study describes how complex goal-directed behavior can be obtained through adaptation in a hierarchically organized recurrent neural network using a genetic algorithm.  Robot simulations showed that different types of dynamic structures self-organize in the lower and higher levels of the network for the purpose of achieving complex navigation tasks.  Behavior primitives are switched in a top-down way through lower level parametric bifurcation structures. In the higher level, a topologically ordered mapping of initial cell activation states to motor-primitive sequences self-organizes by utilizing the initial sensitivity characteristics of nonlinear dynamical systems.  The biological plausibility of the model's essential principles is discussed.