Neuro-Robotic Models of Learning and Addiction

Summary

Principal Investigator: O Sporns
Abstract: DESCRIPTION: (provided by applicant) The overall aim of this research program is to provide a realistic computational model of how neuromodulatory systems act upon cortical and subcortical networks of the human brain, and of how their actions influence normal and addictive modes of behavior in the real world. An integrated systems-level computational approach will be pursued in order to (a) discover and implement anatomical and physiological principles underlying neuromodulatory functions, (b) study their integration with other brain areas and processes, especially those underlying learning and memory, and (c) study the interactions between (internal) neural events and (external) behaviors. In stages of increasing complexity, a detailed neuronal network model of sensory and motor cortical areas, subcortical circuits and neuromodulatory nuclei will be designed and implemented in an autonomous robot. The model will incorporate realistic anatomical and physiological properties and be capable of plastic changes in connectivity depending upon actual sensory experience and behavior. In a first stage, we plan to implement a neuromodulatory system with properties similar to a midbrain dopamine system, producing neural responses that are related to reward and reward predicting stimuli. In addition to this reward system we will implement a separate system responsive to aversive stimuli and investigate possible modes of functional interaction between them. In order to investigate the hypothesized connection between the development of addictive behavior and processes related to memory and learning we will expand the model to include additional cortical and subcortical networks. Our modeling studies will allow us to provide an analysis of the causal roles played by different components of the neural architecture, of pharmacological and physiological properties, of learning and memory and of actual behavior in the switch from normal and controlled modes of behavior to addiction. A comprehensive and detailed embodied (robot) model has the potential of serving as a unique explanatory and predictive tool aiding in future empirical research on drug abuse and addiction.
Funding Period: 2002-09-01 - 2005-08-31
more information: NIH RePORT

Top Publications

  1. ncbi Methods for quantifying the informational structure of sensory and motor data
    Max Lungarella
    Department of Mechano Informatics, School of Information Science and Technology, University of Tokyo, 113 8656 Tokyo, Japan
    Neuroinformatics 3:243-62. 2005
  2. pmc The human connectome: A structural description of the human brain
    Olaf Sporns
    Department of Psychology, Indiana University, Bloomington, Indiana, United States of America
    PLoS Comput Biol 1:e42. 2005
  3. ncbi A large-scale neurocomputational model of task-oriented behavior selection and working memory in prefrontal cortex
    George L Chadderdon
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
    J Cogn Neurosci 18:242-57. 2006

Scientific Experts

  • O Sporns
  • George L Chadderdon
  • Max Lungarella
  • Daniel Bulwinkle
  • Teresa Pegors

Detail Information

Publications3

  1. ncbi Methods for quantifying the informational structure of sensory and motor data
    Max Lungarella
    Department of Mechano Informatics, School of Information Science and Technology, University of Tokyo, 113 8656 Tokyo, Japan
    Neuroinformatics 3:243-62. 2005
    ....
  2. pmc The human connectome: A structural description of the human brain
    Olaf Sporns
    Department of Psychology, Indiana University, Bloomington, Indiana, United States of America
    PLoS Comput Biol 1:e42. 2005
    ..We propose a research strategy to achieve this goal, and discuss its potential impact...
  3. ncbi A large-scale neurocomputational model of task-oriented behavior selection and working memory in prefrontal cortex
    George L Chadderdon
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
    J Cogn Neurosci 18:242-57. 2006
    ..Our simulation results suggest a range of predictions for behavioral, single-cell, and neuroimaging response data under the proposed task set and under manipulations of DA concentration...