Introduction

Chapter
Part of the Cognitive Science and Technology book series (CSAT)

Abstract

The area of computational modeling of basal ganglia has seen an explosive growth in the last couple of decades. In this area, there is currently a multitude of modeling approaches, each approaching the functions of basal ganglia in a unique fashion, pursuing a specialized line of investigation. Existing models fall under certain prominent schools of thought, each successfully explaining a subset of basal ganglia functions that are amenable to that specific approach, while ignoring a host of other functions. The aim of this book is to describe a class of the basal ganglia models that comprehensively accommodates a wide range of the basal ganglia functions within a single modeling framework. This class of models is essentially based on reinforcement learning, a currently dominant paradigm for describing the basal ganglia function. However, the class of computational models described herein deviate significantly from some of the classical approaches like, for example, the Go-NoGo interpretation of the functional pathways of the basal ganglia. This class of models successfully explains a wide variety of motor functions, and some cognitive functions of the basal ganglia, in healthy and pathological conditions like the Parkinson’s disease and other disorders associated with the basal ganglia.

References

  1. Atallah, H. E., Lopez-Paniagua, D., Rudy, J. W., & O’Reilly, R. C. (2007). Separate neural substrates for skill learning and performance in the ventral and dorsal striatum. Nature Neuroscience, 10(1), 126–131.  https://doi.org/10.1038/nn1817.CrossRefGoogle Scholar
  2. Balasubramani, P. P., Chakravarthy, S., Ravindran, B., & Moustafa, A. A. (2014). An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning. Frontiers in Computational Neuroscience, 8, 47.CrossRefGoogle Scholar
  3. Balasubramani, P. P., Chakravarthy, S., Ravindran, B., & Moustafa, A. A. (2015). A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making. Frontiers in Computational Neuroscience, 9, 76.CrossRefGoogle Scholar
  4. Brown, L. L., Feldman, S. M., Smith, D. M., Cavanaugh, J. R., Ackermann, R. F., & Graybiel, A. M. (2002). Differential metabolic activity in the striosome and matrix compartments of the rat striatum during natural behaviors. Journal of Neuroscience, 22(1), 305–314.Google Scholar
  5. Colas, J. T., Pauli, W. M., Larsen, T., Tyszka, J. M., & O’Doherty, J. P. (2017). Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI. PLoS Computational Biology, 13(10), e1005810.  https://doi.org/10.1371/journal.pcbi.1005810.CrossRefGoogle Scholar
  6. Gangadhar, G., Joseph, D., & Chakravarthy, V. S. (2008). Understanding Parkinsonian handwriting through a computational model of basal ganglia. Neural Computation, 20(10), 2491–2525.CrossRefGoogle Scholar
  7. Gangadhar, G., Joseph, D., Srinivasan, A. V., Subramanian, D., Shivakeshavan, R. G., Shobana, N., & Chakravarthy, V. S. (2009). A computational model of Parkinsonian handwriting that highlights the role of the indirect pathway in the basal ganglia. Human Movement Science, 28(5), 602–618.Google Scholar
  8. Gupta, A., Balasubramani, P. P., & Chakravarthy, V. S. (2013). Computational model of precision grip in Parkinson’s disease: A utility based approach. Frontiers in Computational Neuroscience, 7.  https://doi.org/10.3389/fncom.2013.00172.
  9. Helie, S., Chakravarthy, S., & Moustafa, A. A. (2013). Exploring the cognitive and motor functions of the basal ganglia: an integrative review of computational cognitive neuroscience models. Frontiers in Computational Neuroscience, 7, 174.  https://doi.org/10.3389/fncom.2013.00174.CrossRefGoogle Scholar
  10. Houk, J. C., Davis, J. L., & Beiser, D. G. (1995). Models of information processing in the basal ganglia. Cambridge: The MIT press.Google Scholar
  11. Krishnan, R., Ratnadurai, S., Subramanian, D., Chakravarthy, V. S., & Rengaswamy, M. (2011). Modeling the role of basal ganglia in saccade generation: is the indirect pathway the explorer? Neural Networks, 24(8), 801–813.Google Scholar
  12. Li, J., McClure, S. M., King-Casas, B., & Montague, P. R. (2006). Policy adjustment in a dynamic economic game. PLoS ONE, 1, e103.  https://doi.org/10.1371/journal.pone.0000103.CrossRefGoogle Scholar
  13. Magdoom, K., Subramanian, D., Chakravarthy, V. S., Ravindran, B., Amari, S.-I., & Meenakshisundaram, N. (2011). Modeling basal ganglia for understanding parkinsonian reaching movements. Neural Computation, 23(2), 477–516.CrossRefMATHGoogle Scholar
  14. Mandali, A., Rengaswamy, M., Chakravarthy, S., & Moustafa, A. A. (2015). A spiking Basal Ganglia model of synchrony, exploration and decision making. Frontiers in Neuroscience, 9, 191.CrossRefGoogle Scholar
  15. Moustafa, A. A. & Maida, A. S. (2007). Using TD learning to simulate working memory performance in a model of the prefrontal cortex and basal ganglia. Cognitive Systems Research, 8, 262–281.CrossRefGoogle Scholar
  16. Moustafa, A. A., Chakravarthy, S., Phillips, J. R., Gupta, A., Keri, S., Polner, B., … Jahanshahi, M. (2016). Motor symptoms in Parkinson’s disease: A unified framework. Neuroscience & Biobehavioral Reviews, 68, 727–740.Google Scholar
  17. Moustafa, A. A., Cohen, M. X., Sherman, S. J., & Frank, M. J. (2008). A role for dopamine in temporal decision making and reward maximization in parkinsonism. Journal of Neuroscience, 28(47), 12294–12304.  https://doi.org/10.1523/JNEUROSCI.3116-08.2008.CrossRefGoogle Scholar
  18. Muralidharan, V., Balasubramani, P. P., Chakravarthy, V. S., Gilat, M., Lewis, S. J., & Moustafa, A. A. (2017). A Neurocomputational Model of the Effect of Cognitive Load on Freezing of Gait in Parkinson’s Disease. Frontiers in Human Neuroscience, 10, 649.  https://doi.org/10.3389/fnhum.2016.00649.CrossRefGoogle Scholar
  19. Muralidharan, V., Balasubramani, P. P., Chakravarthy, V. S., Lewis, S. J., & Moustafa, A. A. (2014). A computational model of altered gait patterns in parkinson’s disease patients negotiating narrow doorways. Frontiers in Human Neuroscience, 7, 190.  https://doi.org/10.3389/fncom.2013.00190.Google Scholar
  20. O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304(5669), 452–454.CrossRefGoogle Scholar
  21. Piray, P., Zeighami, Y., Bahrami, F., Eissa, A. M., Hewedi, D. H., & Moustafa, A. A. (2014). Impulse control disorders in Parkinson’s disease are associated with dysfunction in stimulus valuation but not action valuation. The Journal of Neuroscience, 34(23), 7814–7824.CrossRefGoogle Scholar
  22. Sridharan, D., Prashanth, P., & Chakravarthy, V. (2006). The role of the basal ganglia in exploration in a neural model based on reinforcement learning. International Journal of Neural Systems, 16(02), 111–124.CrossRefGoogle Scholar
  23. Sukumar, D., Rengaswamy, M., & Chakravarthy, V. S. (2012). Modeling the contributions of Basal ganglia and Hippocampus to spatial navigation using reinforcement learning. PLoS ONE, 7(10), e47467.CrossRefGoogle Scholar
  24. Wilson, C. J. (2004). Basal ganglia. In G. M. Shepherd (Ed.), The synaptic organization of the brain (pp. 361–413). New York: Oxford University Press.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • V. Srinivasa Chakravarthy
    • 1
  • Ahmed A. Moustafa
    • 2
    • 3
  1. 1.Department of BiotechnologyIndian Institute of Technology, MadrasChennaiIndia
  2. 2.School of Social Sciences and Psychology & Marcs Institute for Brain and BehaviourWestern Sydney UniversitySydneyAustralia
  3. 3.Marcs Institute for Brain and BehaviourWestern Sydney UniversitySydneyAustralia

Personalised recommendations