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.
- 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
- 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
- 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
- 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
- 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.
- 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
- Houk, J. C., Davis, J. L., & Beiser, D. G. (1995). Models of information processing in the basal ganglia. Cambridge: The MIT press.Google Scholar
- 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
- 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
- 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
- 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