Abstract
In this chapter, we built a hybrid model using the combination of biophysical and Izhikevich neurons and validated our earlier hypothesis about Go-Explore-NoGo (GEN) mechanism in BG. The hybrid model consists of Hodgkin–Huxley type model for STN, GPe, and GPi and spiking model for striatum. To capture the effect of dopamine (DA) on the BG nuclei dynamics, the synaptic weights between STN–GPe and the T-type cs in STN known to induce bursting behavior were modulated by DA. We compared the results from hybrid model with spiking Izhikevich model and rate-coded model for binary action selection task. The results from the hybrid model further reinforced the theory of GEN showing exploration levels are dependent on the level of DA. The results from n-arm bandit task also show that by decreasing the striatum (D1) to GPi weight in the spiking model, we can increase the exploration level in the system reflected as the decreased average reward obtained by the model. The n-arm bandit results were compared with the results from rate-coded and lumped softmax model.
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Mandali, A., Akila Parvathy Dharshini, S., Srinivasa Chakravarthy, V. (2018). Go-Explore-NoGo (GEN) Paradigm in Decision Making—A Multimodel Approach. In: Computational Neuroscience Models of the Basal Ganglia. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-8494-2_9
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DOI: https://doi.org/10.1007/978-981-10-8494-2_9
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