Abstract
Reward prediction is essential for learning behavior and decision-making process in the brain. It is well known that neurons in both prefrontal cortex (PFC) and striatum are involved in encoding reward values. The difference in reward coding function between these two brain regions remains unclear. In this work, local field potentials (LFPs) were recorded in the lateral PFC and striatum of a male monkey while performing a reward prediction task. A pattern classification method was used to characterize the function of PFC and striatum for encoding reward values. We used two different feature extraction methods to extract input features to two different classifiers, including random forest (RF) and support vector machine (SVM). We optimized the SVM using the particle swarm optimization (PSO) algorithm. The results suggested that even in a model-based process, the neurons in striatum are capable of encoding more reward information than those in PFC.
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References
Garrison, J., Erdeniz, B., Done, J.: Prediction error in reinforcement learning: a meta-analysis of neuroimaging studies. Neurosci. Biobehav. Rev. 37, 1297–1310 (2013)
Kahnt, T., Heinzle, J., Park, S.Q., Haynes, J.D.: Decoding the formation of reward predictions across learning. J. Neurosci. 31, 14624–14630 (2011)
Shingo, T., Pan, X., Mineki, O., Jessica, E.T., Masamichi, S.: Dissociable functions of reward inference in the lateral prefrontal cortex and the striatum. Front. Psychol. 6, 995 (2015)
Pan, X., Fan, H., Sawa, K., Tsuda, I., Tsukada, M., Sakagami, M.: Reward inference by primate prefrontal and striatal neurons. J. Neurosci. 34, 1380–1396 (2014)
Pan, X., Sawa, K., Tsuda, I., Tsukada, M., Sakagami, M.: Reward prediction based on stimulus categorization in primate lateral prefrontal cortex. Nat. Neurosci. 11, 703–712 (2008)
Kumar, Y., Dewal, M.L., Anand, R.S.: Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing. 133, 271–279 (2014)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. U.S.A. 88, 2297–2301 (1991)
Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36, 2027–2036 (2009)
Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification: Int. J. Neural Syst. 25, 1550023 (2015)
Abe, S.: Fuzzy support vector machines for multilabel classification. Pattern Recognit. 48, 2110–2117 (2015)
Chang, B.-M., Tsai, H.-H., Yen, C.-Y.: SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains. Eng. Appl. Artif. Intell. 52, 96–107 (2016)
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Wen, Z., Zhang, J., Pan, X. (2018). A Comparison of Reward Values Encoding Function Between the Prefrontal Cortex and Striatum in Monkey. In: Delgado-García, J., Pan, X., Sánchez-Campusano, R., Wang, R. (eds) Advances in Cognitive Neurodynamics (VI). Advances in Cognitive Neurodynamics. Springer, Singapore. https://doi.org/10.1007/978-981-10-8854-4_4
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DOI: https://doi.org/10.1007/978-981-10-8854-4_4
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