Abstract
To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision making and responding according to changes in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits, and the encoding and decoding mechanisms from stimuli to responses, are important goals in neuroscience. A biologically plausible decision circuit consisting of computational neuron and synapse models and its learning mechanism are designed in this paper. The learning mechanism is based on two parts: first, effect of the punishment from the environment on the temporal correlations of neuron firings; second, spike timing dependent plasticity (STDP) of synapse. The decision circuit was used successfully to simulate the behavior of Drosophila exhibited in real experiments. In this paper, we place focus on the connections and interactions among excitatory and inhibitory neurons and try to give an explanation at a micro level (i.e. neurons and neural circuit) of how the observable decision making behavior is acquired and achieved.
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Acknowledgments
This work was supported by NSFC project (Project No.61375122), and in part by Shanghai Science and Technology Development Funds (13dz2260200, 13511504300). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
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Wei, H., Bu, Y., Dai, D. (2016). A Possible Neural Circuit for Decision Making and Its Learning Process. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_18
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DOI: https://doi.org/10.1007/978-3-319-49685-6_18
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