Advanced Topics in Systems Neuroscience
2019 Spring Semester
課程名稱(英)：Advanced Topics in Systems Neuroscience
The goal of this class is to gain proficiency in reading modern systems neuroscience literature, with a special focus on behavior neurophysiology. The class will dissect recent papers that study cognitive functions (attention, reward, executive functions) using single unit recording in animal models. The class will emphasize skills for data interpretation, and discuss analytical methods commonly used in neurophysiology studies. Students are required to read the assigned paper before each class, and encouraged to participate in discussion. Students will need to present one paper in the class, and write a final paper.
This class is open to both graduate and undergraduate students. Class size limited to 12. Enrolling students must have taken a systems neuroscience class before, and must seek permission from the instructor at the start of the semester. Students are evaluated based on participation in class discussions, one oral presentation and one final paper.
Dopamine and reward prediction error2019/3/19M. Matsumoto, O. Hikosaka, Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature. 459, 837–841 (2009).
2019/3/26 J. Y. Cohen, S. Haesler, L. Vong, B. B. Lowell, N. Uchida, Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature. 482, 85–88 (2012).
2019/4/2 N. Eshel et al., Arithmetic and local circuitry underlying dopamine prediction errors. Nature. 525, 243–246 (2015).
2019/4/9 H. F. Kim, A. Ghazizadeh, O. Hikosaka, Dopamine Neurons Encoding Long-Term Memory of Object Value for Habitual Behavior. Cell. 163, 1165–1175 (2015).
2019/4/16 W. R. Stauffer, A. Lak, W. Schultz, Dopamine reward prediction error responses reflect marginal utility. Curr. Biol. 24, 2491–2500 (2014).
Decision making2019/4/23K. Nomoto, W. Schultz, T. Watanabe, M. Sakagami, Temporally extended dopamine responses to perceptually demanding reward-predictive stimuli. J. Neurosci. 30, 10692–10702 (2010).
2019/4/30de Lafuente, R. Romo, Neuronal correlates of subjective sensory experience. Nat. Neurosci. 8, 1698–1703 (2005).
Attention 2019/5/7R. D. Wimmer et al., Thalamic control of sensory selection in divided attention. Nature. 526, 705–709 (2015).
2019/5/14L. I. Schmitt et al., Thalamic amplification of cortical connectivity sustains attentional control. Nature. 545, 219–223 (2017).
2019/5/21R. V. Rikhye, A. Gilra, M. M. Halassa, Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat. Neurosci. 21, 1753–1763 (2018).
Decision making2019/5/28V. Mante, D. Sussillo, K. V. Shenoy, W. T. Newsome, Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature. 503, 78–84 (2013).
2019/6/4D. G. R. Tervo et al., Behavioral variability through stochastic choice and its gating by anterior cingulate cortex. Cell. 159, 21–32 (2014).
2019/6/11A. Kepecs, N. Uchida, H. A. Zariwala, Z. F. Mainen, Neural correlates, computation and behavioural impact of decision confidence. Nature. 455, 227–231 (2008).