Prerequisites: NBIO 140

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1 Professor Gina Turrigiano NBIO 145 Systems Neuroscience Spring 2019, Tues-Friday 3:30-4:50 Volen 119 Instructor: Gina Turrigiano; x62684; Office Hours: by appointment Prerequisites: NBIO 140 Course overview: This course will take a systems-level perspective to explore how the brain encodes sensory information, and supports behavior and cognition. Students will read and discuss seminal and current findings to understand how the brain represents and perceives the external environment, adapts and learns, executes control, and uses internal representations to make decisions and drive behavior. The focus will in particular be on underlying circuits, neural representations and neurophysiological mechanisms. Emphasis will be on bridging across these levels, and on critical analysis and synthesis. Attendance is mandatory. Course material: Papers for discussion are listed on the syllabus and are available through the Brandeis ejournals; students must access reading well ahead of time. Links to some classic papers that are more difficult to access will be listed on the syllabus. Additional sources from the primary literature can be found through PubMed or other database searches. Changes to reading assignments or timing will be announced in class. Course requirements: The class format will focus on discussion of research papers. Reading must be completed prior to class. We will discuss the paper in detail including each figure, the methods used to generate it, how the data were analyzed and represented, the take-home message of the figure, and any weaknesses. The instructor will call on students at random to discuss figures, as well as to give brief summaries of the paper and the overall take-home message. It is highly recommended that students generate summaries/notes on each paper prior to class to aid in class discussions. Two term papers (mid-term and final), each of which will be revised after peer review. Format of the papers will be discussed in class. Topics will be assigned by the instructor. Grading will be based on completeness, accuracy, clarity of the argument presented, and critical thinking demonstrated. Success in this 4 credit hour course is based on the expectation that students will spend 3 hours per week in class and a minimum of 9 hours of study time per week in preparation for class. Grading, assessment, and graded assignments: 40% Paper discussions. Command of reading material, preparation for class, participation in discussion 30% Mid-term paper (20% for paper, 10% peer-review): 1 st draft 3/7; peer review 3/14: final draft 3/21 30% Final paper (20% for paper, 10% peer-review): 1 st draft 4/11; peer review 4/18; final draft 5/3 Learning Outcomes: Students will gain familiarity with many important concepts in circuit/systems neuroscience, and will learn how to read and critically evaluate papers from the primary scientific literature. Students will learn how to research and write a scientific paper that involves summarizing, evaluating, and synthesizing scientific information from many primary sources. If you are a student with a documented disability on record at Brandeis University and wish to have a reasonable accommodation made for you in this class, please see me immediately.

2 TENTATIVE TOPICS AND READING LIST - subject to change at instructor s discretion Date Topic/Reading assignments Tues, Jan 15 Class 1 Introduction: Elements of neural circuits and methods for studying them; representations, maps, and codes; quick review of retinal circuitry Topic I: Sensory Systems and Representations Thurs Jan 17 Class 2 Retinal receptive fields and feature detectors 1. Kuffler SW (1953) Discharge patterns and functional organization of mammalian retina. J. Neurophysiol. 16: Zhang Y, Kim IJ, Sanes JR, Meister M. (2012) The most numerous ganglion cell type of the mouse retina is a selective feature detector. Proc Natl Acad Sci U S A. ;109:E2391- Background reading: Kevan Martin (1994): A brief history of the feature detector. Cerebral Cortex 4:1-7; Maturana HR, Lettvin JY, McCulloch WS and Pitts (1960) Anatomy and physiology of vision in the Frog (Rana pipiens). J. Gen. Physiol.43: ; Dhande and Huberman (2014) Retinal ganglion cell maps in the brain: implications for visual processing. Curr Opin Neurobiol 24: Thurs Jan 24 Class 3 Mechanisms of Retinal Direction Selectivity 1. Barlow HB and Levick, WR (1965) The mechanism of directionally selective units in rabbit's retina. J. Physiol. 178: Euler T, Detwiler PB and Denk W (2002) Directionally selective calcium signals in dendrites of starburst amacrine cells. Nature 418: Background reading: Demb (2007) Cellular mechanisms for direction selectivity in the retina. Neuron 55: ; Elstrott and Feller (2009) Vision and the establishment of direction selectivity; a tale of two circuits. Curr Opin Neurobiol 19: Tues Jan 29th Class 4 Visual Cortical Receptive Fields and Functional Architecture 1. Hubel, DH and TN Wiesel (1962) Receptive fields, binocular interaction and functional architecture in the cat s visual cortex. J. Physiol. 160: Ohki, Chung, Kara, Hubener, Bonhoeffer, and Reid (2006): Highly ordered arrangement of single neurons in orientation pinwheels. Nature 442: Background reading: Ohki and Reid (2007) Specificity and randomness in the visual cortex. Curr Opin Neurobiology 17: Thurs Jan 31 Class 5 Spill-over Tues Feb 5 Class 6 Cortical Microcircuitry and Mechanisms of Tuning 1: 1. Finish: Hubel, DH and TN Wiesel (1962) Receptive fields, binocular interaction and functional architecture in the cat s visual cortex. J. Physiol. 160:

3 2. Reid and Alonso (1995) Specificity of monosynaptic connections from thalamus to visual cortex. Nature 378: Thurs Feb 7 Class 7 Cortical Microcircuitry and Mechanisms of Tuning 2: 1. Somers, Nelson, and Sur (1995) An emergent model of orientation selectivity in cat visual cortical simple cells. J Neurosci. 15: Lien and Scanziani (2013) Tuned thalamic excitation is amplified by visual cortical circuits. Nature Neuroscience 16: Tues Feb 12 Class 8 Auditory Processing: detection of interaural time differences 1. Carr and Konishi (1990) A circuit for detection of interaural time differences in the brain stem of the barn owl. J Neurosci 10: Carr and Konishi (1988) Axonal delay lines for time measurements in the owl s brainstem. PNAS 85: Background reading: what about mammals? McAlpine and Grothe (2003) Sound localization and delay lines do mammals fit the model? Trends in Neuroscience 26: Topic II: The Nature of the Neural Code: Population, Rate, and Timing Codes Thurs Feb 14 Class 9 Population codes 1. Georgopoulos et al. (1986) Neuronal population coding of movement direction. Science 233: Lewis and Kristan (1998) A neuronal network for computing population vectors in the leech. Nature 391:76-79 Background reading: Kristan and Shaw (1997) Population coding and behavioral choice. Current Opin. Neurobiol. 7: Feb Winter recess Tues Feb 26 Class 10 Rate codes and Temporal Codes 1. O Keefe (1976) Place Units in the hippocampus of the feely moving rat. Exper. Neurology 51: Huxter, Burgess, and O Keefe (2003) Independent rate and temporal coding in hippocampal pyramidal cells. Nature 425: Background reading: Panzeri et al. (2017) Cracking the neural code for sensory perception by combining statistics, intervention and behavior. Neuron 93: Midterm paper assigned Thurs Feb 28 Class 11 Temporal codes continued: Inhibition and Temporal Fidelity 1. Panzeri, Petersen, Schultz, Lebedev, and Diamond (2001) The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron 29: Luna et al. (2005) Neural codes for perceptual discrimination in primary somatosensory cortex. Nat Neurosci 8:

4 Tues March 5 Class 12 Higher-order representations: face recognition 1. Quiroga, Reddy, Kreiman, Kich, and Fried (2005) Invariant visual representation by single neurons in the human brain. Nature 435: Chang and Tsao (2017) The code for facial identity in the primate brain. Cell 169:1013:1028 Thurs March 7 Class 13 Spill over, snow day 2 Background reading: Kornblith and Tsao (2017) How thoughts arise from sights: inferotemporal and prefronal contributions to vision. Current Opin. Neurobiol. 46: Midterm paper due (1 st draft) by midnight (ie one minute after 11:59 pm). Topic III: Experience Dependent Plasticity of Sensory Systems and Representations Tues March 12 Guest Prof: Steve Van Hooser Thurs March 14 Guest Prof: Steve Van Hooser Class 14 Class 15 Visual System Plasticity 1: Phenomenology 1. Wiesel TN, Hubel DH (1965) Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens. J Neurophysiol 1965, 28: Wiesel TN, Hubel DH (1965) Extent of recovery from the effects of visual deprivation in kittens. J Neurophysiol 1965, 28: Background reading: Espinosa and Stryker (2012) Development and plasticity of the primary visual system. Neuron 75(2): Visual System Plasticity 2: Mechanisms 1. Heynen AJ, Yoon BJ, Liu CH, Chung HJ, Huganir RL, Bear MF (2003) Molecular mechanism for loss of visual cortical responsiveness following brief monocular deprivation. Nat Neurosci. 6(8): Miska, Richter, Cary, Gjorjieva, and Turrigiano (2018) Sensory experience inversely regulates feedforward and feedback excitation-inhibition ratio in rodent visual cortex. Elife DOI: /eLife Peer review of midterm paper due by midnight Tues March 19 Class 15 Homeostatic Adjustments in Circuit Function 1. Keck et al (2013) Synaptic scaling and homeostatic plasticity in the mouse visual cortex in vivo. Neuron 80: Hengen et al. (2016) Neuronal firing rate homeostasis is inhibited by sleep and promoted by wake. Cell 165(1): Review for background: Turrigiano, G.G., Nelson, S.B. (2004) Homeostatic Plasticity in Developing Cortical Networks. Nature Reviews Neurosci. 5:97 Tues March 21 Class 17 Birdsong and Vocal Learning

5 1. Marler P, Peters S (1977) Selective vocal learning in a sparrow. Science 198: Leonardo A, Konishi M (1999). Decrystallization of adult birdsong by perturbation of auditory feedback. Nature 399: Brainard, M.S. and Doupe, A. J. (2000) Interruption of a basal ganglia-forebrain circuit prevents plasticity of learned vocalizations. Nature 404: Background reading: Brainard and Doupe (2000) Auditory feedback in learning and maintenance of vocal behavior. Nat. Rev. Neurosci 1:31-40 Revised Midterm paper due by Midnight Tues March 26 Class 18 Map Registration: Aligning Visual and Auditory Maps 1. Knudsen EI (1983) Early Auditory Experience Aligns the Auditory Map of Space in the Optic Tectum of the Barn Owl. Science, 222: Bergen et al. (2005) Hunting increases adaptive auditory map plasticity in barn owls. J Neurosci. 25: Background reading: Knudsen EI. (2002) Instructed learning in the auditory localization pathway of the barn owl. Nature. 417: Thurs March 28 Class 19 Adult Plasticity of cortical representations 1. Wang et al. (1995) Remodeling of hand representation in adult cortex determined by timing of tactile stimulation. Nature 378: He, Ray, Dennis, and Quinlan (2007) Experience-dependent recovery of vision following chronic deprivation amblyopia. Nat. Neurosci. 10: Background reading: Buonomano and Merzenich (1998) Cortical plasticity: from synapses to maps. Annu Rev Neurosci 21: Topic IV: Neural Basis of Simple and Complex Behaviors Tues April 2 Class 20 Spatial Learning and Cognitive Maps: 1. Leutgeb, Leutgeb, Marnes, Moser, McNaughton, and Moser (2005) Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Science 309: Hafting, Fyhn, Molden, Moser, and Moser (2005) Microstructure of a spatial map in the entorhinal cortex. Nature 436: Fyhn, Hafting, Treves, Moser, and Moser (2007) Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446: Background reading: Leutgeb, Leutgeb, Moser, and Moser (2005) Place cell, spatial maps and the population code for memory. Curr Opin Neurobiol. 15: ; McNaughton, Battaglia, Jensen, Moser, and Moser (2006) Path integration and the neural basis of the cognitive map. Nat. Rev. Neurosci. 7:

6 Thurs April 4 Class 21 Sleep and Replay (bird and hipp) 1. Wilson MA, McNaughton BL. (1994) Reactivation of hippocampal ensemble memories during sleep. Science. 265: Dave AS, Margoliash D. (2000) Song replay during sleep and computational rules for sensorimotor vocal learning. Science. 290:812-6 Perspective: Schwartz (2003) Are life episodes replayed during dreaming? Trends Cogn Sci. 7: Tues April 9 Class 22 Hippocampal replay (reactivation) II 1. Dupret et al. (2010) The reorganization and reactivation of hippocampal maps predict spatial memory performance. Nat Neurosci 13: Pfeiffer and Foster (2013) Hippocampal place-cell sequences depict future paths to remembered goals. Nature 497: Background reading: Carr et al. (2011) Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval. Nat Neurosci. 14: Thurs April 11 Class 23 Dopamine: reward and prediction 1. Schultz et al (1997) A neural substrate of prediction and reward. Science 275: Eshel et al. (2015) Arithmetic and local circuitry underlying dopamine prediction errors. Nature 525: Background reading: Dayan and Balleine (2002) Reward, motivation, and reinforcement learning. Neuron 10: Final paper 1 st draft due by Midnight Tues April 16 Class 24 Dopamine: reward and prediction II 1. Steinberg et al. (2013) A causal link between prediction error, dopamine neurons and learning. Nat Neurosci 16: Hamid (2016) Mesolimbic dopamaine signals the value of work. Nat Neurosci 19: Thurs April 18 Class 25 Neural Correlates of Decision-Making 1. Newsome, Britten, Movshon (1989) Neural correlated of a perceptual decision. Nature 341: Salzman, Britten, and Newsome (1990) Cortical microstimulation influences perceptual judgments of motion direction. Nature 346: Yang and Shadlen (2007) Probabilistic reasoning by neurons. Nature 447: Background reading: Gold and Shadlen (2007) The Neural Basis of decision making. Ann Rev. Neurosci. 30: Peer review of Final Paper 1 st draft due by Midnight April Brandeis Spring Break

7 Tues April 30 Class 26 Wrapping up! May 3 Revised Final Paper due by Midnight