Inferring cortical function in the mouse visual system through large-scale systems neuroscience

Proceedings of the National Academy of Sciences of the United States of America

Volumn 113, Issue 27, 2016, Pages 7337-7344

  Hawrylycz, Michael ^a   Anastassiou, Costas ^a   Arkhipov, Anton ^a   Berg, Jim ^a   Buice, Michael ^a   Cain, Nicholas ^a   Gouwens, Nathan W ^a   Gratiy, Sergey ^a   Iyer, Ramakrishnan ^a   Lee, Jung Hoon ^a   Mihalas, Stefan ^a   Mitelut, Catalin ^a   Olsen, Shawn ^a   Reid, R Clay ^a   Teeter, Corinne ^a   De Vries, Saskia ^a   Waters, Jack ^a   Zeng, Hongkui ^a   Koch, Christof ^a  

^a ALLEN INSTITUTE FOR BRAIN SCIENCE   (United States)

Author keywords

Computation; Neocortex; Neural coding; Simulation; Visual system

Indexed keywords

ANIMAL BEHAVIOR; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; BRAIN CELL; BRAIN CORTEX; BRAIN FUNCTION; CELLS BY BODY ANATOMY; CONTROLLED STUDY; DYNAMICS; GENETICS; LABORATORY; MAMMAL; MOUSE; NEOCORTEX; NEUROSCIENCE; NONHUMAN; PHYSIOLOGY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RETINA; VISUAL STIMULATION; VISUAL SYSTEM; ANIMAL; BIOLOGICAL MODEL; C57BL MOUSE; MALE; NERVE CELL; PROCEDURES; SYSTEM ANALYSIS; VISUAL CORTEX;

ANIMALS; MALE; MICE; MICE, INBRED C57BL; MODELS, NEUROLOGICAL; NEURONS; NEUROSCIENCES; SYSTEMS ANALYSIS; VISUAL CORTEX;

EID: 84977271293     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1512901113     Document Type: Article

References (43)

1. Herculano-Houzel S (2009) The human brain in numbers: A linearly scaled-up primate brain. Front Hum Neurosci 3(2009):31.
2. Jeon CJ, Strettoi E, Masland RH (1998) The major cell populations of the mouse retina. J Neurosci 18(21):8936-8946.
3. Tasic B, et al. (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat Neurosci 19(2):335-346.
4. Oh SW, et al. (2014) A mesoscale connectome of the mouse brain. Nature 508(7495): 207-214.
5. Sporns O (2011) Networks of the Brain (MIT Press, Cambridge, MA).
6. Koch C (1999) Biophysics of Computation (Oxford Univ Press, New York).
7. Izhikevich EM (2007) Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (MIT Press, Cambridge, MA).
8. Mihala S, Niebur E (2009) A generalized linear integrate-and-fire neural model produces diverse spiking behaviors. Neural Comput 21(3):704-718.
9. Pozzorini C, et al. (2015) Automated high-throughput characterization of single neurons by means of simplified spiking models. PLOS Comput Biol 11(6):e1004275.
10. Paninski L, Pillow JW, Simoncelli EP (2004) Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Comput 16(12):2533-2561.
11. Druckmann S, et al. (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1(1):7-18.
12. Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9(6):1179-1209.
13. Hay E, Hill S, Schürmann F, Markram H, Segev I (2011) Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLOS Comput Biol 7(7):e1002107.
14. Shai AS, Anastassiou CA, Larkum ME, Koch C (2015) Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: Coincidence detection through bursting. PLOS Comput Biol 11(3):e1004090.
15. Markram H, et al. (2015) Reconstruction and simulation of neocortical microcircuitry. Cell 163(2):456-492.
16. Iyer R, Menon V, Buice M, Koch C, Mihalas S (2013) The influence of synaptic weight distribution on neuronal population dynamics. PLOS Comput Biol 9(10):e1003248.
17. Piscopo DM, El-Danaf RN, Huberman AD, Niell CM (2013) Diverse visual features encoded in mouse lateral geniculate nucleus. J Neurosci 33(11):4642-4656.
18. Chichilnisky EJ (2001) A simple white noise analysis of neuronal light responses. Network 12(2):199-213.
19. Vintch B, Movshon JA, Simoncelli EP (2015) A convolutional subunit model for neuronal responses in Macaque V1. J Neurosci 35(44):14829-14841.
20. Omurtag A, Knight BW, Sirovich L (2000) On the simulation of large populations of neurons. J Comput Neurosci 8(1):51-63.
21. Nykamp DQ, Tranchina D (2001) A population density approach that facilitates largescale modeling of neural networks: Extension to slow inhibitory synapses. Neural Comput 13(3):511-546.
22. de Kamps M (2003) A simple and stable numerical solution for the population density equation. Neural Comput 15(9):2129-2146.
23. de Kamps M (2013) Modeling large populations of spiking neurons with a universal population density solver. BMC Neurosci 14(Suppl 1):90.
24. Potjans TC, Diesmann M (2014) The cell-type specific cortical microcircuit: Relating structure and activity in a full-scale spiking network model. Poster presentation abstract from the Twenty Second Annual Computational Neuroscience Meeting, CNS 2013, Paris, France, July 13-18, 2013. Cereb Cortex 24(3):785-806.
25. Wang XJ (2012) Neural dynamics and circuit mechanisms of decision-making. Curr Opin Neurobiol 22(6):1039-1046.
26. Compte A, Brunel N, Goldman-Rakic PS, Wang XJ (2000) Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb Cortex 10(9):910-923.
27. Rudy B, Fishell G, Lee S, Hjerling-Leffler J (2011) Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev Neurobiol 71(1):45-61.
28. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M (2013) Inhibition of inhibition in visual cortex: The logic of connections between molecularly distinct interneurons. Nat Neurosci 16(8):1068-1076.
29. Zhang S, et al. (2014) Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345(6197):660-665.
30. Adesnik H, Bruns W, Taniguchi H, Huang ZJ, Scanziani M (2012) A neural circuit for spatial summation in visual cortex. Nature 490(7419):226-231.
31. Cardin JA, et al. (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459(7247):663-667.
32. Perin R, Berger TK, Markram H (2011) A synaptic organizing principle for cortical neuronal groups. Proc Natl Acad Sci USA 108(13):5419-5424.
33. Wu L-J, Li X, Chen T, Ren M, Zhuo M (2009) Characterization of intracortical synaptic connections in the mouse anterior cingulate cortex using dual patch clamp recording. Mol Brain 2:32.
34. Lefort S, Tomm C, Floyd Sarria JC, Petersen CCH (2009) The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61(2): 301-316.
35. Schüz A, Palm G (1989) Density of neurons and synapses in the cerebral cortex of the mouse. J Comp Neurol 286(4):442-455.
36. Lien AD, Scanziani M (2013) Tuned thalamic excitation is amplified by visual cortical circuits. Nat Neurosci 16(9):1315-1323.
37. Niell CM, Stryker MP (2008) Highly selective receptive fields in mouse visual cortex. J Neurosci 28(30):7520-7536.
38. Stepanyants A, Martinez LM, Ferecskó AS, Kisvárday ZF (2009) The fractions of short- and long-range connections in the visual cortex. Proc Natl Acad Sci USA 106(9):3555-3560.
39. Holt GR, Koch C (1999) Electrical interactions via the extracellular potential near cell bodies. J Comput Neurosci 6(2):169-184.
40. Reimann MW, et al. (2013) A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79(2): 375-390.
41. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2(11):1019-1025.
42. Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T (2007) Robust object recognition with cortex-like mechanisms. IEEE Trans Pattern Anal Mach Intell 29(3):411-426.
43. Hubel D, Wiesel T (2005) Brain and Visual Perception: The Story of a 25-Year Collaboration (Oxford Univ Press, Oxford).