| scholarly article | Q13442814 |
| P356 | DOI | 10.1038/S41593-019-0550-9 |
| P698 | PubMed publication ID | 31844315 |
| P50 | author | Christof Koch | Q1084303 |
| Nicholas Cain | Q41045383 | ||
| Eric Lee | Q41713026 | ||
| Hongkui Zeng | Q42648435 | ||
| David Feng | Q56424180 | ||
| Jack Waters | Q61163692 | ||
| Jun Zhuang | Q61163694 | ||
| Jérôme Lecoq | Q64880304 | ||
| R. Larsen | Q67483960 | ||
| Amy Bernard | Q67483995 | ||
| Saskia de Vries | Q88882723 | ||
| Stefan Mihalas | Q89891250 | ||
| Shawn R Olsen | Q89891253 | ||
| Eric Shea-Brown | Q91820050 | ||
| Michael A Buice | Q92026077 | ||
| Ulf Knoblich | Q92026082 | ||
| P2093 | author name string | Colin Farrell | |
| Robert Howard | |||
| Lu Li | |||
| Andrew Cho | |||
| David Sullivan | |||
| Peter A Groblewski | |||
| R Clay Reid | |||
| Chris Lau | |||
| Fuhui Long | |||
| Carol Thompson | |||
| Daniela M Witten | |||
| John W Phillips | |||
| Felix Lee | |||
| Chris Barber | |||
| Lydia Ng | |||
| Gabriel K Ocker | |||
| John Galbraith | |||
| Casey White | |||
| Fiona Griffin | |||
| Kate Roll | |||
| Sissy Cross | |||
| Jianghong Shi | |||
| Miranda Robertson | |||
| Daniel Millman | |||
| Nathalie Gaudreault | |||
| Chinh Dang | |||
| Leonard Kuan | |||
| Tim Dolbeare | |||
| Wayne Wakeman | |||
| Tom Keenan | |||
| Michael Oliver | |||
| Lawrence Huang | |||
| Cliff Slaughterbeck | |||
| Jennifer Luviano | |||
| Perry Hargrave | |||
| Peter Ledochowitsch | |||
| Jed Perkins | |||
| Marina Garrett | |||
| Terri L Gilbert | |||
| Derric Williams | |||
| Linzy Casal | |||
| Arielle Leon | |||
| Ali Williford | |||
| Brandon Blanchard | |||
| Josh D Larkin | |||
| Kyla Mace | |||
| Melise Edwards | |||
| Nathan Berbesque | |||
| Nathan Sjoquist | |||
| Nicholas Bowles | |||
| Nika Keller | |||
| Ryan Valenza | |||
| Sam Seid | |||
| Sean Jewell | |||
| Shiella D Caldejon | |||
| Thuyanh Nguyen | |||
| P2860 | cites work | Matplotlib: A 2D Graphics Environment | Q17278583 |
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| How does the brain solve visual object recognition? | Q35836346 | ||
| Network analysis of corticocortical connections reveals ventral and dorsal processing streams in mouse visual cortex | Q35893697 | ||
| Neural correlations, population coding and computation | Q36500184 | ||
| Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs | Q36515304 | ||
| Variability and Correlations in Primary Visual Cortical Neurons Driven by Fixational Eye Movements. | Q36985347 | ||
| Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons | Q37060127 | ||
| Stimulus ensemble and cortical layer determine V1 spatial receptive fields. | Q37321130 | ||
| Fezf2 expression identifies a multipotent progenitor for neocortical projection neurons, astrocytes, and oligodendrocytes | Q37379315 | ||
| GENSAT BAC cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits | Q37412260 | ||
| Functional specialization of mouse higher visual cortical areas. | Q37423115 | ||
| Correlations and Neuronal Population Information | Q37472399 | ||
| Complex Visual Motion Representation in Mouse Area V1. | Q37558846 | ||
| A neural circuit for spatial summation in visual cortex | Q39709858 | ||
| Functional specialization of seven mouse visual cortical areas. | Q40072576 | ||
| Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation | Q41811463 | ||
| Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1. | Q41973182 | ||
| Fate-restricted neural progenitors in the mammalian cerebral cortex. | Q42118528 | ||
| Cortical excitatory neurons and glia, but not GABAergic neurons, are produced in the Emx1-expressing lineage. | Q42524386 | ||
| Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data | Q44905213 | ||
| Fully integrated silicon probes for high-density recording of neural activity | Q45072447 | ||
| Cellular and synaptic architecture of multisensory integration in the mouse neocortex | Q45077715 | ||
| Natural stimulus statistics alter the receptive field structure of v1 neurons. | Q46139295 | ||
| New paradigm for optical imaging: temporally encoded maps of intrinsic signal | Q46547489 | ||
| Leptin directly activates SF1 neurons in the VMH, and this action by leptin is required for normal body-weight homeostasis | Q47352671 | ||
| Sensorimotor mismatch signals in primary visual cortex of the behaving mouse | Q47646781 | ||
| Natural versus synthetic stimuli for estimating receptive field models: a comparison of predictive robustness. | Q48677566 | ||
| Adaptation of the simple or complex nature of V1 receptive fields to visual statistics. | Q49038266 | ||
| Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli. | Q50193416 | ||
| A biologically inspired algorithm for the recovery of shading and reflectance images. | Q50485872 | ||
| The logic of single-cell projections from visual cortex. | Q51737387 | ||
| Vision and Locomotion Shape the Interactions between Neuron Types in Mouse Visual Cortex. | Q55137223 | ||
| Independent component filters of natural images compared with simple cells in primary visual cortex. | Q55312647 | ||
| Flow stimuli reveal ecologically appropriate responses in mouse visual cortex | Q57460040 | ||
| SCALPEL: EXTRACTING NEURONS FROM CALCIUM IMAGING DATA | Q59808087 | ||
| EXACT SPIKE TRAIN INFERENCE VIA ℓ0 OPTIMIZATION. | Q64915342 | ||
| Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex | Q72261555 | ||
| The unsolved mystery of vision | Q80766721 | ||
| Targeting Cre recombinase to specific neuron populations with bacterial artificial chromosome constructs | Q81250914 | ||
| Spontaneous behaviors drive multidimensional, brainwide activity | Q89998019 | ||
| A Suite of Transgenic Driver and Reporter Mouse Lines with Enhanced Brain-Cell-Type Targeting and Functionality | Q90210850 | ||
| Single-trial neural dynamics are dominated by richly varied movements | Q90263054 | ||
| Fast nonconvex deconvolution of calcium imaging data | Q91501594 | ||
| Data Structures for Statistical Computing in Python | Q104437798 | ||
| P433 | issue | 1 | |
| P304 | page(s) | 138-151 | |
| P577 | publication date | 2019-12-16 | |
| P1433 | published in | Nature Neuroscience | Q1535359 |
| P1476 | title | A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex | |
| P478 | volume | 23 |
| Q108127172 | Accelerating with FlyBrainLab the discovery of the functional logic of the brain in the connectomic and synaptomic era |
| Q97520065 | Characterization of Learning, Motivation, and Visual Perception in Five Transgenic Mouse Lines Expressing GCaMP in Distinct Cell Populations |
| Q89891256 | Experience shapes activity dynamics and stimulus coding of VIP inhibitory cells |
| Q92356836 | Interpreting in vivo calcium signals from neuronal cell bodies, axons, and dendrites: a review |
| Q108127116 | Standardized and reproducible measurement of decision-making in mice |
| Q92452431 | Superficial Bound of the Depth Limit of Two-Photon Imaging in Mouse Brain |
| Q89857216 | The SONATA data format for efficient description of large-scale network models |
| Q98386185 | Universality and individuality in neural dynamics across large populations of recurrent networks |
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