33 The mammalian visual system, from retina to neocortex, has been extensively studied at both 34 anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, 35 but characterization of cellular-level functional interactions across multiple levels of this 36 hierarchy is lacking, partially due to the challenge of simultaneously recording activity across 37 numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) 38 that surveys spiking from units in six cortical and two thalamic regions responding to a battery of 39 visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional 40 connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. 41Classical functional measures of hierarchy, including visual response latency, receptive field 42 size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated 43 with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral 44 relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide 45 a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual 46 system. 47
SummaryTo understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.
Multiphoton calcium imaging is commonly used to monitor the spiking of large populations of neurons. Recovering action potentials from fluorescence necessitates calibration experiments, often with simultaneous imaging and cell-attached recording.Here we performed calibration for imaging conditions matching those of the Allen Brain Observatory. We developed a novel crowd-sourced, algorithmic approach to quality control. Our final data set was 50 recordings from 35 neurons in 3 mouse lines. Our calibration indicated that 3 or more spikes were required to produce consistent changes in fluorescence. Moreover, neither a simple linear model nor a more complex biophysical model accurately predicted fluorescence for small numbers of spikes (1-3). We observed increases in fluorescence corresponding to prolonged depolarizations, particularly in Emx1-IRES-Cre mouse line crosses. Our results indicate that deriving spike times from fluorescence measurements may be an intractable problem in some mouse lines.Introduction 1 Systems neuroscience demands a steady increase in the number of simultaneously 2 recorded neurons. Over the last five decades, the number of recorded neurons has 3 doubled approximately every 7 years, mimicking Moore's Law [1]. Electrophysiology has 4 been the de facto gold standard for measuring neural activity for more than 80 years, 5 starting with pioneering work on the squid giant axon by Young, Curtis, Cole, Hodgkin, 6 and Huxley [2][3][4]. However, when the express goal is to push the envelope on the 7 number of simultaneously recorded neurons in vivo, intracellular electrophysiology is 8 impractical, and extracellular electrophysiology has other limitations, e.g. that the 9 precise origin of the signal (positional, genetic, morphological, and physiological details 10 of the generating neuron) are typically unknown [5]. Simultaneous recordings from large 11 numbers of genetically-identified neurons or experiments that necessitate recording from 12 the same neurons repeatedly over many days are difficult to achieve with 13 electrophysiology and are more tractable with imaging techniques. 14 One commonly used imaging technique is 2-photon calcium imaging. Anticipated15 from first principles by Maria Göppert-Mayer [6, 7], and experimentally pioneered by 16 Denk, Strickler, and Webb [8], 2-photon imaging emerged as a complementary technique 17 for the acquisition of neural activity with single-cell resolution from hundreds of neurons 18 in the living brain. Modern technology has enabled the development of genetically 19 1/32 encoded calcium indicators (GECIs), which can be introduced via viral transfection or 20transgenic strategy, and are expressed in a promoter-specific manner. The development 21 of GCaMP6 in particular, with its promise of elusive single spike sensitivity [9], heralded 22 a new era of massive optophysiological surveys [10], recording large numbers of neurons 23 from specific, genetically distinct populations. 24Unfortunately, the fluorescence of a calcium indicator is only indirectly li...
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