Benchmarking spike rate inference in population calcium imaging

A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience

Subcortical Source and Modulation of the Narrowband Gamma Oscillation in Mouse Visual Cortex

Primary visual cortex exhibits two types of gamma rhythm: broadband activity in the 30-90 Hz range and a narrowband oscillation seen in mice at frequencies close to 60 Hz. We investigated the sources of the narrowband gamma oscillation, the factors modulating its strength, and its relationship to broadband gamma activity. Narrowband and broadband gamma power were uncorrelated. Increasing visual contrast had opposite effects on the two rhythms: it increased broadband activity, but suppressed the narrowband oscillation. The narrowband oscillation was strongest in layer 4 and was mediated primarily by excitatory currents entrained by the synchronous, rhythmic firing of neurons in the lateral geniculate nucleus (LGN). The power and peak frequency of the narrowband gamma oscillation increased with light intensity. Silencing the cortex optogenetically did not abolish the narrowband oscillation in either LGN firing or cortical excitatory currents, suggesting that this oscillation reflects unidirectional flow of signals from thalamus to cortex

Functional characterization of the retinogeniculate pathway

More than 30 functional types of retinal ganglion cells (RGCs) compute in parallel distinct features of the visual world and send this information to the brain. Little is known, however, about which RGC types project to the dorsolateral geniculate nucleus (dLGN) of the thalamus, and how the different RGC channels recombine there. Interest in these questions has been fuelled by recent estimates of retinogeniculate convergence obtained by anatomical work, which far exceeded those obtained in electrophysiological recordings. To get insights into the nature of retinal input to the dLGN, we conditionally expressed the genetically encoded Ca2+ indicator GCaMP6f in dLGN-projecting (dLGN-p) RGCs, followed by in vitro retinal two-photon Ca2+ imaging of light-evoked responses. Visual stimuli matched those in a previously published survey of mouse functional RGC types (Baden et al., 2016). We then assigned each dLGN-p RGC to the best-matching RGC type with the best-matching response properties. We found that most functional RGC types seem to innervate dLGN, with certain types, such as ON- and OFF alpha cells or OFF supressed cells, showing clear overrepresentations. In a separate set of experiments, we characterized the responses of dLGN neurons to the same visual stimuli using in-vivo extracellular multi-electrode recordings in the dLGN of awake, head-fixed mice. We quantitatively assessed the degree of diversity in the dLGN responses by using sparse non-negative matrix factorization (NNMF), which decomposed the dLGN population response into a rich and highly diverse set of components. Finally, we linked the functionally characterized population of dLGN-projecting RGCs and geniculate neurons, via computational modelling to provide a quantitative account of the transformations in visual representation between RGCs and dLGN neurons. We found that responses of dLGN neurons could be best predicted as a sparse linear combination of responses from 3-7 different RGC types. In conclusion, this study provides fundamental insights into how the representation of visual information changes along the first stages of the retino-geniculo-cortical pathway, suggesting that the precortical basis of vision displays an unexpectedly rich functional diversity of retino- geniculate projections and thalamic features that can be modelled by a sparse feed-forward model

The functional diversity of retinal ganglion cells in the mouse

In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells. Each type encodes distinct visual features in parallel for transmission to the brain. How many such ‘output channels’ exist and what each encodes are areas of intense debate. In the mouse, anatomical estimates range from 15 to 20 channels, and only a handful are functionally understood. By combining two-photon calcium imaging to obtain dense retinal recordings and unsupervised clustering of the resulting sample of more than 11,000 cells, here we show that the mouse retina harbours substantially more than 30 functional output channels. These include all known and several new ganglion cell types, as verified by genetic and anatomical criteria. Therefore, information channels from the mouse eye to the mouse brain are considerably more diverse than shown thus far by anatomical studies, suggesting an encoding strategy resembling that used in state-of-the-art artificial vision systems

Data from: The functional diversity of retinal ganglion cells in the mouse

In the vertebrate visual system, all output of the retina is carried by retinal ganglion cells. Each type encodes distinct visual features in parallel for transmission to the brain. How many such ‘output channels’ exist and what each encodes are areas of intense debate. In the mouse, anatomical estimates range from 15 to 20 channels, and only a handful are functionally understood. By combining two-photon calcium imaging to obtain dense retinal recordings and unsupervised clustering of the resulting sample of more than 11,000 cells, here we show that the mouse retina harbours substantially more than 30 functional output channels. These include all known and several new ganglion cell types, as verified by genetic and anatomical criteria. Therefore, information channels from the mouse eye to the mouse brain are considerably more diverse than shown thus far by anatomical studies, suggesting an encoding strategy resembling that used in state-of-the-art artificial vision systems