Biology subjects

Euler, T.

Publications and source records attributed to Euler, T..

4 recordsLinked to original sources

bioRxiv2018

Bayesian Hypothesis Testing And Experimental Design For Two-Photon Imaging Data

Variability, stochastic or otherwise, is a central feature of neural circuits. Yet the means by which variation and uncertainty are derived from noisy observations of neural activity is often unprincipled, with too much weight placed on numerical convenience at the cost of statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a complete statistical pipeline for the inference and analysis of neural activity using Gaussian Process Regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guiding the design of light stimulation in the midst of ongoing two-photon experiments.

neuroscience↗

bioRxiv2018

Mouse dLGN receives input from a diverse population of retinal ganglion cells with limited convergence

In the mouse, the parallel output of more than 30 functional types of retinal ganglion cells (RGCs) serves as the basis for all further visual processing. Little is known about how the representation of visual information changes between the retina and the dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay station between the retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate the dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that the responses of dLGN neurons could be predicted as a linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives input from a diverse population of RGCs with limited functional convergence.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗

bioRxiv2017

Local Signal Processing In Mouse Horizontal Cell Dendrites

The mouse retina contains a single type of horizontal cell, a GABAergic interneuron that samples from all cone photoreceptors within reach and modulates their glutamatergic output via parallel feedback mechanisms. Because horizontal cells form an electrically-coupled network, they have been implicated in global signal processing, such as large scale contrast enhancement. Recently, it has been proposed that horizontal cells can also act locally at the level of individual cone photoreceptors. To test this possibility physiologically, we used two-photon microscopy to record light stimulus-evoked Ca2+ signals in cone axon terminals and horizontal cell dendrites as well as glutamate release in the outer plexiform layer. By selectively stimulating the two mouse cone opsins with green and UV light, we assessed whether signals from individual cones remain \"isolated\" within horizontal cell dendritic tips, or whether they spread across the dendritic arbour. Consistent with the mouse s opsin expression gradient, we found that the Ca2+ signals recorded from dendrites of dorsal horizontal cells were dominated by M- and those of ventral horizontal cells by S-opsin activation. The signals measured in neighbouring horizontal cell dendritic tips varied markedly in their chromatic preference, arguing against global processing. Rather, our experimental data and results from biophysically realistic modelling support the idea that horizontal cells can process cone input locally, extending the \"classical\" view of horizontal cells function. Pharmacologically removing horizontal cells from the circuitry reduced the sensitivity of the cone signal to low frequencies, suggesting that local horizontal cell feedback shapes the temporal properties of cone output.\n\nHighlightsO_LILight-evoked Ca2+ signals in horizontal cell dendrites reflect opsin gradient\nC_LIO_LIChromatic preferences in neighbouring dendritic tips vary markedly\nC_LIO_LIMouse horizontal cells process cone photoreceptor input locally\nC_LIO_LILocal horizontal cell feedback shapes the temporal properties of cone output\nC_LI\n\neTOC BlurbChapot et al. show that local light responses in mouse horizontal cell dendrites inherit properties, including chromatic preference, from the presynaptic cone photoreceptor, suggesting that their dendrites can provide \"private\" feedback to cones, for instance, to shape the temporal filtering properties of the cone synapse.

neuroscience↗