Biology subjects

Szatko, K. P.

Publications and source records attributed to Szatko, K. P..

6 recordsLinked to original sources

bioRxiv2022

A chromatic feature detector in the retina signals visual context changes

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour-opponency in transient Suppressed-by-Contrast RGCs (tSbC), a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by heador eye-movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

neuroscience↗

bioRxiv2022

Efficient coding of natural scenes improves neural system identification

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the "stand-alone" system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

neuroscience↗

bioRxiv2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal. We compare our method to previous approaches on a large-scale dataset of two-photon imaging recordings of retinal bipolar cell responses to visual stimuli. This dataset provides a unique benchmark as it contains biological signal from well-defined cell types that is obscured by large inter-experimental variability. In a supervised setting, we compare the generalization performance of cell type classifiers across experiments, which we validate with anatomical cell type distributions from electron microscopy data. In an unsupervised setting, we remove inter-experimental variability from data which can then be fed into arbitrary downstream analyses. In both settings, we find that our method achieves the best trade-off between removing inter-experimental variability and preserving biological signal. Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience. Code available at https://github.com/eulerlab/rave.

neuroscience↗

bioRxiv2020

Mouse retinal specializations reflect knowledge of natural environment statistics

Pressures for survival drive sensory circuit adaption to a species habitat, making it essential to statistically characterise natural scenes. Mice, a prominent visual system model, are dichromatic with enhanced sensitivity to green and UV. Their visual environment, however, is rarely considered. Here, we built a UV-green camera to record footage from mouse habitats. We found chromatic contrast to greatly diverge in the upper but not the lower visual field, an environmental difference that may underlie the species superior colour discrimination in the upper visual field. Moreover, training an autoencoder on upper but not lower visual field scenes was sufficient for the emergence of colour-opponent filters. Furthermore, the upper visual field was biased towards dark UV contrasts, paralleled by more light-offset-sensitive cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution. Lead contactFurther information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Thomas Euler (thomas.euler@cin.uni-tuebingen.de)

neuroscience↗

bioRxiv2019

Neural circuits in the mouse retina support color vision in the upper visual field

Color vision is essential to the survival of most animals. Its neural basis lies in the retina, where chromatic signals from different photoreceptor types sensitive to distinct wavelengths are locally compared by neural circuits. Mice, like most mammals, are generally dichromatic and have two cone photoreceptor types. However, in the ventral retina most cones display the same spectral preference, impairing spectral comparisons necessary for color vision. This conflicts with behavioral evidence showing that mice can discriminate colors only in the corresponding upper visual field. Here, we systematically investigated the neural circuits underlying mouse color vision across three processing stages of the retina by recording the output of cones, bipolar and ganglion cells using two-photon imaging. Surprisingly, we found that across all retinal layers most color-opponent cells were located in the ventral retina. This started at the level of the cone output, where color-opponency was mediated by horizontal cells and likely involving rod photoreceptors. Next, bipolar cells relayed the chromatic information to ganglion cells in the inner retina, where type-specific, non-linear center-surround interactions resulted in specific color-opponent output channels to the brain. This suggests that neural circuits in the mouse retina are specifically tuned to extract color information from the upper visual field, aiding robust detection of aerial predators and ensuring the animals survival.

neuroscience↗

bioRxiv2019

The temporal structure of the inner retina at a single glance

The retina decomposes visual stimuli into parallel channels that encode different features of the visual environment. Central to this computation is the synaptic processing in a dense and thick layer of neuropil, the so-called inner plexiform layer (IPL). Here, different types of bipolar cells stratifying at distinct depths relay the excitatory feedforward drive from photoreceptors to amacrine and ganglion cells. Current experimental techniques for studying processing in the IPL do not allow imaging the entire IPL simultaneously in the intact tissue. Here, we extend a two-photon microscope with an electrically tunable lens allowing us to obtain optical vertical slices of the IPL, which provide a complete picture of the response diversity of bipolar cells at a \"single glance\". The nature of these axial recordings additionally allowed us to isolate and investigate batch effects, i.e. inter-experimental variations resulting in systematic differences in response speed. As a proof of principle, we developed a simple model that disentangles biological from experimental causes of variability, and allowed us to recover the characteristic gradient of response speeds across the IPL with higher precision than before. Our new framework will make it possible to study the computations performed in the central synaptic layer of the retina more efficiently.

neuroscience↗