Biology subjects

Sokoloski, S.

Publications and source records attributed to Sokoloski, S..

3 recordsLinked to original sources

bioRxiv2023

Effects of non-retinal inputs in visual thalamus depend on visual responsiveness and stimulus context

In the dorsolateral geniculate nucleus (dLGN) of the thalamus, stimulus-driven signals are combined with modulatory inputs such as corticothalamic (CT) feedback and behavioural state. How these shape dLGN activity remains an open question. We recorded extracellular responses in dLGN of awake mice to a movie stimulus, while photosuppressing CT feedback, and tracking locomotion and pupil size. To assess the relative impact of stimulus and modulatory inputs, we fit single neuron responses with generalized linear models. While including CT feedback and behavioural state as predictors significantly improved the models overall performance, the improvement was especially pronounced for a subpopulation of neurons poorly responsive to the movie stimulus. In addition, the observed impact of CT feedback was faster and more prevalent in the absence of a patterned visual stimulus. Finally, for neurons that were sensitive to CT feedback, visual stimuli could be more easily discriminated based on spiking activity when CT feedback was suppressed. Together, these results show that effects of modulatory inputs in dLGN depend on visual responsiveness and stimulus type.

neuroscience↗

bioRxiv2023

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of "predatory"-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

neuroscience↗

bioRxiv2020

Modelling the neural code in large populations of correlated neurons

Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.

neuroscience↗