Biology subjects

Goldin, M. A.

Publications and source records attributed to Goldin, M. A..

6 recordsLinked to original sources

bioRxiv2025

A cortical code emerges in Layer5a of S1 through temporal integration of thalamic inputs

Tactile representations in the barrel field of the primary somatosensory cortex (wS1) receive input from two thalamic nuclei: ventro-posterior-medial (VPM) and posterior-medial complex (POm). However, how these inputs generate distinct cortical representations remains unclear. Previous work revealed a sweep-stick code in rat wS1 using novel velocity-white noise whisker stimulus. Sticks are high-velocity single-whisker bumps; sweeps are large multi-whisker displacements with extended temporal profiles. We hypothesized that cortex neurons inherit 'stick' responses from VPM and 'sweep' responses from first order POm. We tested this by studying coding strategies in both thalamic nuclei and wS1 in mice, determining where the cortical sweep and stick codes originate. We found a stratified wS1 representation of sweep and stick classes, whereas in our recordings VPM and POm mostly contained stick-responding neurons. Layer 4 'stick' responses are delayed versions of VPM responses, and layer 5b 'sweep' responses come from first order POm. Notably, layer 5a 'sweep' responses arise from temporal integration of 'stick' information from VPM and first order POm. Thus, contrary to our initial hypothesis, sweep responses are not inherited from the VPM or first order POm but are constructed within the cortex. In contrast, higher order POm presented only 'sweep' responses inherited from layer 5b of cortex, with a latency similar to that in layer 5a. Our findings reveal a circuit where fast VPM-encoded 'sticks' allow fine-tuned texture processing, complemented by cortically constructed sweep responses that integrate multi-whisker information.

neuroscience↗

bioRxiv2025

Accurate spatiotemporal retinal responses require a color intensity balance fine-tuned to natural conditions

Color vision is vital for animal survival, essential for foraging and predator detection. In mice, as in other mammals, color vision originates in the retina, where photoreceptor signals are processed by neural circuits. However, retinal responses to stimuli involving multiple colors are still not well understood. One possible explanation of this knowledge gap is that previous studies have not thoroughly examined how neuronal activity adapts to a 30 seconds to a few minutes timescale when exposed to multiple color sources. To address this, we systematically varied the UV-to-green light balance with a custom-built stimulator targeting mice opsins spectra while recording retinal ganglion cell responses across the dorso-ventral axis of the retina using multielectrode arrays. Responses to full-field chirp and checkerboard stimulations with alternating UV and green light revealed that more than one order of magnitude of intensity difference favoring green M-opsin over UV S-opsin is needed for a balanced reliability in retinal ganglion cell responses in the ventral retina. An incorrect balance, with slightly increased UV light, silenced responses to green illumination. To determine if these values are consistent with natural conditions, we analyzed isomerisation rates in the mouse retina across different times of the day. We found that the M- to S-opsin activation ratio remains constant through the mesopic-photopic range, and that our empirically determined values in the ventral retina align well with these natural conditions. These lie far from a simple equalization of M- and S-opsin isomerisation rates, which we found only balances ganglion cell responses in the dorsal retina. In conclusion, a finely tuned color intensity balance matching natural light spectrum is essential for accurately measuring both fast temporal responses and detailed spatial receptive fields in the ventral retina.

neuroscience↗

bioRxiv2024

Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes

Eye growth is regulated by the visual input. Many studies suggest that the retina can detect if a visual image is focused in front or behind the back of the eye, and modulate eye growth to bring it back to focus. How can the retina distinguish between these two types of defocus? Here we simulated how eye optics transform natural images and recorded how the isolated retina responds to different types of simulated defocus. We found that some ganglion cell types could distinguish between an image focussed in front or behind the retina, by estimating spatial contrast. Aberrations in the eye optics made spatial contrast, but not luminance, a reliable cue to distinguish these two types of defocus. Our results suggest a mechanism for how the retina can estimate the sign of defocus and provide an explanation for several results aiming at mitigating strong myopia by slowing down eye growth.

neuroscience↗

bioRxiv2023

Nitric oxide mediates differential effects in mouse retinal ganglion cells

Neuromodulators have major influences on the regulation of neural circuit activity across the nervous system. Nitric oxide (NO) has been shown to be a prominent neuromodulator in many circuits and has been extensively studied in the retina. Here, it has been associated with the regulation of light adaptation, gain control, and gap junctional coupling, but its effect on the retinal output, specifically on the different types of retinal ganglion cells (RGCs), is still poorly understood. In this study, we used two-photon Ca2+imaging and multi-electrode array (MEA) recordings to measure light-evoked activity of RGCs in the ganglion cell layer in the ex vivo mouse retina. This approach allowed us to investigate the neuromodulatory effects of NO on a cell type-level. Our findings reveal that NO selectively modulates the suppression of temporal responses in a distinct subset of contrast-suppressed RGC types, increasing their activity without altering the spatial properties of their receptive fields. Given that under photopic conditions, NO release is triggered by quick changes in light levels, we propose that these RGC types signal fast contrast changes to higher visual regions. Remarkably, we found that about one-third of the RGC types, recorded using two-photon Ca2+imaging, exhibited consistent, cell type-specific adaptational response changes throughout an experiment, independent of NO. By employing a sequential-recording paradigm, we could disentangle those additional adaptational response changes from drug-induced modulations. Taken together, our research highlights the selective neuromodulatory effects of NO on RGCs and emphasizes the need of considering non-pharmacological activity changes, like adaptation, in such study designs.

neuroscience↗

bioRxiv2023

Scalable gaussian process inference of neural responses to natural images

1Predicting the responses of sensory neurons is a long-standing neuroscience goal. However, while there has been much progress in modeling neural responses to simple and/or artificial stimuli, predicting responses to natural stimuli remains an ongoing challenge. One the one hand, deep neural networks perform very well on certain data-sets, but can fail when data is limited. On the other hand, gaussian processes (GPs) perform well on limited data, but are generally poor at predicting responses to high-dimensional stimuli, such as natural images. Here we show how structured priors, e.g. for local and smooth receptive fields, can be used to scale up GPs to high-dimensional stimuli. We show that when we do this, a GP model largely outperforms a deep neural network trained to predict retinal responses to natural images, with largest differences observed when both models are trained on a very small data-set. Further, since GPs compute the uncertainty in their predictions, they are well-suited to closed-loop experiments, where stimuli are chosen actively so as to collect informative neural data. We show how this can be done in practice on our retinal data-set, so as to: (i) efficiently learn a model of retinal responses to natural images, using little data, and (ii) rapidly distinguish between competing models (e.g. a linear vs a non-linear model). In the future, our approach could be applied to other low-level sensory areas, beyond the retina.

neuroscience↗

bioRxiv2021

Context-dependent selectivity to natural scenes in the retina

Retina ganglion cells extract specific features from natural scenes and send this information to the brain. In particular, they respond to local light increase (ON responses), and/or decrease (OFF). However, it is unclear if this ON-OFF selectivity, characterized with synthetic stimuli, is maintained when they are stimulated with natural scenes. Here we recorded the responses of ganglion cells of mice and axolotls to stimuli composed of natural images slightly perturbed by patterns of random noise to determine their selectivity during natural stimulation. The ON-OFF selectivity strongly depended on the natural image. A single ganglion cell can signal luminance increase for one natural image, and luminance decrease for another. Modeling and experiments showed that this was due to the non-linear combination of different pathways of the retinal circuit. Despite the versatility of the ON-OFF selectivity, a systematic analysis demonstrated that contrast was reliably encoded in these responses. Our perturbative approach thus uncovers the selectivity of retinal ganglion cells to more complex features than initially thought during natural scene stimulation.

neuroscience↗