Biology subjects

Ramakrishna, V.

Publications and source records attributed to Ramakrishna, V..

6 recordsLinked to original sources

bioRxiv2026

Image modifications reduce differences in natural-image encoding by retinal ganglion cells between natural and optogenetic stimulation

Optogenetics is a promising approach for restoring vision after photoreceptor degeneration. Yet, little is known about how responses to natural stimuli in optogenetically treated retinas compare to photoreceptor-driven responses and how any differences might be counteracted by adjusting the stimulation. We here therefore directly compared the encoding of natural images by individual retinal ganglion cells under both conditions in mouse retinas with intact photoreceptors and channelrhodopsin expression in ganglion cells. This showed that channelrhodopsin-evoked responses display reduced dynamic range, more linear encoding of receptive-field activation, and reduced sensitivity to local spatial contrast. We thus devised image modifications, combing thresholding and scaling of pixel intensities with spatial low-pass filtering, and found that applying such modified images for optogenetic stimulation restores original encoding characteristics, yielding responses more similar to the original photoreceptor-evoked activity. These findings may help optimize stimulation of optogenetically treated retinas to achieve more natural vision in future therapeutic applications.

neuroscience↗

bioRxiv2025

A High-Dimensional Interfacial Wave Assay for Early Biophysical Profiling of Therapeutic Antibodies

Therapeutic antibody performance depends not only on sequence and structure, but also on how the molecule responds to physico-chemical perturbations encountered in its environment. Whereas sequence and static structure can be inferred under controlled conditions, behaviour is a conditional, multidimensional response to environment, most directly characterised by applying defined perturbations and quantifying the resulting dynamics. Conventional early-stage developability methods capture isolated dimensions of this response under near-equilibrium conditions, deferring integrated behavioural assessment to late-stage characterisation, when the cost of correction is highest. We introduce Variations in Interfacial Behaviour under Excitation (VIBE), an interfacial wave method implemented via Liquid State Intelligence (LSI), a sensing architecture that transduces molecular perturbations at the air-liquid interface into high-dimensional wave patterns. A colloidal liquid substrate operated near a thermodynamic transition couples small molecular perturbations to large dynamical responses, integrating structural flexibility, charge distribution, and surface hydrophobicity into a single behavioural readout from microgram-scale samples. Applied to antibodies previously characterised by industrial benchmarks, the primary behavioural descriptor, VIBE1, functioned as a high-precision triage tool, flagging candidates carrying multiple biophysical liabilities. In a clinical-stage cohort, the proportion of high-VIBE1 antibodies declined progressively from early trials through approval, and high-VIBE1 candidates showed an elevated clinical failure rate. Concordance analysis against estab-lished methods confirmed that VIBE1 captures a composite signal spanning hydrophobicity, polyreactivity, self-interaction, and thermal stability rather than recapitulating any single conventional readout. These findings establish interfacial wave sensing as a low-material modality for early-stage developability assessment, repositioning molecular behaviour from late-stage validation to discovery-phase characterisation.

biophysics↗

bioRxiv2025

Spatial Adaptation of Primate Retinal Ganglion Cells between Artificial and Natural Stimuli

The retina encodes a broad range of stimuli, adapting its computations to features like brightness, contrast, or motion. However, it is unclear to what extent it also adapts to spatial frequency content - as theories of efficient coding would predict - for instance, when switching between natural scenes and white noise. To address this, we analyzed neural activity of marmoset retinal ganglion cells (RGCs) in response to white noise and naturalistic movie stimuli. We trained linear-nonlinear models on both stimuli, evaluated their performance and compared their receptive fields (RFs) across the stimulus domains. We found that the models with spatial filters trained on either one of the stimulus ensembles were not able to predict the neural activity on the other as accurately as the models trained on the target stimulus. This suggests that spatial processing adapts to stimulus statistics. Different RGC types exhibited distinct changes: the midget OFF cells RFs became enlarged under natural movie statistics, resulting in a lower cutoff frequency. Parasol cells did not change their RF size significantly. Large OFF cells RFs decreased in size. All cell types exhibited stronger surrounds under natural movies, resembling the whitening filters predicted by efficient coding. However, quantifying the effect of the filter adaptation on the stimulus power spectrum showed a significant contribution towards whitening only in ON parasol cells. The whitening effect emerged regardless of the training stimulus. These results suggest that while RGCs adapt to the spatial frequency content of the input, efficient coding can only partially account for this adaptation. Significance statementNatural scenes differ from artificial stimuli like white noise, in spatial frequency structure. How the retina adapts to these differences remains unclear. To explore this, we studied responses of four primate retinal ganglion cell types to artificial and natural stimuli. Our results show that some cell types, like midget cells, enlarge their receptive fields and surrounds under natural stimuli, while others, like parasol cells, only enhance surrounds. These changes align qualitatively with the efficient coding theory, which posits redundancy reduction. However, in three cell types, the enhanced surrounds did not significantly whiten responses to natural stimuli, contrary to efficient coding predictions. These findings challenge how fully efficient coding explains retinal adaptation, suggesting that other principles underlie the processing of visual inputs.

systems biology↗

bioRxiv2024

Accelerated spike-triggered non-negative matrix factorization reveals coordinated ganglion cell subunit mosaics in the primate retina

A standard circuit motif in sensory systems is the pooling of sensory information from an upstream neuronal layer. A downstream neuron thereby collects signals across different locations in stimulus space, which together compose the neurons receptive field. In addition, nonlinear transformations in the signal transfer between the layers give rise to functional subunits inside the receptive field. For ganglion cells in the vertebrate retina, for example, receptive field subunits are thought to correspond to presynaptic bipolar cells. Identifying the number and locations of subunits from the stimulus-response relationship of a recorded ganglion cell has been an ongoing challenge in order to characterize the retinas functional circuitry and to build computational models that capture nonlinear signal pooling. Here we present a novel version of spike-triggered non-negative matrix factorization (STNMF), which can extract localized subunits in ganglion-cell receptive fields from recorded spiking responses under spatiotemporal white-noise stimulation. The method provides a more than 100-fold speed increase compared to a previous implementation, which can be harnessed for systematic screening of hyperparameters, such as sparsity regularization. We demonstrate the power and flexibility of this approach by analyzing populations of ganglion cells from salamander and primate retina. We find that subunits of midget as well as parasol ganglion cells in the marmoset retina form separate mosaics that tile visual space. Moreover, subunit mosaics show alignment with each other for ON and OFF midget as well as for ON and OFF parasol cells, indicating a spatial coordination of ON and OFF signals at the bipolar-cell level. Thus, STNMF can reveal organizational principles of signal transmission between successive neural layers, which are not easily accessible by other means.

neuroscience↗

bioRxiv2024

Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies

Retinal ganglion cells, the output neurons of the vertebrate retina, often display nonlinear summation of visual signals over their receptive fields. This creates sensitivity to spatial contrast, letting the cells respond to spatially structured visual stimuli even when no net change in overall illumination of the receptive field occurs. Yet, computational models of ganglion cell responses are often based on linear receptive fields, and typical nonlinear extensions, which separate receptive fields into nonlinearly combined subunits, are often cumbersome to fit to experimental data. Previous work has suggested to model spatial-contrast sensitivity in responses to flashed images by combining signals from the mean and variance of light intensity inside the receptive field. Here, we extend and adjust this spatial contrast model for application to spatiotemporal stimulation and explore its performance on spiking responses that we recorded from ganglion cells of marmosets under artificial and naturalistic movies. We show how the model can be fitted to experimental data and that it outperforms common models with linear spatial integration to different degrees for different types of ganglion cells. Finally, we use the model framework to infer the cells spatial scale of nonlinear spatial integration. Our work shows that the spatial contrast model can capture aspects of nonlinear spatial integration in the primate retina with only few free parameters. The model can be used to assess the cells functional properties under natural stimulation and provides a simple-to-obtain benchmark for comparison with more detailed nonlinear encoding models. Author SummaryOur visual experience depends on the retinas remarkable ability to detect light patterns and contrast in the world around us. Retinal ganglion cells, the output neurons of the retina, modulate their activity based on signals within small, specific regions of the visual scene, called their receptive fields. But many cells do not only encode overall brightness, summed linearly across the receptive field, but are also sensitive to local spatial contrast, that is, variations in brightness within the receptive field. Computational models that account for this nonlinear spatial integration exist, but require large amounts of data and are challenging to fit. We therefore developed the spatial contrast model, which takes a simple measure of light-intensity variations as an input, and tested it on measured responses of primate retinal ganglion cells to both artificial and naturalistic movies. The model substantially outperformed standard models with linear receptive fields, despite having only one additional tunable parameter. Furthermore, we used the model to investigate the spatial scale at which the cells integrate spatial contrast and found striking consistency across cell types. The spatial contrast model thus offers a practical tool for capturing retinal stimulus encoding and a simple-to-obtain benchmark for modeling nonlinear spatial integration.

neuroscience↗

bioRxiv2023

Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cells receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within less than an hour of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.

neuroscience↗