Biology subjects

Rudzite, A. M.

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

4 recordsLinked to original sources

bioRxiv2023

Decomposition of retinal ganglion cell electrical images for cell type and functional inference

ObjectiveIdentifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. ApproachThe electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. ResultsThe decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches. SignificanceThese findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.

neuroscience↗

bioRxiv2023

Population encoding of stimulus features along the visual hierarchy

The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.

neuroscience↗

bioRxiv2022

Large scale interrogation of retinal cell functions by 1-photon light-sheet microscopy

Visual processing in the retina depends on the collective activity of large ensembles of neurons organized in different layers. Current techniques for measuring activity of layer-specific neural ensembles rely on expensive pulsed infrared lasers to drive 2-photon activation of calcium-dependent fluorescent reporters. Here, we present a 1-photon light-sheet imaging system that can measure the activity in hundreds of ex vivo retinal neurons over a large field of view while simultaneously presenting visual stimuli. This allowed for a reliable functional classification of different retinal ganglion cell types. We also demonstrate that the system has sufficient resolution to image calcium entry at individual synaptic release sites across the axon terminals of dozens of simultaneously imaged bipolar cells. The simple design, a large field of view, and fast image acquisition, make this a powerful system for high-throughput and high-resolution measurements of retinal processing at a fraction of the cost of alternative approaches.

neuroscience↗

bioRxiv2022

The functional organization of retinal ganglion cell receptive fields across light levels

Two major functions performed by the retina are to establish the parallel processing of visual information and to adapt visual encoding to the trillion-fold range of light intensities encountered in the environment. Previous work has highlighted many specialized cell types and circuits that instantiate parallel processing and light adaptation. However, fully understanding either process requires identifying how light adaptation and parallel processing interact. One possibility is that light adaptation causes uniform or proportional scaling to the receptive fields (RFs) of different retinal ganglion cell (RGC) types, the output neurons of the retina. Alternatively, light adaptation could cause a reorganization of RF structures across RGC types. To resolve these possibilities, we examined how the spatiotemporal RF structure of six simultaneously measured RGC types in the rat retina change from rod- to cone-mediated light levels. While light adaptation altered the RF properties of all six RGC types, we found that the relative structure across different RGC types was largely preserved across light levels. Surprisingly, most RGC types retained their center-surround RF structure even at low light levels, an observation that is at odds with prior efficient coding predictions. However, we show these predictions are incomplete and when RFs interact over a finite viewing area, efficient coding predicts the retention of surrounds under low signal-to-noise conditions.

neuroscience↗