Biology subjects

Cooler, S.

Publications and source records attributed to Cooler, S..

5 recordsLinked to original sources

bioRxiv2024

Functional diversity in the output of the primate retina

The visual image transmitted by the retina to the brain has long been understood in terms of spatial filtering by the center-surround receptive fields of retinal ganglion cells (RGCs). Recently, this textbook view has been challenged by the stunning functional diversity and specificity observed in [~]40 distinct RGC types in the mouse retina. However, it is unclear whether the [~]20 morphologically and molecularly identified RGC types in primates exhibit similar functional diversity, or instead exhibit center-surround organization at different spatial scales. Here, we reveal striking and surprising functional diversity in macaque and human RGC types using large-scale multi-electrode recordings from isolated macaque and human retinas. In addition to the five well-known primate RGC types, 18-27 types were distinguished by their functional properties, likely revealing several previously unknown types. Surprisingly, many of these cell types exhibited striking non-classical receptive field structure, including irregular spatial and chromatic properties not previously reported in any species. Qualitatively similar results were observed in recordings from the human retina. The receptive fields of less-understood RGC types formed uniform mosaics covering visual space, confirming their classification, and the morphological counterparts of two types were established using single-cell recording. The striking receptive field diversity was paralleled by distinctive responses to natural movies and complexity of visual computation. These findings suggest that diverse RGC types, rather than merely filtering the scene at different spatial scales, instead play specialized roles in human vision.

neuroscience↗

bioRxiv2024

Modeling responses of macaque and human retinal ganglion cells to natural images using a convolutional neural network

Linear-nonlinear (LN) cascade models provide a simple way to capture retinal ganglion cell (RGC) responses to artificial stimuli such as white noise, but their ability to model responses to natural images is limited. Recently, convolutional neural network (CNN) models have been shown to produce light response predictions that were substantially more accurate than those of a LN model. However, this modeling approach has not yet been applied to responses of macaque or human RGCs to natural images. Here, we train and test a CNN model on responses to natural images of the four numerically dominant RGC types in the macaque and human retina - ON parasol, OFF parasol, ON midget and OFF midget cells. Compared with the LN model, the CNN model provided substantially more accurate response predictions. Linear reconstructions of the visual stimulus were more accurate for CNN compared to LN model-generated responses, relative to reconstructions obtained from the recorded data. These findings demonstrate the effectiveness of a CNN model in capturing light responses of major RGC types in the macaque and human retinas in natural conditions.

neuroscience↗

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↗

bioRxiv2021

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We used visually-evoked responses to classify 1859 mouse RGCs into 42 types. We also obtained morphological or transcriptomic data from subsets and used these measurements to align the functional classification to publicly available morphological and transcriptomic data sets. We created an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

neuroscience↗

bioRxiv2020

An offset ON-OFF receptive field is created by gap junctions between distinct types of retinal ganglion cells

Receptive fields (RFs) are a foundational concept in sensory neuroscience. The RF of a sensory neuron is shaped by the properties of its synaptic inputs from connected neurons. In the early visual system, retinotopic maps define a strict relationship between the location of a cells dendrites and its RF location in visual space1-3. Retinal ganglion cells (RGCs), the output cells of the retina, form dendritic mosaics that tile retinal space and have corresponding RF mosaics that tile visual space1,2. The precise location of dendrites in some RGCs has been shown to predict their RF shape4. Previously described ON-OFF RGCs have aligned dendrites in ON and OFF synaptic layers, so the cells respond to increments and decrements of light at the same locations in visual space5-8. Here we report a systematic offset between the ON and OFF RFs of an RGC type. Surprisingly, this property does not come from offset ON and OFF dendrites but instead arises from electrical synapses with RGCs of a different type. This circuit represents a new channel for direct communication between ON and OFF RGCs. Using a multi-cell model, we find that offset ON-OFF RFs could improve the precision with which edge location is represented in an RGC population.

neuroscience↗