Parallel processing of natural images by overlapping retinal neuronal ensembles

Abstract

Even though the retinal microcircuit organization has been described in detail at the single-cell level, little is known about how groups of retinal cells' coordinated activity encode and process parallel information representing the spatial and temporal structure of changing environmental conditions. To describe the population dynamics of retinal neuronal ensembles, we used microelectrode array recordings that describe hundreds of retinal ganglion cells' simultaneous activity in response to a short movie captured in the natural environment where our subject develops their visual behaviors. The vectorization of population activity allowed the identification of retinal neuronal ensembles that synchronize to specific segments of natural stimuli. These synchronous retinal neuronal ensembles were reliably activated by the same stimuli at different trials, indicating a robust population response of retinal microcircuits. The generation of asynchronous events required integrating a physiologically meaningful time window larger than 80 ms, demonstrating that retinal neuronal ensembles' time integration filters non-structured visual information. Interestingly, individual neurons could be part of several ensembles indicating that parallel circuits could encode environmental conditions changes. We conclude that parallel neuronal ensembles could represent the functional unit of retinal computations and propose that the further study of retinal neuronal ensembles could reveal emergent properties of retinal circuits that individual cells' activity cannot explain.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…For peripheral nervous system recordings, discrimination and identification of APs cannot lack spike sorting either (Metcalfe et al, 2021 ). Spike sorting is required to isolate retinal ganglion cells based on their multiunit activity recordings (Tsai et al, 2017 ; Pérez-Ortega et al, 2021 ) and identify their electrical responses (Li et al, 2021 ), but non-neural tissues can also benefit from it by applying spike sorting algorithms, i.e., on pancreatic biosignals (Iniguez-Lomeli et al, 2021 ).…”