More than expected: extracellular waveforms and functional responses in monkey LGN

Abstract

Unlike the exhaustive determination of cell types in the retina, key populations in the lateral geniculate nucleus of the thalamus (LGN) may have been missed. Here, we have begun to characterize the full range of extracellular neuronal responses in the LGN of awake monkeys using multi-electrodes during the presentation of colored noise visual stimuli to identify any previously overlooked signals. Extracellular spike waveforms of single units were classified into seven distinct classes, revealing previously unrecognized diversity: four negative-dominant classes that were narrow or broad, one triphasic class, and two positive-dominant classes. Based on their mapped receptive field (RF), these units were further categorized into either magnocellular (M), parvocellular (P), koniocellular (K), or non-RF (N). We found correlations between spike shape and mapped RF and response characteristics, with negative and narrow spiking waveform units predominantly associated with P and N RFs, and positive waveforms mostly linked to M RFs. Responses from positive waveforms exhibited shorter latencies, larger RF sizes, and were associated with larger eccentricities in the visual field than the other waveform classes. Additionally, N cells, those without an estimated RF, were consistently responsive to the visually presented mapping stimulus at a lower and more sustained rate than units with an RF. These findings suggest that the LGN cell population may be more diverse than previously believed. Significance statementThis study uncovers evidence for an intricate diversity of neuronal responses within the lateral geniculate nucleus (LGN), challenging conventional classifications and revealing previously overlooked populations. By characterizing extracellular spike waveforms and revising receptive field classifications, we provide novel insights into LGN function. Our findings have significant implications for understanding early visual processing mechanisms and interpreting extracellular signals in neural circuits. Furthermore, we identify non-receptive field units, prompting exploration into their functional roles and broader implications for visual and non-visual computations. This study not only advances our understanding of LGN organization but also highlights the importance of considering recording biases in electrophysiological studies. Overall, our work opens new avenues for interdisciplinary research and contributes to advancing our knowledge of neural dynamics in the visual system.