Information processing in the LGN: a comparison of neural codes and cell types

Abstract

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus ( rate code ), or additional information is contained in the temporal pattern of the spikes ( temporal code ). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient “temporal code” is employed, while for X-OFF cells a straightforward “rate code” is used, which is more reliable and is correlated with energy consumption.

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

“…But why is carrying out this task cardinal? The fact that neighboring neurons are not necessarily the nearest in connections, i.e., are activated by different pathways or stimuli (Rey et al, 2015 ) mainly owing to information processing and energy optimization through structural solutions (Pregowska et al, 2019 ) calls the promise of neural decoding. This concept, although not equal with spike sorting, relies heavily on it and undertakes the risk of bias and wrong intensity estimation generated by spike sorting (Shibue and Komaki, 2017 ).…”

“…By employing a three-to-five standard deviation threshold, most authors agree that spike prominence is correctly estimated (Laboy-Juárez et al, 2019 ), while others focus on loss minimization and push threshold values lower (Bigelow and Malone, 2021 ; Chou et al, 2021 ). Instead of declaring signal standard deviation as the event detection threshold, it may be reasonable to surge robustness against alternating firing rates and calculate with median values (by recognizing that high amplitudes or spiking activity could only represent a small fraction of the recorded data) (Pregowska et al, 2019 ). Therefore, an automatically set threshold value could be determined as follows (Quian and Nadasdy, 2004 ):…”

Effects of three-dimension movie visual fatigue on cognitive performance and brain activity

“…Moreover, there was a significant repeated measures correlation between CFF and the oxy-Hb signal change around the right primary somatosensory cortex for the visual task in the 3D condition [ r rm (15) = 0.644], indicating that brain activity around the right primary somatosensory cortex in the visual task deteriorated among the participants with increased visual fatigue after watching the 1-h movie in 3D. It has been suggested that the lateral geniculate nucleus of the thalamus receives visual information from the eye ( Bundesen et al, 2005 ; Pregowska et al, 2019 ) and that the thalamus contributes to the conscious perception or awareness of visual information ( Saalmann and Kastner, 2011 ). Furthermore, the thalamus connects to the primary somatosensory cortex structurally and functionally; thus, the interactions between them are pertinent to the cognitive process ( Kang et al, 2018 ).…”

Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

“…The spike rate plays a major role in encoding the information transmitted by the spikes [22] and can show large variability, depending on the specific stimulus. As a consequence, it is very important to design a spike detector with a performance (almost) insensitive to the spike firing rate [23].…”