Temporal and spatial tuning of dorsal lateral geniculate nucleus neurons in unanesthetized rats

Sriram, Balaji; Meier, Philip; Reinagel, Pamela

doi:10.1152/jn.00812.2014

Cited by 16 publications

(15 citation statements)

References 96 publications

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“…Simple contacts dominate retinal input onto cat Y cells, whereas X cell dendritic appendages preferentially participate in complex synaptic structures (Robson & Mason, 1979; Hamos et al, 1985; Koch, 1985; Sherman & Guillery, 1996; Sherman, 2004). Similar distinctions among TC neurons have been observed in the mice by morphological analysis, but have not been associated with distinct patterns of synaptic structures nor delineated by physiology (Krahe et al, 2011; El-Danaf et al, 2015; Sriram et al, 2016). …”

Section: Retinogeniculate Synaptic Structuresupporting

confidence: 73%

An evolving view of retinogeniculate transmission

Litvina

Chen

2017

Vis Neurosci

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The thalamocortical (TC) relay neuron of the dorsoLateral Geniculate Nucleus (dLGN) has borne its imprecise label for many decades in spite of strong evidence that its role in visual processing transcends the implied simplicity of the term “relay”. The retinogeniculate synapse is the site of communication between a retinal ganglion cell and a TC neuron of the dLGN. Activation of retinal fibers in the optic tract causes reliable, rapid, and robust postsynaptic potentials that drive postsynaptics spikes in a TC neuron. Cortical and subcortical modulatory systems have been known for decades to regulate retinogeniculate transmission. The dynamic properties that the retinogeniculate synapse itself exhibits during and after developmental refinement further enrich the role of the dLGN in the transmission of the retinal signal. Here we consider the structural and functional substrates for retinogeniculate synaptic transmission and plasticity, and reflect on how the complexity of the retinogeniculate synapse imparts a novel dynamic and influential capacity to subcortical processing of visual information.

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Section: Retinogeniculate Synaptic Structuresupporting

confidence: 73%

An evolving view of retinogeniculate transmission

Litvina

Chen

2017

Vis Neurosci

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“…For the black hooded rat there is one published study investigating the OPN ( Trejo and Cicerone, 1984 ) and one for the SCN ( Aggelopoulos and Meissl, 2000 ). In contrast, rich literature exists covering light responsive cells in the dLGN of different species [mouse: ( Brown et al, 2010 , 2012 ; Piscopo et al, 2013 ; Denman et al, 2017 ; Stabio et al, 2018 ; Román Rosón et al, 2019 ); black hooded rats: ( Sriram et al, 2016 ; Jeczmien-Lazur et al, 2019 )].…”

Section: Discussionmentioning

confidence: 99%

Neuronal Responses to Short Wavelength Light Deficiency in the Rat Subcortical Visual System

et al. 2021

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The amount and spectral composition of light changes considerably during the day, with dawn and dusk being the most crucial moments when light is within the mesopic range and short wavelength enriched. It was recently shown that animals use both cues to adjust their internal circadian clock, thereby their behavior and physiology, with the solar cycle. The role of blue light in circadian processes and neuronal responses is well established, however, an unanswered question remains: how do changes in the spectral composition of light (short wavelengths blocking) influence neuronal activity? In this study we addressed this question by performing electrophysiological recordings in image (dorsal lateral geniculate nucleus; dLGN) and non-image (the olivary pretectal nucleus; OPN, the suprachiasmatic nucleus; SCN) visual structures to determine neuronal responses to spectrally varied light stimuli. We found that removing short-wavelength from the polychromatic light (cut off at 525 nm) attenuates the most transient ON and sustained cells in the dLGN and OPN, respectively. Moreover, we compared the ability of different types of sustained OPN neurons (either changing or not their response profile to filtered polychromatic light) to irradiance coding, and show that both groups achieve it with equal efficacy. On the other hand, even very dim monochromatic UV light (360 nm; log 9.95 photons/cm2/s) evokes neuronal responses in the dLGN and SCN. To our knowledge, this is the first electrophysiological experiment supporting previous behavioral findings showing visual and circadian functions disruptions under short wavelength blocking environment. The current results confirm that neuronal activity in response to polychromatic light in retinorecipient structures is affected by removing short wavelengths, however, with type and structure – specific action. Moreover, they show that rats are sensitive to even very dim UV light.

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“…We aimed in this work to demonstrate, as a proof-of-concept using rat LGN data, the utility of the proposed CNN model, and more generally, deep learning, in modeling visual encoding. The choice of rats in this study is consistent with other studies that examined rat LGN firing properties to get insights on the visual encoding in the LGN in addition to visual prosthesis studies that relied on rats as target species [50][51][52][53]. However, this model could also be employed and extended to model visual encoding in other species.…”

Section: Discussionmentioning

confidence: 61%

A deep convolutional visual encoding model of neuronal responses in the LGN

et al. 2021

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The Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

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Temporal and spatial tuning of dorsal lateral geniculate nucleus neurons in unanesthetized rats

Cited by 16 publications

References 96 publications

An evolving view of retinogeniculate transmission

An evolving view of retinogeniculate transmission

Neuronal Responses to Short Wavelength Light Deficiency in the Rat Subcortical Visual System

A deep convolutional visual encoding model of neuronal responses in the LGN

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