Nuclear and cytoplasmic RNA in visual cortical neurons of adult rats during visual deprivation and photic stimulation

1980 ◽  
Vol 90 (4) ◽  
pp. 1455-1457
Author(s):  
L. D. Malinauskaite
1980 ◽  
Vol 12 (2) ◽  
pp. 77-84
Author(s):  
I. A. Shevelev ◽  
V. G. Marchenko ◽  
I. V. Maksimova

2013 ◽  
Vol 30 (5-6) ◽  
pp. 315-330 ◽  
Author(s):  
SETH W. EGGER ◽  
KENNETH H. BRITTEN

AbstractMany complex behaviors rely on guidance from sensations. To perform these behaviors, the motor system must decode information relevant to the task from the sensory system. However, identifying the neurons responsible for encoding the appropriate sensory information remains a difficult problem for neurophysiologists. A key step toward identifying candidate systems is finding neurons or groups of neurons capable of representing the stimuli adequately to support behavior. A traditional approach involves quantitatively measuring the performance of single neurons and comparing this to the performance of the animal. One of the strongest pieces of evidence in support of a neuronal population being involved in a behavioral task comes from the signals being sufficient to support behavior. Numerous experiments using perceptual decision tasks show that visual cortical neurons in many areas have this property. However, most visually guided behaviors are not categorical but continuous and dynamic. In this article, we review the concept of sufficiency and the tools used to measure neural and behavioral performance. We show how concepts from information theory can be used to measure the ongoing performance of both neurons and animal behavior. Finally, we apply these tools to dorsal medial superior temporal (MSTd) neurons and demonstrate that these neurons can represent stimuli important to navigation to a distant goal. We find that MSTd neurons represent ongoing steering error in a virtual-reality steering task. Although most individual neurons were insufficient to support the behavior, some very nearly matched the animal’s estimation performance. These results are consistent with many results from perceptual experiments and in line with the predictions of Mountcastle’s “lower envelope principle.”


2020 ◽  
Author(s):  
Lukas Klimmasch ◽  
Johann Schneider ◽  
Alexander Lelais ◽  
Bertram E. Shi ◽  
Jochen Triesch

AbstractThe development of binocular vision is an active learning process comprising the development of disparity tuned neurons in visual cortex and the establishment of precise vergence control of the eyes. We present a computational model for the learning and self-calibration of active binocular vision based on the Active Efficient Coding framework, an extension of classic efficient coding ideas to active perception. Under normal rearing conditions, the model develops disparity tuned neurons and precise vergence control, allowing it to correctly interpret random dot stereogramms. Under altered rearing conditions modeled after neurophysiological experiments, the model qualitatively reproduces key experimental findings on changes in binocularity and disparity tuning. Furthermore, the model makes testable predictions regarding how altered rearing conditions impede the learning of precise vergence control. Finally, the model predicts a surprising new effect that impaired vergence control affects the statistics of orientation tuning in visual cortical neurons.


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