Faculty Opinions recommendation of Dynamic modification of cortical orientation tuning mediated by recurrent connections.

Author(s):  
Mriganka Sur
Neuron ◽  
2002 ◽  
Vol 36 (5) ◽  
pp. 945-954 ◽  
Author(s):  
Gidon Felsen ◽  
Yao-song Shen ◽  
Haishan Yao ◽  
Gareth Spor ◽  
Chaoyi Li ◽  
...  

Glycobiology ◽  
2021 ◽  
Author(s):  
Hannah M Stephen ◽  
Trevor M Adams ◽  
Lance Wells

Abstract Thousands of nuclear and cytosolic proteins are modified with a single β-N-acetylglucosamine on serine and threonine residues in mammals, a modification termed O-GlcNAc. This modification is essential for normal development and plays important roles in virtually all intracellular processes. Additionally, O-GlcNAc is involved in many disease states, including cancer, diabetes, and X-linked intellectual disability. Given the myriad of functions of the O-GlcNAc modification, it is therefore somewhat surprising that O-GlcNAc cycling is mediated by only two enzymes: the O-GlcNAc transferase (OGT), which adds O-GlcNAc, and the O-GlcNAcase (OGA), which removes it. A significant outstanding question in the O-GlcNAc field is how do only two enzymes mediate such an abundant and dynamic modification. In this review, we explore the current understanding of mechanisms for substrate selection for the O-GlcNAc cycling enzymes. These mechanisms include direct substrate interaction with specific domains of OGT or OGA, selection of interactors via partner proteins, posttranslational modification of OGT or OGA, nutrient sensing, and localization alteration. Altogether, current research paints a picture of an exquisitely regulated and complex system by which OGT and OGA select substrates. We also make recommendations for future work, toward the goal of identifying interaction mechanisms for specific substrates that may be able to be exploited for various research and medical treatment goals.


2008 ◽  
Vol 20 (7) ◽  
pp. 1847-1872 ◽  
Author(s):  
Mark C. W. van Rossum ◽  
Matthijs A. A. van der Meer ◽  
Dengke Xiao ◽  
Mike W. Oram

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.


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