scholarly journals Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia

2014 ◽  
Vol 37 (3) ◽  
pp. 417-437 ◽  
Author(s):  
Axel Hutt ◽  
Laure Buhry
Keyword(s):  
Tonic Inhibition ◽  
Single Neurons ◽  
Neural Populations

scholarly journals A Statistical Model of Shared Variability in the Songbird Auditory System

2017 ◽  
Author(s):  
Lars Buesing ◽  
Ana Calabrese ◽  
John P. Cunningham ◽  
Sarah M. N. Woolley ◽  
Liam Paninski
Keyword(s):  
Simple Model ◽  
Simple Structure ◽  
Vocal Communication ◽  
Factor Model ◽  
Primary Auditory Cortex ◽  
Single Neurons ◽  
Correlated Activity ◽  
Neural Populations ◽  
Cortical Regions ◽  
Simultaneous Activity

AbstractVocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

scholarly journals High precision coding in visual cortex

2019 ◽  
Author(s):  
Carsen Stringer ◽  
Michalis Michaelos ◽  
Marius Pachitariu
Keyword(s):  
Visual Cortex ◽  
Sensory Cortex ◽  
Visual Features ◽  
Single Neurons ◽  
Neural Noise ◽  
Learning Tasks ◽  
Visual Areas ◽  
Neural Populations ◽  
Behavioral States ◽  
Sequential Nature

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

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