Name
Title | ||
|---|---|---|
Cellular and Circuit Mechanisms Maintain Low Spike Co-Variability and Enhance Population Coding in Somatosensory Cortex. |
Abstract | ||
|---|---|---|
The responses of cortical neurons are highly variable across repeated presentations of a stimulus. Understanding this variability is critical for theories of both sensory and motor processing, since response variance affects the accuracy of neural codes. Despite this influence, the cellular and circuit mechanisms that shape the trial-to-trial variability of populations responses remain poorly understood. We used a combination of experimental and computational techniques to uncover the mechanisms underlying response variability of populations of pyramidal (E) cells in layer 2/3 of rat whisker barrel cortex. Spike train recorded from pairs of E-cells during either spontaneous activity or whisker deflected responses show similarly low levels of spiking co-variability, despite large differences in network activation between the two states. We developed network models that show how spike threshold non-linearities dilute E-cell spiking co-variability during spontaneous activity and low velocity whisker deflections. In contrast, during high velocity whisker deflections, cancelation mechanisms mediated by feedforward inhibition maintain low E-cell pairwise co-variability. Thus, the combination of these two mechanisms ensure low E-cell population variability over a wide range of whisker deflection velocities. Finally, we show how this active decorrelation of population variability leads to a drastic increase in the population information about whisker velocity. The prevalence of spiking non-linearities and feedforward inhibition in the nervous system suggests that the mechanisms for low network variability presented in our study may generalize throughout the brain. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3389/fncom.2012.00007 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
Keywords | Field | DocType |
layer 2/3 somatosensory cortex,whisker stimulation,noise correlation,Fisher information | Population,Neuroscience,Neural coding,Computer science,Barrel cortex,Nervous system,Somatosensory system,Artificial intelligence,Stimulus (physiology),Sensory system,Machine learning,Feed forward | Journal |
Volume | ISSN | Citations |
6 | 1662-5188 | 8 |
PageRank | References | Authors |
0.59 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Ly | 1 | 19 | 3.50 |
| Jason W Middleton | 2 | 8 | 0.59 |
| Brent Doiron | 3 | 168 | 17.71 |