Statistical analysis of neuronal population codes for encoding acute pain

Author(s):  
Zhe Chen ◽  
Jing Wang
2019 ◽  
Author(s):  
Ru-Yuan Zhang ◽  
Xue-Xin Wei ◽  
Kendrick Kay

ABSTRACTPrevious studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.


2021 ◽  
Author(s):  
Samuel W Failor ◽  
Matteo Carandini ◽  
Kenneth D Harris

The response of a neuronal population to a stimulus can be summarized by a vector in a high-dimensional space. Learning theory suggests that the brain should be most able to produce distinct behavioral responses to two stimuli when the rate vectors they evoke are close to orthogonal. To investigate how learning modifies population codes, we measured the orientation tuning of 4,000-neuron populations in visual cortex before and after training on a visual discrimination task. Learning suppressed responses to the task-informative stimuli, most strongly amongst weakly-tuned neurons. This suppression reflected a simple change at the population level: sparsening of population responses to relevant stimuli, resulting in orthogonalization of their rate vectors. A model of F-I curve modulation, requiring no synaptic plasticity, quantitatively predicted the learning effect.


2014 ◽  
Vol 17 (10) ◽  
pp. 1380-1387 ◽  
Author(s):  
Yin Yan ◽  
Malte J Rasch ◽  
Minggui Chen ◽  
Xiaoping Xiang ◽  
Min Huang ◽  
...  

1998 ◽  
Vol 80 (5) ◽  
pp. 2584-2592 ◽  
Author(s):  
John E. Lewis ◽  
William B. Kristan

Lewis, John E. and William B. Kristan, Jr. Representation of touch location by a population of leech sensory neurons. J. Neurophysiol. 80: 2584–2592, 1998. To form accurate representations of the world, sensory systems must accurately encode stimuli in the spike trains of populations of neurons. The nature of such neuronal population codes is beginning to be understood. We characterize the entire sensory system underlying a simple withdrawal reflex in the leech, a bend directed away from the site of a light touch. Our studies show that two different populations of mechanosensory neurons each encode touch information with an accuracy that can more than account for the behavioral output. However, we found that only one of the populations, the P cells, is important for the behavior. The sensory representation of touch location is based on the spike counts in all of the four P cells. Further, fewer than three action potentials in the P cell population, occurring during the first 100 ms of a touch stimulus, may be required to process touch location information to produce the appropriately directed bend.


2017 ◽  
Vol 14 (3) ◽  
pp. 036023 ◽  
Author(s):  
Zhe Chen ◽  
Qiaosheng Zhang ◽  
Ai Phuong Sieu Tong ◽  
Toby R Manders ◽  
Jing Wang

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