Estimating the correlation between bursty spike trains and local field potentials

2014 ◽  
Vol 57 ◽  
pp. 63-72 ◽  
Author(s):  
Zhaohui Li ◽  
Gaoxiang Ouyang ◽  
Li Yao ◽  
Xiaoli Li
2015 ◽  
Vol 16 (Suppl 1) ◽  
pp. P194
Author(s):  
Jacob Yates ◽  
Evan Archer ◽  
Alexander C Huk ◽  
Il Memming Park

2008 ◽  
Vol 99 (3) ◽  
pp. 1461-1476 ◽  
Author(s):  
Malte J. Rasch ◽  
Arthur Gretton ◽  
Yusuke Murayama ◽  
Wolfgang Maass ◽  
Nikos K. Logothetis

We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted. Two kinds of features of the LFP are exploited for prediction: the frequency power of bands in the high γ-range (40–90 Hz) and information contained in low-frequency oscillations (<10 Hz), where both phase and power modulations are informative. Information analysis revealed that both features code (mainly) independent aspects of the spike-to-LFP relationship, with the low-frequency LFP phase coding for temporally clustered spiking activity. Although both features and prediction quality are similar during seminatural movie stimuli and spontaneous activity, prediction performance during spontaneous activity degrades much more slowly with increasing electrode distance. The general trend of data obtained with anesthetized animals is qualitatively mirrored in that of a more limited data set recorded in V1 of non-anesthetized monkeys. In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity.


2010 ◽  
Vol 103 (5) ◽  
pp. 2912-2930 ◽  
Author(s):  
Diego A. Gutnisky ◽  
Krešimir Josić

Experimental advances allowing for the simultaneous recording of activity at multiple sites have significantly increased our understanding of the spatiotemporal patterns in neural activity. The impact of such patterns on neural coding is a fundamental question in neuroscience. The simulation of spike trains with predetermined activity patterns is therefore an important ingredient in the study of potential neural codes. Such artificially generated spike trains could also be used to manipulate cortical neurons in vitro and in vivo. Here, we propose a method to generate spike trains with given mean firing rates and cross-correlations. To capture this statistical structure we generate a point process by thresholding a stochastic process that is continuous in space and discrete in time. This stochastic process is obtained by filtering Gaussian noise through a multivariate autoregressive (AR) model. The parameters of the AR model are obtained by a nonlinear transformation of the point-process correlations to the continuous-process correlations. The proposed method is very efficient and allows for the simulation of large neural populations. It can be optimized to the structure of spatiotemporal correlations and generalized to nonstationary processes and spatiotemporal patterns of local field potentials and spike trains.


2021 ◽  
Vol 11 (7) ◽  
pp. 882
Author(s):  
Yeon Hee Yu ◽  
Seong-Wook Kim ◽  
Dae-Kyoon Park ◽  
Ho-Yeon Song ◽  
Duk-Soo Kim ◽  
...  

Increased prevalence of chronic kidney disease (CKD) and neurological disorders including cerebrovascular disease, cognitive impairment, peripheral neuropathy, and dysfunction of central nervous system have been reported during the natural history of CKD. Psychological distress and depression are serious concerns in patients with CKD. However, the relevance of CKD due to decline in renal function and the pathophysiology of emotional deterioration is not clear. Male Sprague Dawley rats were divided into three groups: sham control, 5/6 nephrectomy at 4 weeks, and 5/6 nephrectomy at 10 weeks. Behavior tests, local field potentials, and histology and laboratory tests were conducted and investigated. We provided direct evidence showing that CKD rat models exhibited anxiogenic behaviors and depression-like phenotypes, along with altered hippocampal neural oscillations at 1–12 Hz. We generated CKD rat models by performing 5/6 nephrectomy, and identified higher level of serum creatinine and blood urea nitrogen (BUN) in CKD rats than in wild-type, depending on time. In addition, the level of α-smooth muscle actin (α-SMA) and collagen I for renal tissue was markedly elevated, with worsening fibrosis due to renal failures. The level of anxiety and depression-like behaviors increased in the 10-week CKD rat models compared with the 4-week rat models. In the recording of local field potentials, the power of delta (1–4 Hz), theta (4–7 Hz), and alpha rhythm (7–12 Hz) was significantly increased in the hippocampus of CKD rats compared with wild-type rats. Together, our findings indicated that anxiogenic behaviors and depression can be induced by CKD, and these abnormal symptoms can be worsened as the onset of CKD was prolonged. In conclusion, our results show that the hippocampus is vulnerable to uremia.


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