scholarly journals Inferring Spike Trains From Local Field Potentials

2008 ◽  
Vol 99 (3) ◽  
pp. 1461-1476 ◽  
Author(s):  
Malte J. Rasch ◽  
Arthur Gretton ◽  
Yusuke Murayama ◽  
Wolfgang Maass ◽  
Nikos K. Logothetis
Keyword(s):  
Spontaneous Activity ◽  
Local Field ◽  
Local Field Potentials ◽  
Low Frequency ◽  
Spike Trains ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Support Vector ◽  
Field Potentials ◽  
Data Set ◽  
Spiking Activity

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.

Changes in the spatiotemporal pattern of spontaneous activity across a cortical column after noise trauma

2021 ◽  
Author(s):  
Vinay Parameshwarappa ◽  
Laurent Pezard ◽  
Arnaud Jean Norena
Keyword(s):  
Hearing Loss ◽  
Spontaneous Activity ◽  
Local Field ◽  
Local Field Potentials ◽  
Spatiotemporal Pattern ◽  
Noise Induced Hearing Loss ◽  
Field Potentials ◽  
Cortical Column ◽  
Cortical Layers ◽  
Noise Trauma

In the auditory modality, noise trauma has often been used to investigate cortical plasticity as it causes cochlear hearing loss. One limitation of these past studies, however, is that the effects of noise trauma have been mostly documented at the granular layer, which is the main cortical recipient of thalamic inputs. Importantly, the cortex is composed of six different layers each having its own pattern of connectivity and specific role in sensory processing. The present study aims at investigating the effects of acute and chronic noise trauma on the laminar pattern of spontaneous activity in primary auditory cortex of the anesthetized guinea pig. We show that spontaneous activity is dramatically altered across cortical layers after acute and chronic noise-induced hearing loss. First, spontaneous activity was globally enhanced across cortical layers, both in terms of firing rate and amplitude of spike-triggered average of local field potentials. Second, current source density on (spontaneous) spike-triggered average of local field potentials indicates that current sinks develop in the supra- and infragranular layers. These latter results suggest that supragranular layers become a major input recipient and that the propagation of spontaneous activity over a cortical column is greatly enhanced after acute and chronic noise-induced hearing loss. We discuss the possible mechanisms and functional implications of these changes.

scholarly journals Canonical correlations reveal co-variability between spike trains and local field potentials in area MT)

2015 ◽  
Vol 16 (Suppl 1) ◽  
pp. P194
Author(s):  
Jacob Yates ◽  
Evan Archer ◽  
Alexander C Huk ◽  
Il Memming Park
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Spike Trains ◽  
Field Potentials ◽  
Area Mt ◽  
Canonical Correlations

scholarly journals Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

eNeuro ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. ENEURO.0178-19.2019 ◽  
Author(s):  
Junmo An ◽  
Taruna Yadav ◽  
John P. Hessburg ◽  
Joseph T. Francis
Keyword(s):  
Motor Cortex ◽  
Local Field ◽  
Primary Motor Cortex ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Field Coherence ◽  
Spiking Activity

scholarly journals Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

2019 ◽  
Author(s):  
Lorena Casado-Román ◽  
Guillermo V. Carbajal ◽  
David Pérez-González ◽  
Manuel S. Malmierca
Keyword(s):  
Prefrontal Cortex ◽  
Auditory System ◽  
Local Field ◽  
Prediction Error ◽  
Time Course ◽  
Local Field Potentials ◽  
Predictive Processing ◽  
Field Potentials ◽  
Deviance Detection ◽  
Spiking Activity

AbstractThe mismatch negativity (MMN) is a key biomarker of automatic deviance detection thought to emerge from two cortical sources. First, the auditory cortex (AC) encodes spectral regularities and reports frequency-specific deviances. Then, more abstract representations in the prefrontal cortex (PFC) allow to detect contextual changes of potential behavioral relevance. However, the precise location and time asynchronies between neuronal correlates underlying this fronto-temporal network remain unclear and elusive. Our study presented auditory oddball paradigms along with ‘no-repetition’ controls to record mismatch responses in neuronal spiking activity and local field potentials at the rat medial PFC. Whereas mismatch responses in the auditory system are mainly induced by stimulus-dependent effects, we found that auditory responsiveness in the PFC was driven by unpredictability, yielding context-dependent, comparatively delayed, more robust and longer-lasting mismatch responses mostly comprised of prediction error signaling activity. This characteristically different composition discarded that mismatch responses in the PFC could be simply inherited or amplified downstream from the auditory system. Conversely, it is more plausible for the PFC to exert top-down influences on the AC, since the PFC exhibited flexible and potent predictive processing, capable of suppressing redundant input more efficiently than the AC. Remarkably, the time course of the mismatch responses we observed in the spiking activity and local field potentials of the AC and the PFC combined coincided with the time course of the large-scale MMN-like signals reported in the rat brain, thereby linking the microscopic, mesoscopic and macroscopic levels of automatic deviance detection.

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