scholarly journals Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

2015 ◽  
Vol 11 (12) ◽  
pp. e1004584 ◽  
Author(s):  
Alberto Mazzoni ◽  
Henrik Lindén ◽  
Hermann Cuntz ◽  
Anders Lansner ◽  
Stefano Panzeri ◽  
...  
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Network Models ◽  
Integrate And Fire ◽  
Local Field Potential Lfp

scholarly journals Estimation of neural network model parameters from local field potentials (LFPs)

2019 ◽  
Author(s):  
Jan-Eirik W. Skaar ◽  
Alexander J. Stasik ◽  
Espen Hagen ◽  
Torbjørn V. Ness ◽  
Gaute T. Einevoll
Keyword(s):  
Network Model ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Low Frequency ◽  
Network Models ◽  
Neural Nets ◽  
Model Parameters ◽  
Neuron Network ◽  
Local Field Potential Lfp

AbstractMost modeling in systems neuroscience has beendescriptivewhere neural representations, that is, ‘receptive fields’, have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented bymechanisticmodels based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to infer properties of the neuronal network. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three synaptic weight parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary ‘background’ LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation.Significance statementMost of what we have learned about brain networksin vivohave come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the ground-truth parameters are known. By application of convolutional neural nets (CNNs) we found that the synaptic weights indeed could be accurately estimated from ‘background’ LFP signals, suggesting a future key role for LFP in development of network models.

scholarly journals Comparison of tuning properties of gamma and high-gamma power in local field potential (LFP) versus electrocorticogram (ECoG) in visual cortex

2019 ◽  
Author(s):  
Agrita Dubey ◽  
Supratim Ray
Keyword(s):  
Visual Cortex ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Orientation Contrast ◽  
High Gamma ◽  
Gamma Power ◽  
Electrocorticogram Ecog ◽  
Local Field Potential Lfp

AbstractElectrocorticogram (ECoG), obtained from macroelectrodes placed on the cortex, is typically used in drug-resistant epilepsy patients, and is increasingly being used to study cognition in humans. These studies often use power in gamma (30-70 Hz) or high-gamma (>80 Hz) ranges to make inferences about neural processing. However, while the stimulus tuning properties of gamma/high-gamma power have been well characterized in local field potential (LFP; obtained from microelectrodes), analogous characterization has not been done for ECoG. Using a hybrid array containing both micro and ECoG electrodes implanted in the primary visual cortex of two female macaques, we compared the stimulus tuning preferences of gamma/high-gamma power in LFP versus ECoG and found them to be surprisingly similar. High-gamma power, thought to index the average firing rate around the electrode, was highest for the smallest stimulus (0.3° radius), and decreased with increasing size in both LFP and ECoG, suggesting local origins of both signals. Further, gamma oscillations were similarly tuned in LFP and ECoG to stimulus orientation, contrast and spatial frequency. This tuning was significantly weaker in electroencephalogram (EEG), suggesting that ECoG is more like LFP than EEG. Overall, our results validate the use of ECoG in clinical and basic cognitive research.

scholarly journals Novel Porous Brain Electrodes for Augmented Local Field Potential Signal Detection

Materials ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 542 ◽  
Author(s):  
Sung Lee ◽  
Kyeong-Seok Lee ◽  
Saurav Sorcar ◽  
Abdul Razzaq ◽  
Maan-Gee Lee ◽  
...  
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Brain Activity ◽  
Field Potential ◽  
Porous Electrodes ◽  
Surface Topology ◽  
Neural Electrode ◽  
Flow Of Information ◽  
Potential Signal ◽  
Local Field Potential Lfp

Intracerebral local field potential (LFP) measurements are commonly used to monitor brain activity, providing insight into the flow of information across neural networks. Herein we describe synthesis and application of a neural electrode possessing a nano/micro-scale porous surface topology for improved LFP measurement. Compared with conventional brain electrodes, the porous electrodes demonstrate higher measured amplitudes with lower noise levels.

scholarly journals Effect of amplitude correlations on coherence in the local field potential

2014 ◽  
Vol 112 (4) ◽  
pp. 741-751 ◽  
Author(s):  
Ramanujan Srinath ◽  
Supratim Ray
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Multiple Scales ◽  
Field Potential ◽  
Interelectrode Distance ◽  
Study Phase ◽  
Temporal Correlations ◽  
Two Factors ◽  
The Brain ◽  
Local Field Potential Lfp

Neural activity across the brain shows both spatial and temporal correlations at multiple scales, and understanding these correlations is a key step toward understanding cortical processing. Correlation in the local field potential (LFP) recorded from two brain areas is often characterized by computing the coherence, which is generally taken to reflect the degree of phase consistency across trials between two sites. Coherence, however, depends on two factors—phase consistency as well as amplitude covariation across trials—but the spatial structure of amplitude correlations across sites and its contribution to coherence are not well characterized. We recorded LFP from an array of microelectrodes chronically implanted in the primary visual cortex of monkeys and studied correlations in amplitude across electrodes as a function of interelectrode distance. We found that amplitude correlations showed a similar trend as coherence as a function of frequency and interelectrode distance. Importantly, even when phases were completely randomized between two electrodes, amplitude correlations introduced significant coherence. To quantify the contributions of phase consistency and amplitude correlations to coherence, we simulated pairs of sinusoids with varying phase consistency and amplitude correlations. These simulations confirmed that amplitude correlations can significantly bias coherence measurements, resulting in either over- or underestimation of true phase coherence. Our results highlight the importance of accounting for the correlations in amplitude while using coherence to study phase relationships across sites and frequencies.

scholarly journals Visual cortex neurons phase-lock selectively to subsets of LFP oscillations

2019 ◽  
Vol 121 (6) ◽  
pp. 2364-2378 ◽  
Author(s):  
N. V. Swindale ◽  
M. A. Spacek
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Local Field ◽  
Rem Sleep ◽  
Local Field Potential ◽  
Cortical Neurons ◽  
Field Potential ◽  
Frequency Selective ◽  
Visual Cortex Neurons ◽  
Local Field Potential Lfp

It is generally thought that apart from receptive field differences, such as preferred orientation and spatial frequency selectivity, primary visual cortex neurons are functionally similar to each other. However, the genetic diversity of cortical neurons plus the existence of inputs additional to those required to explain known receptive field properties might suggest otherwise. Here we report the existence of desynchronized states in anesthetized cat area 17 lasting up to 45 min, characterized by variable narrow-band local field potential (LFP) oscillations in the range 2–100 Hz and the absence of a synchronized 1/ f frequency spectrum. During these periods, spontaneously active neurons phase-locked to variable subsets of LFP oscillations. Individual neurons often ignored frequencies that others phase-locked to. We suggest that these desynchronized periods may correspond to REM sleep-like episodes occurring under anesthesia. Frequency-selective codes may be used for signaling during these periods. Hence frequency-selective combination and frequency-labeled pathways may represent a previously unsuspected dimension of cortical organization. NEW & NOTEWORTHY We investigated spontaneous neuronal firing during periods of desynchronized local field potential (LFP) activity, resembling REM sleep, in anesthetized cats. During these periods, neurons synchronized their spikes to specific phases of multiple LFP frequency components, with some neurons ignoring frequencies that others were synchronized to. Some neurons fired at phase alignments of frequency pairs, thereby acting as phase coincidence detectors. These results suggest that internal brain signaling may use frequency combination codes to generate temporally structured spike trains.

scholarly journals Neuronal rhythms orchestrate cell assembles to distinguish perceptual categories

2017 ◽  
Author(s):  
Morteza Moazami Goudarzi ◽  
Jason Cromer ◽  
Jefferson Roy ◽  
Earl K. Miller
Keyword(s):  
Prefrontal Cortex ◽  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Beta Band ◽  
Lateral Prefrontal Cortex ◽  
Category Information ◽  
Oscillatory Rhythms ◽  
Field Coherence ◽  
Local Field Potential Lfp

AbstractCategories are reflected in the spiking activity of neurons. However, how neurons form ensembles for categories is unclear. To address this, we simultaneously recorded spiking and local field potential (LFP) activity in the lateral prefrontal cortex (lPFC) of monkeys performing a delayed match to category task with two independent category sets (Animals: Cats vs Dogs; Cars: Sports Cars vs Sedans). We found stimulus and category information in alpha and beta band oscillations. Different category distinctions engaged different frequencies. There was greater spike field coherence (SFC) in alpha (∼8-14 Hz) for Cats and in beta (∼16-22 Hz) for Dogs. Cars showed similar differences, albeit less pronounced: greater alpha SFC for Sedans and greater beta SFC for Sports Cars. Thus, oscillatory rhythms can help coordinate neurons into different ensembles. Engagement of different frequencies may help differentiate the categories.

scholarly journals Distinct frequency bands in the local field potential are differently tuned to stimulus drift rate

2018 ◽  
Vol 120 (2) ◽  
pp. 681-692 ◽  
Author(s):  
Siddhesh Salelkar ◽  
Gowri Manohari Somasekhar ◽  
Supratim Ray
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Drift Rate ◽  
Narrow Band ◽  
Field Potential ◽  
Gamma Oscillations ◽  
Frequency Bands ◽  
Wide Range ◽  
Distinct Frequency ◽  
Local Field Potential Lfp

Local field potential (LFP) recorded with a microelectrode reflects the activity of several neural processes, including afferent synaptic inputs, microcircuit-level computations, and spiking activity. Objectively probing their contribution requires a design that allows dissociation between these potential contributors. Earlier reports have shown that the primate lateral geniculate nucleus (LGN) has a higher temporal frequency (drift rate) cutoff than the primary visual cortex (V1), such that at higher drift rates inputs into V1 from the LGN continue to persist, whereas output ceases, permitting partial dissociation. Using chronic microelectrode arrays, we recorded spikes and LFP from V1 of passively fixating macaques while presenting sinusoidal gratings drifting over a wide range. We further optimized the gratings to produce strong gamma oscillations, since recent studies in rodent V1 have reported LGN-dependent narrow-band gamma oscillations. Consistent with earlier reports, power in higher LFP frequencies (above ~140 Hz) tracked the population firing rate and were tuned to preferred drift rates similar to those for spikes. Significantly, power in the lower (up to ~40 Hz) frequencies increased transiently in the early epoch after stimulus onset, even at high drift rates, and had preferred drift rates higher than for spikes/high gamma. Narrow-band gamma (50–80 Hz) power was not strongly correlated with power in high or low frequencies and had much lower preferred temporal frequencies. Our results demonstrate that distinct frequency bands of the V1 LFP show diverse tuning profiles, which may potentially convey different attributes of the underlying neural activity. NEW & NOTEWORTHY In recent years the local field potential (LFP) has been increasingly studied, but interpreting its rich frequency content has been difficult. We use a stimulus manipulation that generates different tuning profiles for low, gamma, and high frequencies of the LFP, suggesting contributions from potentially different sources. Our results have possible implications for design of better neural prosthesis systems and brain-machine interfacing applications.

Sign in / Sign up

Export Citation Format

Share Document