scholarly journals A kernel-based method to calculate local field potentials from networks of spiking neurons

2020 ◽  
Author(s):  
Bartosz Telenczuk ◽  
Maria Telenczuk ◽  
Alain Destexhe
Keyword(s):  
Local Field ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Point Of View ◽  
Spiking Neurons ◽  
Inhibitory Neurons ◽  
List Type ◽  
Synaptic Currents ◽  
Hippocampal Pyramidal Neurons ◽  
Low Pass

AbstractBackgroundThe local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons.New methodHere, we adopt a different point of view and relate the spiking of neurons to the LFP through efferent synaptic connections and provide a method to calculate LFPs.ResultsWe show that the so-called unitary LFPs (uLFP) provide the key to such a calculation. We show experimental measurements and simulations of uLFPs in neocortex and hippocampus, for both excitatory and inhibitory neurons. We fit a “kernel” function to measurements of uLFPs, and we estimate its spatial and temporal spread by using simulations of morphologically detailed reconstructions of hippocampal pyramidal neurons. Assuming that LFPs are the sum of uLFPs generated by every neuron in the network, the LFP generated by excitatory and inhibitory neurons can be calculated by convolving the trains of action potentials with the kernels estimated from uLFPs. This provides a method to calculate the LFP from networks of spiking neurons, even for point neurons for which the LFP is not easily defined. We show examples of LFPs calculated from networks of point neurons and compare to the LFP calculated from synaptic currents.ConclusionsThe kernel-based method provides a practical way to calculate LFPs from networks of point neurons.HighlightsWe provide a method to estimate the LFP from spiking neuronsThis method is based on kernels, estimated from experimental dataWe show applications of this method to calculate the LFP from networks of spiking neuronsWe show that the kernel-based method is a low-pass filtered version of the LFP calculated from synaptic currents

scholarly journals Modeling unitary fields and the single-neuron contribution to local field potentials in the hippocampus

2019 ◽  
Author(s):  
Maria Teleńczuk ◽  
Bartosz Teleńczuk ◽  
Alain Destexhe
Keyword(s):  
Local Field ◽  
Computational Models ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Pyramidal Cells ◽  
Inhibitory Synapses ◽  
Simple Method ◽  
Hippocampal Pyramidal Neurons

AbstractSynaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically-reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results also show that somatic inhibitory currents are large contributors of the LFP, which is important information to interpret this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically-grounded method to calculate LFPs from neural networks.

scholarly journals Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns

2018 ◽  
Author(s):  
Scott Cole ◽  
Bradley Voytek
Keyword(s):  
Local Field ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Detectable Effect ◽  
Field Recordings ◽  
Cognitive States ◽  
Open Questions ◽  
Hippocampal Theta

AbstractBrain rhythms are nearly always analyzed in the spectral domain in terms of their power, phase, and frequency. While this conventional approach has uncovered spike-field coupling, as well as correlations to normal behaviors and pathological states, emerging work has highlighted the physiological and behavioral importance of multiple novel oscillation features. Oscillatory bursts, for example, uniquely index a variety of cognitive states, and the nonsinusoidal shape of oscillations relate to physiological changes, including Parkinson’s disease. Open questions remain regarding how bursts and nonsinusoidal features relate to circuit-level processes, and how they interrelate. By analyzing unit and local field recordings in the rodent hippocampus, we uncover a number of significant relationships between oscillatory bursts, nonsinusoidal waveforms, and local inhibitory and excitatory spiking patterns. Bursts of theta oscillations are surprisingly related to a decrease in pyramidal neuron synchrony, and have no detectable effect on firing sequences, despite significant increases in neuronal firing rates during periods of theta bursting. Theta burst duration is predicted by the asymmetries of its first cycle, and cycle asymmetries relate to firing rate, synchrony, and sequences of pyramidal neurons and interneurons. These results provide compelling physiological evidence that time-domain features, of both nonsinusoidal hippocampal theta waveform and the theta bursting state, reflects local circuit properties. These results point to the possibility of inferring circuit states from local field potential features in the hippocampus and perhaps other brain regions with other rhythms.

scholarly journals Estimation of Population-Specific Synaptic Currents from Laminar Multielectrode Local Field Potential Recordings

2012 ◽  
Vol 102 (3) ◽  
pp. 546a
Author(s):  
Sergey L. Gratiy ◽  
Anna Devor ◽  
Gaute T. Einevoll ◽  
Anders M. Dale
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Synaptic Currents

scholarly journals Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis: Validation and Comparison with Non-Parametric Granger Causality

2019 ◽  
Author(s):  
Timothy O. West ◽  
David M. Halliday ◽  
Steven L. Bressler ◽  
Simon F. Farmer ◽  
Vladimir Litvak
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Local Field ◽  
Autoregressive Model ◽  
Field Potential ◽  
List Type ◽  
Signal To Noise ◽  
Neural Data ◽  
Signal Routing ◽  
Non Parametric

AbstractBackground‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015)MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing.ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.HighlightsNon-parametric directionality (NPD) is a novel directed connectivity metric.NPD estimates are equivalent to Granger causality but more robust to signal confounds.Multivariate extensions of NPD can correctly identify signal routing.AbbreviationsdFCDirected functional connectivityEEGElectroencephalogramLFPLocal field potentialMEGMagnetoencephalogramMVARMultivariate autoregressive (model)NPDNon-parametric directionalityNPGNon-parametric Granger (causality)SMASupplementary motor areaSNRSignal-to-noise ratioSTNSubthalamic Nucleus

scholarly journals Somatostatin and parvalbumin inhibitory synapses onto hippocampal pyramidal neurons are regulated by distinct mechanisms

2018 ◽  
Vol 115 (3) ◽  
pp. 589-594 ◽  
Author(s):  
Meryl E. Horn ◽  
Roger A. Nicoll
Keyword(s):  
Calcium Channels ◽  
Brain Function ◽  
Pyramidal Neurons ◽  
Neuronal Firing ◽  
Inhibitory Neurons ◽  
Inhibitory Synapses ◽  
Hippocampal Pyramidal Neurons ◽  
Excitatory Neurons ◽  
Glutamatergic Signaling ◽  
Different Populations

Excitation–inhibition balance is critical for optimal brain function, yet the mechanisms underlying the tuning of inhibition from different populations of inhibitory neurons are unclear. Here, we found evidence for two distinct pathways through which excitatory neurons cell-autonomously modulate inhibitory synapses. Synapses from parvalbumin-expressing interneurons onto hippocampal pyramidal neurons are regulated by neuronal firing, signaling through L-type calcium channels. Synapses from somatostatin-expressing interneurons are regulated by NMDA receptors, signaling through R-type calcium channels. Thus, excitatory neurons can cell-autonomously regulate their inhibition onto different subcellular compartments through their input (glutamatergic signaling) and their output (firing). Separately, while somatostatin and parvalbumin synapses onto excitatory neurons are both dependent on a common set of postsynaptic proteins, including gephyrin, collybistin, and neuroligin-2, decreasing neuroligin-3 expression selectively decreases inhibition from somatostatin interneurons, and overexpression of neuroligin-3 selectively enhances somatostatin inhibition. These results provide evidence that excitatory neurons can selectively regulate two distinct sets of inhibitory synapses.

scholarly journals Visual recognition is heralded by shifts in local field potential oscillations and inhibitory networks in primary visual cortex

2020 ◽  
Author(s):  
Dustin J. Hayden ◽  
Daniel P. Montgomery ◽  
Samuel F. Cooke ◽  
Mark F. Bear
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
High Frequency ◽  
Peak Power ◽  
Field Potential ◽  
Low Frequency ◽  
Inhibitory Neurons ◽  
Stimulus Familiarity ◽  
Frequency Power

AbstractFiltering out familiar, irrelevant stimuli eases the computational burden on the cerebral cortex. Inhibition is a candidate mechanism in this filtration process. Oscillations in the cortical local field potential (LFP) serve as markers of the engagement of different inhibitory neurons. In awake mice, we find pronounced changes in LFP oscillatory activity present in layer 4 of primary visual cortex (V1) with progressive stimulus familiarity. Over days of repeated stimulus presentation, low frequency (alpha/beta ~15 Hz peak) power increases while high frequency (gamma ~65 Hz peak) power decreases. This high frequency activity re-emerges when a novel stimulus is shown. Thus, high frequency power is a marker of novelty while low frequency power signifies familiarity. Two-photon imaging of neuronal activity reveals that parvalbumin-expressing inhibitory neurons disengage with familiar stimuli and reactivate to novelty, consistent with their known role in gamma oscillations, whereas somatostatin-expressing inhibitory neurons show opposing activity patterns, indicating a contribution to the emergence of lower frequency oscillations. We also reveal that stimulus familiarity and novelty have differential effects on oscillations and cell activity over a shorter timescale of seconds. Taken together with previous findings, we propose a model in which two interneuron circuits compete to drive familiarity or novelty encoding.

scholarly journals Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset

2017 ◽  
Vol 118 (6) ◽  
pp. 3345-3359 ◽  
Author(s):  
Nathaniel C. Wright ◽  
Mahmood S. Hoseini ◽  
Tansel Baran Yasar ◽  
Ralf Wessel
Keyword(s):  
Local Field ◽  
Visual Processing ◽  
Local Field Potential ◽  
Cortical Neurons ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Cortical Activity ◽  
Stimulus Onset ◽  
Synaptic Inputs ◽  
Network Properties

Cortical activity contributes significantly to the high variability of sensory responses of interconnected pyramidal neurons, which has crucial implications for sensory coding. Yet, largely because of technical limitations of in vivo intracellular recordings, the coupling of a pyramidal neuron’s synaptic inputs to the local cortical activity has evaded full understanding. Here we obtained excitatory synaptic conductance ( g) measurements from putative pyramidal neurons and local field potential (LFP) recordings from adjacent cortical circuits during visual processing in the turtle whole brain ex vivo preparation. We found a range of g-LFP coupling across neurons. Importantly, for a given neuron, g-LFP coupling increased at stimulus onset and then relaxed toward intermediate values during continued visual stimulation. A model network with clustered connectivity and synaptic depression reproduced both the diversity and the dynamics of g-LFP coupling. In conclusion, these results establish a rich dependence of single-neuron responses on anatomical, synaptic, and emergent network properties. NEW & NOTEWORTHY Cortical neurons are strongly influenced by the networks in which they are embedded. To understand sensory processing, we must identify the nature of this influence and its underlying mechanisms. Here we investigate synaptic inputs to cortical neurons, and the nearby local field potential, during visual processing. We find a range of neuron-to-network coupling across cortical neurons. This coupling is dynamically modulated during visual processing via biophysical and emergent network properties.

scholarly journals h-Type Membrane Current Shapes the Local Field Potential from Populations of Pyramidal Neurons

2018 ◽  
Vol 38 (26) ◽  
pp. 6011-6024 ◽  
Author(s):  
Torbjørn V. Ness ◽  
Michiel W.H. Remme ◽  
Gaute T. Einevoll
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Membrane Current

scholarly journals Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

2008 ◽  
Vol 4 (12) ◽  
pp. e1000239 ◽  
Author(s):  
Alberto Mazzoni ◽  
Stefano Panzeri ◽  
Nikos K. Logothetis ◽  
Nicolas Brunel
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Field Potential ◽  
Inhibitory Neurons

scholarly journals Dynamics of Gamma Bursts in Local Field Potentials

2015 ◽  
Vol 27 (1) ◽  
pp. 74-103 ◽  
Author(s):  
Priscilla E. Greenwood ◽  
Mark D. McDonnell ◽  
Lawrence M. Ward
Keyword(s):  
Fixed Point ◽  
Stochastic Model ◽  
Local Field ◽  
Stochastic Dynamics ◽  
Field Potential ◽  
Inhibitory Neurons ◽  
Neural Processes ◽  
Interacting Populations ◽  
Large Phase ◽  
Associated Pair

In this letter, we provide a stochastic analysis of, and supporting simulation data for, a stochastic model of the generation of gamma bursts in local field potential (LFP) recordings by interacting populations of excitatory and inhibitory neurons. Our interest is in behavior near a fixed point of the stochastic dynamics of the model. We apply a recent limit theorem of stochastic dynamics to probe into details of this local behavior, obtaining several new results. We show that the stochastic model can be written in terms of a rotation multiplied by a two-dimensional standard Ornstein-Uhlenbeck (OU) process. Viewing the rewritten process in terms of phase and amplitude processes, we are able to proceed further in analysis. We demonstrate that gamma bursts arise in the model as excursions of the modulus of the OU process. The associated pair of stochastic phase and amplitude processes satisfies their own pair of stochastic differential equations, which indicates that large phase slips occur between gamma bursts. This behavior is mirrored in LFP data simulated from the original model. These results suggest that the rewritten model is a valid representation of the behavior near the fixed point for a wide class of models of oscillatory neural processes.

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