Neural timing of stimulus events with microsecond precision

Luo, Jinhong; Macías, Silvio; Ness, Torbjørn V.; Einevoll, Gaute T.; Zhang, Kechen; Moss, Cynthia F.

doi:10.1371/journal.pbio.2006422

Cited by 24 publications

(12 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational models of the bat auditory system predict that neural circuits arranged to detect synchronized patterns in spike trains across a broad bandwidth can explain how echo features encode cues related to target shape and textures [ 13 ], but the location(s) of the networks are still unknown. It is well-established that the bat inferior colliculus uses precise spike timing for duration, interval, and spectral motion tuning [ 59 – 61 ], but there are also indications that the bat auditory cortex may be uniquely wired to integrate spike-timing information [ 8 , 13 , 62 ], although the relative contributions of temporal codes versus rates codes in bat A1 have not previously been evaluated. In this paper, we try to fill that gap by showing that spectral shape information in the bat A1 is better encoded in the spike timing than in the spike rate of individual neurons.…”

Section: Discussionmentioning

confidence: 99%

Temporal coding of echo spectral shape in the bat auditory cortex

et al. 2020

Self Cite

View full text Add to dashboard Cite

Echolocating bats rely upon spectral interference patterns in echoes to reconstruct fine details of a reflecting object’s shape. However, the acoustic modulations required to do this are extremely brief, raising questions about how their auditory cortex encodes and processes such rapid and fine spectrotemporal details. Here, we tested the hypothesis that biosonar target shape representation in the primary auditory cortex (A1) is more reliably encoded by changes in spike timing (latency) than spike rates and that latency is sufficiently precise to support a synchronization-based ensemble representation of this critical auditory object feature space. To test this, we measured how the spatiotemporal activation patterns of A1 changed when naturalistic spectral notches were inserted into echo mimic stimuli. Neurons tuned to notch frequencies were predicted to exhibit longer latencies and lower mean firing rates due to lower signal amplitudes at their preferred frequencies, and both were found to occur. Comparative analyses confirmed that significantly more information was recoverable from changes in spike times relative to concurrent changes in spike rates. With this data, we reconstructed spatiotemporal activation maps of A1 and estimated the level of emerging neuronal spike synchrony between cortical neurons tuned to different frequencies. The results support existing computational models, indicating that spectral interference patterns may be efficiently encoded by a cascading tonotopic sequence of neural synchronization patterns within an ensemble of network activity that relates to the physical features of the reflecting object surface.

show abstract

Section: Discussionmentioning

confidence: 99%

Temporal coding of echo spectral shape in the bat auditory cortex

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…By now this forward-model method has been used in a number of studies, for example to model extracellular spike waveforms (Holt and Koch, 1999; Gold et al, 2006, 2007; Pettersen and Einevoll, 2008; Pettersen et al, 2008; Franke et al, 2010; Schomburg et al, 2012; Thorbergsson et al, 2012; Reimann et al, 2013; Hagen et al, 2015; Ness et al, 2015; Cserpán et al, 2017; Miceli et al, 2017), LFP signals (Pettersen et al, 2008; Lindén et al, 2010, 2011; Gratiy et al, 2011; Makarova et al, 2011; Schomburg et al, 2012; Łęski et al, 2013; Martín-Vázquez et al, 2013, 2015; Reimann et al, 2013; Głąbska et al, 2014, 2016; Mazzoni et al, 2015; Sinha and Narayanan, 2015; Taxidis et al, 2015; Tomsett et al, 2015; Hagen et al, 2016, 2017; Ness et al, 2016, 2018) and recently axonal LFP contributions (McColgan et al, 2017). Some of these used LFPy to predict extracellular potentials (Łęski et al, 2013; Lindén et al, 2014; Hagen et al, 2015, 2016, 2017; Mazzoni et al, 2015; Ness et al, 2015, 2016, 2018; Tomsett et al, 2015; Miceli et al, 2017; Luo et al, 2018), while in Heiberg et al (2016) LFPy was used to construct a small-world LGN network without predictions of extracellular potentials. Further, in Uhlirova et al (2016) LFPy was used to compute neuronal membrane potentials.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0

Hagen

Næss

Ness

et al. 2018

Front. Neuroinform.

Self Cite

121

157

View full text Add to dashboard Cite

Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.

show abstract

“…While there are many examples of detailed biophysical modeling of neural activity improving interpretation of measured intracranial extracellular potentials in lab animals [Einevoll et al, 2007;Blomquist et al, 2009;McColgan et al, 2017;Luo et al, 2018;Chatzikalymniou and Skinner, 2018;Teleńczuk et al, 2019], much less has been done for human EEG/MEG signals. This is natural…”

Section: Discussionmentioning

confidence: 99%

Biophysical modeling of the neural origin of EEG and MEG signals

Næss

Halnes

Hagen

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

AbstractElectroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals.Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details.In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.Graphical abstractHighlightsCurrent dipoles are computed from biophysically detailed simulated neuron and network activityExtracted current dipoles allow for accurate computation of EEG and MEG signals in simplified and detailed head modelsCurrent-diplole approximation generally not suitable for accurate calculations of ECoG signalsMethod provides biophysics-based link between detailed neural activity and systems-level signals

show abstract

Neural timing of stimulus events with microsecond precision

Cited by 24 publications

References 84 publications

Temporal coding of echo spectral shape in the bat auditory cortex

Temporal coding of echo spectral shape in the bat auditory cortex

Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0

Biophysical modeling of the neural origin of EEG and MEG signals

Contact Info

Product

Resources

About