411 research outputs found
Dynamic Moment Analysis of the Extracellular Electric Field of a Biologically Realistic Spiking Neuron
Based upon the membrane currents generated by an action potential in a
biologically realistic model of a pyramidal, hippocampal cell within rat CA1,
we perform a moment expansion of the extracellular field potential. We
decompose the potential into both inverse and classical moments and show that
this method is a rapid and efficient way to calculate the extracellular field
both near and far from the cell body. The action potential gives rise to a
large quadrupole moment that contributes to the extracellular field up to
distances of almost 1 cm. This method will serve as a starting point in
connecting the microscopic generation of electric fields at the level of
neurons to macroscopic observables such as the local field potential
Computational study of resting state network dynamics
Lo scopo di questa tesi è quello di mostrare, attraverso una simulazione con il software The Virtual Brain, le più importanti proprietà della dinamica cerebrale durante il resting state, ovvero quando non si è coinvolti in nessun compito preciso e non si è sottoposti a nessuno stimolo particolare. Si comincia con lo spiegare cos’è il resting state attraverso una breve revisione storica della sua scoperta, quindi si passano in rassegna alcuni metodi sperimentali utilizzati nell’analisi dell’attività cerebrale, per poi evidenziare la differenza tra connettività strutturale e funzionale. In seguito, si riassumono brevemente i concetti dei sistemi dinamici, teoria indispensabile per capire un sistema complesso come il cervello. Nel capitolo successivo, attraverso un approccio ‘bottom-up’, si illustrano sotto il profilo biologico le principali strutture del sistema nervoso, dal neurone alla corteccia cerebrale. Tutto ciò viene spiegato anche dal punto di vista dei sistemi dinamici, illustrando il pionieristico modello di Hodgkin-Huxley e poi il concetto di dinamica di popolazione. Dopo questa prima parte preliminare si entra nel dettaglio della simulazione. Prima di tutto si danno maggiori informazioni sul software The Virtual Brain, si definisce il modello di network del resting state utilizzato nella simulazione e si descrive il ‘connettoma’ adoperato. Successivamente vengono mostrati i risultati dell’analisi svolta sui dati ricavati, dai quali si mostra come la criticità e il rumore svolgano un ruolo chiave nell'emergenza di questa attività di fondo del cervello. Questi risultati vengono poi confrontati con le più importanti e recenti ricerche in questo ambito, le quali confermano i risultati del nostro lavoro. Infine, si riportano brevemente le conseguenze che porterebbe in campo medico e clinico una piena comprensione del fenomeno del resting state e la possibilità di virtualizzare l’attività cerebrale
Computational Characterization of the Cellular Origins of Electroencephalography
Electroencephalography (EEG) is a non-invasive technique used to measure brain activity. Despite its near ubiquitous presence in neuroscience, very little research has gone into connecting the electrical potentials it measures on the scalp to the underlying network activity which generates those signals. This results in most EEG analyses being more macroscopically focused (e.g. coherence and correlation analyses). Despite the many uses of macroscopically focuses analyses, limiting research to only these analyses neglects the insights which can be gained from studying network and microcircuit architecture. The ability to study these things through non-invasive techniques like EEG depends upon the ability to understand how the activity of individual neurons affect the electrical potentials recorded by EEG electrodes on the scalp. The research presented here is designed to take the first steps towards providing that link.Current dipole moments generated by multiple multi-compartment, morphologically accurate, three-dimensional neuron models were characterized into a single time series called a dipole response function (DRF). We found that when the soma of a neuron is directly stimulated to threshold, the resulting action potential caused an excess of current which backpropagated up the dendritic tree activating voltage gated ion channels along the way. This backpropigation created a dipole which had a magnitude and duration greater than the current dipoles created by neurons that were synaptically activated to near threshold. Additionally, we presented a novel technique, where, through the combination of the DRFs with point source network activity via convolution, dipoles generated by populations of neurons can be simulated. We validated this technique at multiple spatial scales using data from both animal models and human subjects. Our results show that this technique can provide a reasonable representation of the extracellular fields and EEG signals generated in their physiological counterparts. Finally, analysis of a simulated evoked potential generated via the convolutional methodology proposed showed that ∼ 98% of the variability of simulated signal could be accounted for by the dipoles originating from DRFs of spiking pyramidal cells
Electric and magnetic fields inside neurons and their impact upon the cytoskeletal microtubules
If we want to better understand how the microtubules can translate and input the information carried by the electrophysiologic impulses that enter the brain cortex, a detailed investigation of the local electromagnetic field structure is needed. In this paper are assessed the electric and the magnetic field strengths in different neuronal compartments. The calculated results are verified via experimental data comparison. It is shown that the magnetic field is too weak to input information to microtubules and no Hall effect, respectively QHE is realistic. Local magnetic flux density is less than 1/300 of the Earth’s magnetic field that’s why any magnetic signal will be suffocated by the surrounding noise. In contrast the electric field carries biologically important information and acts upon voltage-gated transmembrane ion channels that control the neuronal action potential. If mind is linked to subneuronal processing of information in the brain microtubules then microtubule interaction with the local electric field, as input source of information is crucial. The intensity of the electric field is estimated to be 10V/m inside the neuronal cytoplasm however the details of the tubulin-electric field interaction are still unknown. A novel hypothesis stressing on the tubulin C-termini intraneuronal function is presented replacing the current flawed models (Tuszynski 2003, Mershin 2003, Hameroff 2003, Porter 2003) presented at the Quantum Mind II Conference held at Tucson, Arizona, 15-19 March 2003, that are shown in this presentation to be biologically and physically inconsistent
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
Chronic and acute implants of multi-electrode arrays that cover several
mm of neural tissue provide simultaneous access to population signals like
extracellular potentials and the spiking activity of 100 or more individual
neurons. While the recorded data may uncover principles of brain function, its
interpretation calls for multiscale computational models with corresponding
spatial dimensions and signal predictions. Such models can facilitate the
search of mechanisms underlying observed spatiotemporal activity patterns in
cortex. Multi-layer spiking neuron network models of local cortical circuits
covering ~1 mm have been developed, integrating experimentally obtained
neuron-type specific connectivity data and reproducing features of in-vivo
spiking statistics. With forward models, local field potentials (LFPs) can be
computed from the simulated spiking activity. To account for the spatial scale
of common neural recordings, we extend a local network and LFP model to 4x4
mm. The upscaling preserves the neuron densities, and introduces
distance-dependent connection probabilities and delays. As detailed
experimental connectivity data is partially lacking, we address this
uncertainty in model parameters by testing parameter combinations within
biologically plausible bounds. Based on model predictions of spiking activity
and LFPs, we find that the upscaling procedure preserves the overall spiking
statistics of the original model and reproduces asynchronous irregular spiking
across populations and weak pairwise spike-train correlations observed in
sensory cortex. In contrast with the weak spike-train correlations, the
correlation of LFP signals is strong and distance-dependent, compatible with
experimental observations. Enhanced spatial coherence in the low-gamma band may
explain the recent experimental report of an apparent band-pass filter effect
in the spatial reach of the LFP.Comment: 44 pages, 9 figures, 5 table
Biophysically grounded mean-field models of neural populations under electrical stimulation
Electrical stimulation of neural systems is a key tool for understanding
neural dynamics and ultimately for developing clinical treatments. Many
applications of electrical stimulation affect large populations of neurons.
However, computational models of large networks of spiking neurons are
inherently hard to simulate and analyze. We evaluate a reduced mean-field model
of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx)
neurons which can be used to efficiently study the effects of electrical
stimulation on large neural populations. The rich dynamical properties of this
basic cortical model are described in detail and validated using large network
simulations. Bifurcation diagrams reflecting the network's state reveal
asynchronous up- and down-states, bistable regimes, and oscillatory regions
corresponding to fast excitation-inhibition and slow excitation-adaptation
feedback loops. The biophysical parameters of the AdEx neuron can be coupled to
an electric field with realistic field strengths which then can be propagated
up to the population description.We show how on the edge of bifurcation, direct
electrical inputs cause network state transitions, such as turning on and off
oscillations of the population rate. Oscillatory input can frequency-entrain
and phase-lock endogenous oscillations. Relatively weak electric field
strengths on the order of 1 V/m are able to produce these effects, indicating
that field effects are strongly amplified in the network. The effects of
time-varying external stimulation are well-predicted by the mean-field model,
further underpinning the utility of low-dimensional neural mass models.Comment: A Python package with an implementation of the AdEx mean-field model
can be found at https://github.com/neurolib-dev/neurolib - code for
simulation and data analysis can be found at
https://github.com/caglarcakan/stimulus_neural_population
Modeling biophysical and neural circuit bases for core cognitive abilities evident in neuroimaging patterns: hippocampal mismatch, mismatch negativity, repetition positivity, and alpha suppression of distractors
This dissertation develops computational models to address outstanding problems in the domain of expectation-related cognitive processes and their neuroimaging markers in functional MRI or EEG. The new models reveal a way to unite diverse phenomena within a common framework focused on dynamic neural encoding shifts, which can arise from robust interactive effects of M-currents and chloride currents in pyramidal neurons. By specifying efficient, biologically realistic circuits that achieve predictive coding (e.g., Friston, 2005), these models bridge among neuronal biophysics, systems neuroscience, and theories of cognition.
Chapter one surveys data types and neural processes to be examined, and outlines the Dynamically Labeled Predictive Coding (DLPC) framework developed during the research. Chapter two models hippocampal prediction and mismatch, using the DLPC framework. Chapter three presents extensions to the model that allow its application for modeling neocortical EEG genesis. Simulations of this extended model illustrate how dynamic encoding shifts can produce Mismatch Negativity (MMN) phenomena, including pharmacological effects on MMN reported for humans or animals.
Chapters four and five describe new modeling studies of possible neural bases for alpha-induced information suppression, a phenomenon associated with active ignoring of stimuli. Two models explore the hypothesis that in simple rate-based circuits, information suppression might be a robust effect of neural saturation states arising near peaks of resonant alpha oscillations. A new proposal is also introduced for how the basal ganglia may control onset and offset of alpha-induced information suppression. Although these rate models could reproduce many experimental findings, they fell short of reproducing a key electrophysiological finding: phase-dependent reduction in spiking activity correlated with power in the alpha frequency band.
Therefore, chapter five also specifies how a DLPC model, adapted from the neocortical model developed in chapter three, can provide an expectation-based model of alpha-induced information suppression that exhibits phase-dependent spike reduction during alpha-band oscillations. The model thus can explain experimental findings that were not reproduced by the rate models. The final chapter summarizes main theses, results, and basic research implications, then suggests future directions, including expanded models of neocortical mismatch, applications to artificial neural networks, and the introduction of reward circuitry
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