3,926 research outputs found
A statistical model for in vivo neuronal dynamics
Single neuron models have a long tradition in computational neuroscience.
Detailed biophysical models such as the Hodgkin-Huxley model as well as
simplified neuron models such as the class of integrate-and-fire models relate
the input current to the membrane potential of the neuron. Those types of
models have been extensively fitted to in vitro data where the input current is
controlled. Those models are however of little use when it comes to
characterize intracellular in vivo recordings since the input to the neuron is
not known. Here we propose a novel single neuron model that characterizes the
statistical properties of in vivo recordings. More specifically, we propose a
stochastic process where the subthreshold membrane potential follows a Gaussian
process and the spike emission intensity depends nonlinearly on the membrane
potential as well as the spiking history. We first show that the model has a
rich dynamical repertoire since it can capture arbitrary subthreshold
autocovariance functions, firing-rate adaptations as well as arbitrary shapes
of the action potential. We then show that this model can be efficiently fitted
to data without overfitting. Finally, we show that this model can be used to
characterize and therefore precisely compare various intracellular in vivo
recordings from different animals and experimental conditions.Comment: 31 pages, 10 figure
Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation
Cortical neurons are subject to sustained and irregular synaptic activity
which causes important fluctuations of the membrane potential (Vm). We review
here different methods to characterize this activity and its impact on spike
generation. The simplified, fluctuating point-conductance model of synaptic
activity provides the starting point of a variety of methods for the analysis
of intracellular Vm recordings. In this model, the synaptic excitatory and
inhibitory conductances are described by Gaussian-distributed stochastic
variables, or colored conductance noise. The matching of experimentally
recorded Vm distributions to an invertible theoretical expression derived from
the model allows the extraction of parameters characterizing the synaptic
conductance distributions. This analysis can be complemented by the matching of
experimental Vm power spectral densities (PSDs) to a theoretical template, even
though the unexpected scaling properties of experimental PSDs limit the
precision of this latter approach. Building on this stochastic characterization
of synaptic activity, we also propose methods to qualitatively and
quantitatively evaluate spike-triggered averages of synaptic time-courses
preceding spikes. This analysis points to an essential role for synaptic
conductance variance in determining spike times. The presented methods are
evaluated using controlled conductance injection in cortical neurons in vitro
with the dynamic-clamp technique. We review their applications to the analysis
of in vivo intracellular recordings in cat association cortex, which suggest a
predominant role for inhibition in determining both sub- and supra-threshold
dynamics of cortical neurons embedded in active networks.Comment: 9 figures, Journal of Neuroscience Methods (in press, 2008
A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons
We present a biophysical approach for the coupling of neural network activity
as resulting from proper dipole currents of cortical pyramidal neurons to the
electric field in extracellular fluid. Starting from a reduced threecompartment
model of a single pyramidal neuron, we derive an observation model for
dendritic dipole currents in extracellular space and thereby for the dendritic
field potential that contributes to the local field potential of a neural
population. This work aligns and satisfies the widespread dipole assumption
that is motivated by the "open-field" configuration of the dendritic field
potential around cortical pyramidal cells. Our reduced three-compartment scheme
allows to derive networks of leaky integrate-and-fire models, which facilitates
comparison with existing neural network and observation models. In particular,
by means of numerical simulations we compare our approach with an ad hoc model
by Mazzoni et al. [Mazzoni, A., S. Panzeri, N. K. Logothetis, and N. Brunel
(2008). Encoding of naturalistic stimuli by local field potential spectra in
networks of excitatory and inhibitory neurons. PLoS Computational Biology 4
(12), e1000239], and conclude that our biophysically motivated approach yields
substantial improvement.Comment: 31 pages, 4 figure
SIMONE: a realistic neural network simulator to reproduce MEA-based recordings
International audienceContemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy
Parameter estimation of neuron models using <i>in-vitro </i>and<i> in-vivo </i>electrophysiological data
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model
Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): Comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
Local field potentials (LFPs) sampled with extracellular electrodes are
frequently used as a measure of population neuronal activity. However, relating
such measurements to underlying neuronal behaviour and connectivity is
non-trivial. To help study this link, we developed the Virtual Electrode
Recording Tool for EXtracellular potentials (VERTEX). We first identified a
reduced neuron model that retained the spatial and frequency filtering
characteristics of extracellular potentials from neocortical neurons. We then
developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large
populations (>100 000 neurons). A VERTEX-based simulation successfully
reproduced features of the LFPs from an in vitro multi-electrode array
recording of macaque neocortical tissue. Our model, with virtual electrodes
placed anywhere in 3D, allows direct comparisons with the in vitro recording
setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and
computational neuroscientists to use models to understand the mechanisms
underlying measured brain dynamics in health and disease.Comment: appears in Brain Struct Funct 201
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process
Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein–Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein–Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential
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