922 research outputs found
Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure
Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law
distribution of population event sizes with an exponent of -3/2. It has been
observed in the superficial layers of cortex both \emph{in vivo} and \emph{in
vitro}. In this paper we analyze the information transmission of a novel
self-organized neural network with active-neuron-dominant structure. Neuronal
avalanches can be observed in this network with appropriate input intensity. We
find that the process of network learning via spike-timing dependent plasticity
dramatically increases the complexity of network structure, which is finally
self-organized to be active-neuron-dominant connectivity. Both the entropy of
activity patterns and the complexity of their resulting post-synaptic inputs
are maximized when the network dynamics are propagated as neuronal avalanches.
This emergent topology is beneficial for information transmission with high
efficiency and also could be responsible for the large information capacity of
this network compared with alternative archetypal networks with different
neural connectivity.Comment: Non-final version submitted to Chao
Model of Low-pass Filtering of Local Field Potentials in Brain Tissue
Local field potentials (LFPs) are routinely measured experimentally in brain
tissue, and exhibit strong low-pass frequency filtering properties, with high
frequencies (such as action potentials) being visible only at very short
distances (10~) from the recording electrode. Understanding
this filtering is crucial to relate LFP signals with neuronal activity, but not
much is known about the exact mechanisms underlying this low-pass filtering. In
this paper, we investigate a possible biophysical mechanism for the low-pass
filtering properties of LFPs. We investigate the propagation of electric fields
and its frequency dependence close to the current source, i.e. at length scales
in the order of average interneuronal distance. We take into account the
presence of a high density of cellular membranes around current sources, such
as glial cells. By considering them as passive cells, we show that under the
influence of the electric source field, they respond by polarisation, i.e.,
creation of an induced field. Because of the finite velocity of ionic charge
movement, this polarization will not be instantaneous. Consequently, the
induced electric field will be frequency-dependent, and much reduced for high
frequencies. Our model establishes that with respect to frequency attenuation
properties, this situation is analogous to an equivalent RC-circuit, or better
a system of coupled RC-circuits. We present a number of numerical simulations
of induced electric field for biologically realistic values of parameters, and
show this frequency filtering effect as well as the attenuation of
extracellular potentials with distance. We suggest that induced electric fields
in passive cells surrounding neurons is the physical origin of frequency
filtering properties of LFPs.Comment: 10 figs, revised tex file and revised fig
Does the 1/f frequency-scaling of brain signals reflect self-organized critical states?
Many complex systems display self-organized critical states characterized by
1/f frequency scaling of power spectra. Global variables such as the
electroencephalogram, scale as 1/f, which could be the sign of self-organized
critical states in neuronal activity. By analyzing simultaneous recordings of
global and neuronal activities, we confirm the 1/f scaling of global variables
for selected frequency bands, but show that neuronal activity is not consistent
with critical states. We propose a model of 1/f scaling which does not rely on
critical states, and which is testable experimentally.Comment: 3 figures, 6 page
Corticothalamic projections control synchronization in locally coupled bistable thalamic oscillators
Thalamic circuits are able to generate state-dependent oscillations of
different frequencies and degrees of synchronization. However, only little is
known how synchronous oscillations, like spindle oscillations in the thalamus,
are organized in the intact brain. Experimental findings suggest that the
simultaneous occurrence of spindle oscillations over widespread territories of
the thalamus is due to the corticothalamic projections, as the synchrony is
lost in the decorticated thalamus. Here we study the influence of
corticothalamic projections on the synchrony in a thalamic network, and uncover
the underlying control mechanism, leading to a control method which is
applicable in wide range of stochastic driven excitable units.Comment: 4 pages with 4 figures (Color online on p.3-4) include
The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity
We study pairwise Ising models for describing the statistics of multi-neuron
spike trains, using data from a simulated cortical network. We explore
efficient ways of finding the optimal couplings in these models and examine
their statistical properties. To do this, we extract the optimal couplings for
subsets of size up to 200 neurons, essentially exactly, using Boltzmann
learning. We then study the quality of several approximate methods for finding
the couplings by comparing their results with those found from Boltzmann
learning. Two of these methods- inversion of the TAP equations and an
approximation proposed by Sessak and Monasson- are remarkably accurate. Using
these approximations for larger subsets of neurons, we find that extracting
couplings using data from a subset smaller than the full network tends
systematically to overestimate their magnitude. This effect is described
qualitatively by infinite-range spin glass theory for the normal phase. We also
show that a globally-correlated input to the neurons in the network lead to a
small increase in the average coupling. However, the pair-to-pair variation of
the couplings is much larger than this and reflects intrinsic properties of the
network. Finally, we study the quality of these models by comparing their
entropies with that of the data. We find that they perform well for small
subsets of the neurons in the network, but the fit quality starts to
deteriorate as the subset size grows, signalling the need to include higher
order correlations to describe the statistics of large networks.Comment: 12 pages, 10 figure
Action Potential Initiation in the Hodgkin-Huxley Model
A recent paper of B. Naundorf et al. described an intriguing negative correlation between variability of the onset potential at which an action potential occurs (the onset span) and the rapidity of action potential initiation (the onset rapidity). This correlation was demonstrated in numerical simulations of the Hodgkin-Huxley model. Due to this antagonism, it is argued that Hodgkin-Huxley-type models are unable to explain action potential initiation observed in cortical neurons in vivo or in vitro. Here we apply a method from theoretical physics to derive an analytical characterization of this problem. We analytically compute the probability distribution of onset potentials and analytically derive the inverse relationship between onset span and onset rapidity. We find that the relationship between onset span and onset rapidity depends on the level of synaptic background activity. Hence we are able to elucidate the regions of parameter space for which the Hodgkin-Huxley model is able to accurately describe the behavior of this system
Extracting synaptic conductances from single membrane potential traces
In awake animals, the activity of the cerebral cortex is highly complex, with
neurons firing irregularly with apparent Poisson statistics. One way to
characterize this complexity is to take advantage of the high interconnectivity
of cerebral cortex and use intracellular recordings of cortical neurons, which
contain information about the activity of thousands of other cortical neurons.
Identifying the membrane potential (Vm) to a stochastic process enables the
extraction of important statistical signatures of this complex synaptic
activity. Typically, one estimates the total synaptic conductances (excitatory
and inhibitory) but this type of estimation requires at least two Vm levels and
therefore cannot be applied to single Vm traces. We propose here a method to
extract excitatory and inhibitory conductances (mean and variance) from single
Vm traces. This "VmT method" estimates conductance parameters using maximum
likelihood criteria, under the assumption are that synaptic conductances are
described by Gaussian stochastic processes and are integrated by a passive
leaky membrane. The method is illustrated using models and is tested on
guinea-pig visual cortex neurons in vitro using dynamic-clamp experiments. The
VmT method holds promises for extracting conductances from single-trial
measurements, which has a high potential for in vivo applications.Comment: Neuroscience (in press
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