1,064 research outputs found
Stability of Negative Image Equilibria in Spike-Timing Dependent Plasticity
We investigate the stability of negative image equilibria in mean synaptic
weight dynamics governed by spike-timing dependent plasticity (STDP). The
neural architecture of the model is based on the electrosensory lateral line
lobe (ELL) of mormyrid electric fish, which forms a negative image of the
reafferent signal from the fish's own electric discharge to optimize detection
of external electric fields. We derive a necessary and sufficient condition for
stability, for arbitrary postsynaptic potential functions and arbitrary
learning rules. We then apply the general result to several examples of
biological interest.Comment: 13 pages, revtex4; uses packages: graphicx, subfigure; 9 figures, 16
subfigure
Non-equilibrium dynamics of stochastic point processes with refractoriness
Stochastic point processes with refractoriness appear frequently in the
quantitative analysis of physical and biological systems, such as the
generation of action potentials by nerve cells, the release and reuptake of
vesicles at a synapse, and the counting of particles by detector devices. Here
we present an extension of renewal theory to describe ensembles of point
processes with time varying input. This is made possible by a representation in
terms of occupation numbers of two states: Active and refractory. The dynamics
of these occupation numbers follows a distributed delay differential equation.
In particular, our theory enables us to uncover the effect of refractoriness on
the time-dependent rate of an ensemble of encoding point processes in response
to modulation of the input. We present exact solutions that demonstrate generic
features, such as stochastic transients and oscillations in the step response
as well as resonances, phase jumps and frequency doubling in the transfer of
periodic signals. We show that a large class of renewal processes can indeed be
regarded as special cases of the model we analyze. Hence our approach
represents a widely applicable framework to define and analyze non-stationary
renewal processes.Comment: 8 pages, 4 figure
Adaptation Reduces Variability of the Neuronal Population Code
Sequences of events in noise-driven excitable systems with slow variables
often show serial correlations among their intervals of events. Here, we employ
a master equation for general non-renewal processes to calculate the interval
and count statistics of superimposed processes governed by a slow adaptation
variable. For an ensemble of spike-frequency adapting neurons this results in
the regularization of the population activity and an enhanced post-synaptic
signal decoding. We confirm our theoretical results in a population of cortical
neurons.Comment: 4 pages, 2 figure
Adherent carbon film deposition by cathodic arc with implantation
A method of improving the adhesion of carbon thin films deposited using a cathodic vacuum arc by the use of implantation at energies up to 20 keV is described. A detailed analysis of carbon films deposited onto silicon in this way is carried out using complementary techniques of transmission electron microscopy and x-ray photoelectron spectroscopy (XPS) is presented. This analysis shows that an amorphous mixing layer consisting of carbon and silicon is formed between the grown pure carbon film and the crystalline silicon substrate. In the mixing layer, it is shown that some chemical bonding occurs between carbon and silicon. Damage to the underlying crystalline silicon substrate is observed and believed to be caused by interstitial implanted carbon atoms which XPS shows are not bonded to the silicon. The effectiveness of this technique is confirmed by scratch testing and by analysis with scanning electron microscopy which shows failure of the silicon substrate occurs before delamination of the carbon film
A comparative study of different integrate-and-fire neurons: spontaneous activity, dynamical response, and stimulus-induced correlation
Stochastic integrate-and-fire (IF) neuron models have found widespread
applications in computational neuroscience. Here we present results on the
white-noise-driven perfect, leaky, and quadratic IF models, focusing on the
spectral statistics (power spectra, cross spectra, and coherence functions) in
different dynamical regimes (noise-induced and tonic firing regimes with low or
moderate noise). We make the models comparable by tuning parameters such that
the mean value and the coefficient of variation of the interspike interval
match for all of them. We find that, under these conditions, the power spectrum
under white-noise stimulation is often very similar while the response
characteristics, described by the cross spectrum between a fraction of the
input noise and the output spike train, can differ drastically. We also
investigate how the spike trains of two neurons of the same kind (e.g. two
leaky IF neurons) correlate if they share a common noise input. We show that,
depending on the dynamical regime, either two quadratic IF models or two leaky
IFs are more strongly correlated. Our results suggest that, when choosing among
simple IF models for network simulations, the details of the model have a
strong effect on correlation and regularity of the output.Comment: 12 page
Random Walks for Spike-Timing Dependent Plasticity
Random walk methods are used to calculate the moments of negative image
equilibrium distributions in synaptic weight dynamics governed by spike-timing
dependent plasticity (STDP). The neural architecture of the model is based on
the electrosensory lateral line lobe (ELL) of mormyrid electric fish, which
forms a negative image of the reafferent signal from the fish's own electric
discharge to optimize detection of sensory electric fields. Of particular
behavioral importance to the fish is the variance of the equilibrium
postsynaptic potential in the presence of noise, which is determined by the
variance of the equilibrium weight distribution. Recurrence relations are
derived for the moments of the equilibrium weight distribution, for arbitrary
postsynaptic potential functions and arbitrary learning rules. For the case of
homogeneous network parameters, explicit closed form solutions are developed
for the covariances of the synaptic weight and postsynaptic potential
distributions.Comment: 18 pages, 8 figures, 15 subfigures; uses revtex4, subfigure, amsmat
Noise Induced Coherence in Neural Networks
We investigate numerically the dynamics of large networks of globally
pulse-coupled integrate and fire neurons in a noise-induced synchronized state.
The powerspectrum of an individual element within the network is shown to
exhibit in the thermodynamic limit () a broadband peak and an
additional delta-function peak that is absent from the powerspectrum of an
isolated element. The powerspectrum of the mean output signal only exhibits the
delta-function peak. These results are explained analytically in an exactly
soluble oscillator model with global phase coupling.Comment: 4 pages ReVTeX and 3 postscript figure
Dynamical response of the Hodgkin-Huxley model in the high-input regime
The response of the Hodgkin-Huxley neuronal model subjected to stochastic
uncorrelated spike trains originating from a large number of inhibitory and
excitatory post-synaptic potentials is analyzed in detail. The model is
examined in its three fundamental dynamical regimes: silence, bistability and
repetitive firing. Its response is characterized in terms of statistical
indicators (interspike-interval distributions and their first moments) as well
as of dynamical indicators (autocorrelation functions and conditional
entropies). In the silent regime, the coexistence of two different coherence
resonances is revealed: one occurs at quite low noise and is related to the
stimulation of subthreshold oscillations around the rest state; the second one
(at intermediate noise variance) is associated with the regularization of the
sequence of spikes emitted by the neuron. Bistability in the low noise limit
can be interpreted in terms of jumping processes across barriers activated by
stochastic fluctuations. In the repetitive firing regime a maximization of
incoherence is observed at finite noise variance. Finally, the mechanisms
responsible for spike triggering in the various regimes are clearly identified.Comment: 14 pages, 24 figures in eps, submitted to Physical Review
Population coding by globally coupled phase oscillators
A system of globally coupled phase oscillators subject to an external input
is considered as a simple model of neural circuits coding external stimulus.
The information coding efficiency of the system in its asynchronous state is
quantified using Fisher information. The effect of coupling and noise on the
information coding efficiency in the stationary state is analyzed. The
relaxation process of the system after the presentation of an external input is
also studied. It is found that the information coding efficiency exhibits a
large transient increase before the system relaxes to the final stationary
state.Comment: 7 pages, 9 figures, revised version, new figures added, to appear in
JPSJ Vol 75, No.
Pulse propagation in discrete excitatory networks of integrate-and-fire neurons
We study the propagation of solitary waves in a discrete excitatory network of integrate-and-fire neurons. We show the existence and the stability of a fast wave and a family of slow waves. Fast waves are similar to those already described in continuum networks. Stable slow waves have not been previously reported in purely excitatory networks and their propagation is particular to the discrete nature of the network. The robustness of our results is studied in the presence of noise
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