1,403 research outputs found
STUDIES OF THE MECHANISM OF ACTION OF COBAMIDE COENZYMES
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73404/1/j.1749-6632.1964.tb45046.x.pd
Dynamical mean-filed approximation to small-world networks of spiking neurons: From local to global, and/or from regular to random couplings
By extending a dynamical mean-field approximation (DMA) previously proposed
by the author [H. Hasegawa, Phys. Rev. E {\bf 67}, 41903 (2003)], we have
developed a semianalytical theory which takes into account a wide range of
couplings in a small-world network. Our network consists of noisy -unit
FitzHugh-Nagumo (FN) neurons with couplings whose average coordination number
may change from local () to global couplings () and/or
whose concentration of random couplings is allowed to vary from regular
() to completely random (p=1). We have taken into account three kinds of
spatial correlations: the on-site correlation, the correlation for a coupled
pair and that for a pair without direct couplings. The original -dimensional {\it stochastic} differential equations are transformed to
13-dimensional {\it deterministic} differential equations expressed in terms of
means, variances and covariances of state variables. The synchronization ratio
and the firing-time precision for an applied single spike have been discussed
as functions of and . Our calculations have shown that with increasing
, the synchronization is {\it worse} because of increased heterogeneous
couplings, although the average network distance becomes shorter. Results
calculated by out theory are in good agreement with those by direct
simulations.Comment: 19 pages, 2 figures: accepted in Phys. Rev. E with minor change
Affinity chromatography of N5-methyltetrahydropfolate-homocysteine methyltransferase on a cobalamin-sepharose
Nonlocal mechanism for cluster synchronization in neural circuits
The interplay between the topology of cortical circuits and synchronized
activity modes in distinct cortical areas is a key enigma in neuroscience. We
present a new nonlocal mechanism governing the periodic activity mode: the
greatest common divisor (GCD) of network loops. For a stimulus to one node, the
network splits into GCD-clusters in which cluster neurons are in zero-lag
synchronization. For complex external stimuli, the number of clusters can be
any common divisor. The synchronized mode and the transients to synchronization
pinpoint the type of external stimuli. The findings, supported by an
information mixing argument and simulations of Hodgkin Huxley population
dynamic networks with unidirectional connectivity and synaptic noise, call for
reexamining sources of correlated activity in cortex and shorter information
processing time scales.Comment: 8 pges, 6 figure
Theory of Interaction of Memory Patterns in Layered Associative Networks
A synfire chain is a network that can generate repeated spike patterns with
millisecond precision. Although synfire chains with only one activity
propagation mode have been intensively analyzed with several neuron models,
those with several stable propagation modes have not been thoroughly
investigated. By using the leaky integrate-and-fire neuron model, we
constructed a layered associative network embedded with memory patterns. We
analyzed the network dynamics with the Fokker-Planck equation. First, we
addressed the stability of one memory pattern as a propagating spike volley. We
showed that memory patterns propagate as pulse packets. Second, we investigated
the activity when we activated two different memory patterns. Simultaneous
activation of two memory patterns with the same strength led the propagating
pattern to a mixed state. In contrast, when the activations had different
strengths, the pulse packet converged to a two-peak state. Finally, we studied
the effect of the preceding pulse packet on the following pulse packet. The
following pulse packet was modified from its original activated memory pattern,
and it converged to a two-peak state, mixed state or non-spike state depending
on the time interval
Exact solution of a linear molecular motor model driven by two-step fluctuations and subject to protein friction
We investigate by analytical means the stochastic equations of motion of a
linear molecular motor model based on the concept of protein friction. Solving
the coupled Langevin equations originally proposed by Mogilner et al. (A.
Mogilner et al., Phys. Lett. {\bf 237}, 297 (1998)), and averaging over both
the two-step internal conformational fluctuations and the thermal noise, we
present explicit, analytical expressions for the average motion and the
velocity-force relationship. Our results allow for a direct interpretation of
details of this motor model which are not readily accessible from numerical
solutions. In particular, we find that the model is able to predict
physiologically reasonable values for the load-free motor velocity and the
motor mobility.Comment: 12 pages revtex, 6 eps-figure
Supermode suppression to below-130 dBc/Hz in a 10 GHz harmonically mode-locked external sigma cavity semiconductor laser
We demonstrate supermode suppression to levels below - 125 dBc/Hz and - 132 dBc/Hz using Fabry-Perot etalons with finesse values of 180 and 650, respectively, for a 10 GHz harmonically mode-locked external sigma cavity semiconductor laser. The laser was hybridly mode-locked using direct electrical modulation in a compact package without the need for an external modulator
Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons
A synfire chain is a simple neural network model which can propagate stable
synchronous spikes called a pulse packet and widely researched. However how
synfire chains coexist in one network remains to be elucidated. We have studied
the activity of a layered associative network of Leaky Integrate-and-Fire
neurons in which connection we embed memory patterns by the Hebbian Learning.
We analyzed their activity by the Fokker-Planck method. In our previous report,
when a half of neurons belongs to each memory pattern (memory pattern rate
), the temporal profiles of the network activity is split into
temporally clustered groups called sublattices under certain input conditions.
In this study, we show that when the network is sparsely connected (),
synchronous firings of the memory pattern are promoted. On the contrary, the
densely connected network () inhibit synchronous firings. The sparseness
and denseness also effect the basin of attraction and the storage capacity of
the embedded memory patterns. We show that the sparsely(densely) connected
networks enlarge(shrink) the basion of attraction and increase(decrease) the
storage capacity
Nonlinear Impurity Modes in Homogeneous and Periodic Media
We analyze the existence and stability of nonlinear localized waves described
by the Kronig-Penney model with a nonlinear impurity. We study the properties
of such waves in a homogeneous medium, and then analyze new effects introduced
by periodicity of the medium parameters. In particular, we demonstrate the
existence of a novel type of stable nonlinear band-gap localized states, and
also reveal an important physical mechanism of the oscillatory wave
instabilities associated with the band-gap wave resonances.Comment: 11 pages, 3 figures; To be published in: Proceedings of the NATO
Advanced Research Workshop "Nonlinearity and Disorder: Theory and
Applications" (Tashkent, 2-6 Oct, 2000) Editors: P.L. Christiansen and F.K.
Abdullaev (Kluwer, 2001
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