1,019 research outputs found
Possible geopotential improvement from satellite altimetry
Possible geopotential improvement from satellite altimetr
Spontaneous structure formation in a network of chaotic units with variable connection strengths
As a model of temporally evolving networks, we consider a globally coupled
logistic map with variable connection weights. The model exhibits
self-organization of network structure, reflected by the collective behavior of
units. Structural order emerges even without any inter-unit synchronization of
dynamics. Within this structure, units spontaneously separate into two groups
whose distinguishing feature is that the first group possesses many
outwardly-directed connections to the second group, while the second group
possesses only few outwardly-directed connections to the first. The relevance
of the results to structure formation in neural networks is briefly discussed.Comment: 4 pages, 3 figures, REVTe
A Heterosynaptic Learning Rule for Neural Networks
In this article we intoduce a novel stochastic Hebb-like learning rule for
neural networks that is neurobiologically motivated. This learning rule
combines features of unsupervised (Hebbian) and supervised (reinforcement)
learning and is stochastic with respect to the selection of the time points
when a synapse is modified. Moreover, the learning rule does not only affect
the synapse between pre- and postsynaptic neuron, which is called homosynaptic
plasticity, but effects also further remote synapses of the pre- and
postsynaptic neuron. This more complex form of synaptic plasticity has recently
come under investigations in neurobiology and is called heterosynaptic
plasticity. We demonstrate that this learning rule is useful in training neural
networks by learning parity functions including the exclusive-or (XOR) mapping
in a multilayer feed-forward network. We find, that our stochastic learning
rule works well, even in the presence of noise. Importantly, the mean learning
time increases with the number of patterns to be learned polynomially,
indicating efficient learning.Comment: 19 page
About the ergodic regime in the analogical Hopfield neural networks. Moments of the partition function
In this paper we introduce and exploit the real replica approach for a
minimal generalization of the Hopfield model, by assuming the learned patterns
to be distributed accordingly to a standard unit Gaussian. We consider the high
storage case, when the number of patterns is linearly diverging with the number
of neurons. We study the infinite volume behavior of the normalized momenta of
the partition function. We find a region in the parameter space where the free
energy density in the infinite volume limit is self-averaging around its
annealed approximation, as well as the entropy and the internal energy density.
Moreover, we evaluate the corrections to their extensive counterparts with
respect to their annealed expressions. The fluctuations of properly introduced
overlaps, which act as order parameters, are also discussed.Comment: 15 page
Learning by message-passing in networks of discrete synapses
We show that a message-passing process allows to store in binary "material"
synapses a number of random patterns which almost saturates the information
theoretic bounds. We apply the learning algorithm to networks characterized by
a wide range of different connection topologies and of size comparable with
that of biological systems (e.g. ). The algorithm can be
turned into an on-line --fault tolerant-- learning protocol of potential
interest in modeling aspects of synaptic plasticity and in building
neuromorphic devices.Comment: 4 pages, 3 figures; references updated and minor corrections;
accepted in PR
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
How glassy are neural networks?
In this paper we continue our investigation on the high storage regime of a
neural network with Gaussian patterns. Through an exact mapping between its
partition function and one of a bipartite spin glass (whose parties consist of
Ising and Gaussian spins respectively), we give a complete control of the whole
annealed region. The strategy explored is based on an interpolation between the
bipartite system and two independent spin glasses built respectively by
dichotomic and Gaussian spins: Critical line, behavior of the principal
thermodynamic observables and their fluctuations as well as overlap
fluctuations are obtained and discussed. Then, we move further, extending such
an equivalence beyond the critical line, to explore the broken ergodicity phase
under the assumption of replica symmetry and we show that the quenched free
energy of this (analogical) Hopfield model can be described as a linear
combination of the two quenched spin-glass free energies even in the replica
symmetric framework
Analysis of Oscillator Neural Networks for Sparsely Coded Phase Patterns
We study a simple extended model of oscillator neural networks capable of
storing sparsely coded phase patterns, in which information is encoded both in
the mean firing rate and in the timing of spikes. Applying the methods of
statistical neurodynamics to our model, we theoretically investigate the
model's associative memory capability by evaluating its maximum storage
capacities and deriving its basins of attraction. It is shown that, as in the
Hopfield model, the storage capacity diverges as the activity level decreases.
We consider various practically and theoretically important cases. For example,
it is revealed that a dynamically adjusted threshold mechanism enhances the
retrieval ability of the associative memory. It is also found that, under
suitable conditions, the network can recall patterns even in the case that
patterns with different activity levels are stored at the same time. In
addition, we examine the robustness with respect to damage of the synaptic
connections. The validity of these theoretical results is confirmed by
reasonable agreement with numerical simulations.Comment: 23 pages, 11 figure
Analogue neural networks on correlated random graphs
We consider a generalization of the Hopfield model, where the entries of
patterns are Gaussian and diluted. We focus on the high-storage regime and we
investigate analytically the topological properties of the emergent network, as
well as the thermodynamic properties of the model. We find that, by properly
tuning the dilution in the pattern entries, the network can recover different
topological regimes characterized by peculiar scalings of the average
coordination number with respect to the system size. The structure is also
shown to exhibit a large degree of cliquishness, even when very sparse.
Moreover, we obtain explicitly the replica symmetric free energy and the
self-consistency equations for the overlaps (order parameters of the theory),
which turn out to be classical weighted sums of 'sub-overlaps' defined on all
possible sub-graphs. Finally, a study of criticality is performed through a
small-overlap expansion of the self-consistencies and through a whole
fluctuation theory developed for their rescaled correlations: Both approaches
show that the net effect of dilution in pattern entries is to rescale the
critical noise level at which ergodicity breaks down.Comment: 34 pages, 3 figure
The TRAPPIST survey of southern transiting planets. I. Thirty eclipses of the ultra-short period planet WASP-43 b
We present twenty-three transit light curves and seven occultation light
curves for the ultra-short period planet WASP-43 b, in addition to eight new
measurements of the radial velocity of the star. Thanks to this extensive data
set, we improve significantly the parameters of the system. Notably, the
largely improved precision on the stellar density (2.41+-0.08 rho_sun) combined
with constraining the age to be younger than a Hubble time allows us to break
the degeneracy of the stellar solution mentioned in the discovery paper. The
resulting stellar mass and size are 0.717+-0.025 M_sun and 0.667+-0.011 R_sun.
Our deduced physical parameters for the planet are 2.034+-0.052 M_jup and
1.036+-0.019 R_jup. Taking into account its level of irradiation, the high
density of the planet favors an old age and a massive core. Our deduced orbital
eccentricity, 0.0035(-0.0025,+0.0060), is consistent with a fully circularized
orbit. We detect the emission of the planet at 2.09 microns at better than
11-sigma, the deduced occultation depth being 1560+-140 ppm. Our detection of
the occultation at 1.19 microns is marginal (790+-320 ppm) and more
observations are needed to confirm it. We place a 3-sigma upper limit of 850
ppm on the depth of the occultation at ~0.9 microns. Together, these results
strongly favor a poor redistribution of the heat to the night-side of the
planet, and marginally favor a model with no day-side temperature inversion.Comment: 14 pages, 6 tables, 11 figures. Accepted for publication in A&
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