1,871 research outputs found
Inferring collective dynamical states from widely unobserved systems
When assessing spatially-extended complex systems, one can rarely sample the
states of all components. We show that this spatial subsampling typically leads
to severe underestimation of the risk of instability in systems with
propagating events. We derive a subsampling-invariant estimator, and
demonstrate that it correctly infers the infectiousness of various diseases
under subsampling, making it particularly useful in countries with unreliable
case reports. In neuroscience, recordings are strongly limited by subsampling.
Here, the subsampling-invariant estimator allows to revisit two prominent
hypotheses about the brain's collective spiking dynamics:
asynchronous-irregular or critical. We identify consistently for rat, cat and
monkey a state that combines features of both and allows input to reverberate
in the network for hundreds of milliseconds. Overall, owing to its ready
applicability, the novel estimator paves the way to novel insight for the study
of spatially-extended dynamical systems.Comment: 7 pages + 12 pages supplementary information + 7 supplementary
figures. Title changed to match journal referenc
Homeostatic plasticity and external input shape neural network dynamics
In vitro and in vivo spiking activity clearly differ. Whereas networks in
vitro develop strong bursts separated by periods of very little spiking
activity, in vivo cortical networks show continuous activity. This is puzzling
considering that both networks presumably share similar single-neuron dynamics
and plasticity rules. We propose that the defining difference between in vitro
and in vivo dynamics is the strength of external input. In vitro, networks are
virtually isolated, whereas in vivo every brain area receives continuous input.
We analyze a model of spiking neurons in which the input strength, mediated by
spike rate homeostasis, determines the characteristics of the dynamical state.
In more detail, our analytical and numerical results on various network
topologies show consistently that under increasing input, homeostatic
plasticity generates distinct dynamic states, from bursting, to
close-to-critical, reverberating and irregular states. This implies that the
dynamic state of a neural network is not fixed but can readily adapt to the
input strengths. Indeed, our results match experimental spike recordings in
vitro and in vivo: the in vitro bursting behavior is consistent with a state
generated by very low network input (< 0.1%), whereas in vivo activity suggests
that on the order of 1% recorded spikes are input-driven, resulting in
reverberating dynamics. Importantly, this predicts that one can abolish the
ubiquitous bursts of in vitro preparations, and instead impose dynamics
comparable to in vivo activity by exposing the system to weak long-term
stimulation, thereby opening new paths to establish an in vivo-like assay in
vitro for basic as well as neurological studies.Comment: 14 pages, 8 figures, accepted at Phys. Rev.
Tailored ensembles of neural networks optimize sensitivity to stimulus statistics
The dynamic range of stimulus processing in living organisms is much larger
than a single neural network can explain. For a generic, tunable spiking
network we derive that while the dynamic range is maximal at criticality, the
interval of discriminable intensities is very similar for any network tuning
due to coalescence. Compensating coalescence enables adaptation of
discriminable intervals. Thus, we can tailor an ensemble of networks optimized
to the distribution of stimulus intensities, e.g., extending the dynamic range
arbitrarily. We discuss potential applications in machine learning.Comment: 6 pages plus supplemental materia
Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
Spreading processes are conventionally monitored on a macroscopic level by
counting the number of incidences over time. The spreading process can then be
modeled either on the microscopic level, assuming an underlying interaction
network, or directly on the macroscopic level, assuming that microscopic
contributions are negligible. The macroscopic characteristics of both
descriptions are commonly assumed to be identical. In this work, we show that
these characteristics of microscopic and macroscopic descriptions can be
different due to coalescence, i.e., a node being activated at the same time by
multiple sources. In particular, we consider a (microscopic) branching network
(probabilistic cellular automaton) with annealed connectivity disorder, record
the macroscopic activity, and then approximate this activity by a (macroscopic)
branching process. In this framework, we analytically calculate the effect of
coalescence on the collective dynamics. We show that coalescence leads to a
universal non-linear scaling function for the conditional expectation value of
successive network activity. This allows us to quantify the difference between
the microscopic model parameter and established macroscopic estimates. To
overcome this difference, we propose a non-linear estimator that correctly
infers the model branching parameter for all system sizes.Comment: 13 page
Improved understanding of cervical carcinogenesis by molecular profiling
Snijders, P.J.F. [Promotor]Meijer, C.J.L.M. [Promotor]Steenbergen, R.D.M. [Copromotor
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