95 research outputs found
Learning as a phenomenon occurring in a critical state
Recent physiological measurements have provided clear evidence about
scale-free avalanche brain activity and EEG spectra, feeding the classical
enigma of how such a chaotic system can ever learn or respond in a controlled
and reproducible way. Models for learning, like neural networks or perceptrons,
have traditionally avoided strong fluctuations. Conversely, we propose that
brain activity having features typical of systems at a critical point,
represents a crucial ingredient for learning. We present here a study which
provides novel insights toward the understanding of the problem. Our model is
able to reproduce quantitatively the experimentally observed critical state of
the brain and, at the same time, learns and remembers logical rules including
the exclusive OR (XOR), which has posed difficulties to several previous
attempts. We implement the model on a network with topological properties close
to the functionality network in real brains. Learning occurs via plastic
adaptation of synaptic strengths and exhibits universal features. We find that
the learning performance and the average time required to learn are controlled
by the strength of plastic adaptation, in a way independent of the specific
task assigned to the system. Even complex rules can be learned provided that
the plastic adaptation is sufficiently slow.Comment: 5 pages, 5 figure
Activity-dependent neuronal model on complex networks
Neuronal avalanches are a novel mode of activity in neuronal networks,
experimentally found in vitro and in vivo, and exhibit a robust critical
behaviour: These avalanches are characterized by a power law distribution for
the size and duration, features found in other problems in the context of the
physics of complex systems. We present a recent model inspired in
self-organized criticality, which consists of an electrical network with
threshold firing, refractory period and activity-dependent synaptic plasticity.
The model reproduces the critical behaviour of the distribution of avalanche
sizes and durations measured experimentally. Moreover, the power spectra of the
electrical signal reproduce very robustly the power law behaviour found in
human electroencephalogram (EEG) spectra. We implement this model on a variety
of complex networks, i.e. regular, small-world and scale-free and verify the
robustness of the critical behaviour.Comment: 9 pages, 8 figure
Dynamical scaling in branching models for seismicity
We propose a branching process based on a dynamical scaling hypothesis
relating time and mass. In the context of earthquake occurrence, we show that
experimental power laws in size and time distribution naturally originate
solely from this scaling hypothesis. We present a numerical protocol able to
generate a synthetic catalog with an arbitrary large number of events. The
numerical data reproduce the hierarchical organization in time and magnitude of
experimental inter-event time distribution.Comment: 3 figures to appear on Physical Review Letter
Brain modularity controls the critical behavior of spontaneous activity
The human brain exhibits a complex structure made of scale-free highly
connected modules loosely interconnected by weaker links to form a small-world
network. These features appear in healthy patients whereas neurological
diseases often modify this structure. An important open question concerns the
role of brain modularity in sustaining the critical behaviour of spontaneous
activity. Here we analyse the neuronal activity of a model, successful in
reproducing on non-modular networks the scaling behaviour observed in
experimental data, on a modular network implementing the main statistical
features measured in human brain. We show that on a modular network, regardless
the strength of the synaptic connections or the modular size and number,
activity is never fully scale-free. Neuronal avalanches can invade different
modules which results in an activity depression, hindering further avalanche
propagation. Critical behaviour is solely recovered if inter-module connections
are added, modifying the modular into a more random structure.Comment: 5 pages, 6 figure
Optimal percentage of inhibitory synapses in multi-task learning
Performing more tasks in parallel is a typical feature of complex brains.
These are characterized by the coexistence of excitatory and inhibitory
synapses, whose percentage in mammals is measured to have a typical value of
20-30\%. Here we investigate parallel learning of more Boolean rules in
neuronal networks. We find that multi-task learning results from the
alternation of learning and forgetting of the individual rules. Interestingly,
a fraction of 30\% inhibitory synapses optimizes the overall performance,
carving a complex backbone supporting information transmission with a minimal
shortest path length. We show that 30\% inhibitory synapses is the percentage
maximizing the learning performance since it guarantees, at the same time, the
network excitability necessary to express the response and the variability
required to confine the employment of resources.Comment: 5 pages, 5 figure
Critical bursts in filtration
Particle detachment bursts during the flow of suspensions through porous
media are a phenomenon that can severely affect the efficiency of deep bed
filters. Despite the relevance in several industrial fields, little is known
about the statistical properties and the temporal organization of these events.
We present experiments of suspensions of deionized water carrying quartz
particles pushed with a peristaltic pump through a filter of glass beads
measuring simultaneously pressure drop, flux and suspension solid fraction. We
find that the burst size distribution scales consistently with a power-law,
suggesting that we are in the presence of a novel experimental realization of a
self-organized critical system. Temporal correlations are present in the time
series, alike in other phenomena as earthquakes or neuronal activity bursts,
and also an analog to Omori's law can be shown. The understanding of bursts
statistics could provide novel insights in different fields, e.g. in filter and
petroleum industries.Comment: 7 pages, 9 figure
Rattler-induced aging dynamics in jammed granular systems
Granular materials jam when developing a network of contact forces able to
resist the applied stresses. Through numerical simulations of the dynamics of
the jamming process, we show that the jamming transition does not occur when
the kinetic energy vanishes. Rather, as the system jams, the kinetic energy
becomes dominated by rattlers particles, that scatter withing their cages. The
relaxation of the kinetic energy in the jammed configuration exhibits a double
power-law decay, which we interpret in terms of the interplay between backbone
and rattlers particles.Comment: The paper has been accepted by the journal "Soft Matter
Role of inhibitory neurons in temporal correlations of critical and supercritical spontaneous activity
Experimental and numerical results suggest that the brain can be viewed as a
system acting close to a critical point, as confirmed by scale-free
distributions of relevant quantities in a variety of different systems and
models. Less attention has received the investigation of the temporal
correlation functions in brain activity in different, healthy and pathological,
conditions. Here we perform this analysis by means of a model with short and
long-term plasticity which implements the novel feature of different recovery
rates for excitatory and inhibitory neurons, found experimentally. We evidence
the important role played by inhibitory neurons in the supercritical state: We
detect an unexpected oscillatory behaviour of the correlation decay, whose
frequency depends on the fraction of inhibitory neurons and their connectivity
degree. This behaviour can be rationalized by the observation that bursts in
activity become more frequent and with a smaller amplitude as inhibition
becomes more relevant.Comment: 15 pages, 6 figure
Non-monotonic dependence of the friction coefficient on heterogeneous stiffness
The complexity of the frictional dynamics at the microscopic scale makes
difficult to identify all of its controlling parameters. Indeed, experiments on
sheared elastic bodies have shown that the static friction coefficient depends
on loading conditions, the real area of contact along the interfaces and the
confining pressure. Here we show, by means of numerical simulations of a 2D
Burridge-Knopoff model with a simple local friction law, that the macroscopic
friction coefficient depends non-monotonically on the bulk elasticity of the
system. This occurs because elastic constants control the geometrical features
of the rupture fronts during the stick-slip dynamics, leading to four different
ordering regimes characterized by different orientations of the rupture fronts
with respect to the external shear direction. We rationalize these results by
means of an energetic balance argument
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