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

A preliminary assessment of the effects of migration on the production structure in Europe: A labor task approach

We assess the effect of migration on the production structure in a selection of European countries for the pre-Great Recession period 2001-2009. We propose a labor-task approach where the inflow of migrants raises the relative supply of manual-physical (or simple) tasks and therefore favors simple-task intensive sectors. We use the US O*NET database in conjunction with European labor data to calculate the index of simple-task intensity at the industry and country level. The analysis confirms that a rise in employment migration rates has a generalized positive impact, but that value added increases significantly more in sectors that use more intensively simple tasks. A traditional shift-share instrument is used to overcome possible endogeneity problems

Identifying fiscal shocks and policy regimes in OECD countries

JEL Classification: E62, H30Fiscal Policy, SVAR

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

Spatial Externalities and Empirical Analysis: The case of Italy

In the last ten years the space issue, i.e. the study of the role played by space in economic phenomena, has attracted a lot of interest from many economic fields. Both the suitability of spatial economics to address questions posed by globalization, and improves in modeling techniques are at the basis of this revolution. The combination of increasing returns, market imperfections, and trade costs creates new forces that, together with factor endowments, determine the distribution of economic activities. These spatial externalities makes agents' location choice highly interdependent, thus allowing to understand the empirical spatial correlation between demand and production previously observed by the market potential literature. Despite their theoretical relevance, there is still little evidence, especially at large scale level, on the effective contribution of this new identified forces to agents' location decisions. The aim of this work is to directly estimate a model of economic geography on some Italian regional data in order to both test the empirical relevance of this theory and try to give a measure of the geographic extent of spatial externalities.Economic Geography; Spatial Externalities; Market Potential

The role of static stress diffusion in the spatio-temporal organization of aftershocks

We investigate the spatial distribution of aftershocks and we find that aftershock linear density exhibits a maximum, that depends on the mainshock magnitude, followed by a power law decay. The exponent controlling the asymptotic decay and the fractal dimensionality of epicenters clearly indicate triggering by static stress. The non monotonic behavior of the linear density and its dependence on the mainshock magnitude can be interpreted in terms of diffusion of static stress. This is supported by the power law growth with exponent $H\simeq 0.5$ of the average main-aftershock distance. Implementing static stress diffusion within a stochastic model for aftershock occurrence we are able to reproduce aftershock linear density spatial decay, its dependence on the mainshock magnitude and its evolution in time.Comment: 4 figure

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