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An interesting fact in nature is that if we observe agents (neurons, particles, animals, humans) behaving, or more precisely moving, inside their environment, we can recognize - tough at different space or time scales - very specific patterns. The existence of those patterns is quite obvious, since not all things in nature behave totally at random, especially if we take into account thinking species like human beings. If a first phenomenon which has been deeply modeled is the gas particle motion as the template of a totally random motion, other phenomena, like foraging patterns of animals such as albatrosses, and specific instances of human mobility wear some randomness away in favor of deterministic components. Thus, while the particle motion may be satisfactorily described with a Wiener Process (also called Brownian motion), the oth...
The problem of statistical inference deals with the approximation of one or more unknown parameters on the basis of the study of random variables related to them, usually by referring to the elements of a sample. The basis of this kind of problem was deeply studied by the mathematical community in the period ranging from 1940 to 1960, building families of estimators for the unknown parameters whose statistical properties could assure a good performance in terms of minimization of suitable risk functions. On the other hand, several researchers working in the field of artificial intelligence started to focus on this problem about at the end of the previous mentioned period, when the development of computer science made available devices able to perform large amount of computations in a relatively small amount of time. This facilities ena...
Neuronal cells (neurons) mainly transmit signals by action potentials or spikes. Neuronal electrical activity is recorded from experimental animals by microelectrodes placed in specific brain areas. These electrochemical fast phenomena occur as all-or-none events and can be analyzed as boolean sequences. Following this approach, several computational analyses reported most variable neuronal behaviors expressed through a large variety of firing patterns. These patterns have been modeled as symbolic strings with a number of different techniques. As a rule, single neurons or neuronal ensembles are manageable as unknown discrete symbol sources S = <Σ, P> where Σ is the source alphabet and P is the unknown symbol probability distribution. Within the hierarchy of Markov Models (MMs), Markov Chains and Hidden MMs have been profusely employed ...
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