Archivio 2017 199 seminari

In recent years bottom-up network models that aim to capture how various brain processes propagate on the brain’s structural connectivity network have been proposed. These spread models are motivated by mounting evidence that both brain activity and various neurodegenerative diseases spread along fiber pathways and ramify within wider brain circuits in a stereotyped fashion. In the case of functional activity, this gives rise to canonical functional networks. In the case of neurodegeneration, the spread is underpinned by a so-called “trans-neuronal transmission” mechanism shared by all common degenerative pathologies, for example Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, corticobasal degeneration, etc. In this talk I will describe some of these graph theoretic models of spread. First, I will summarize how conventional graph theory metrics like small-world and path length are used in neuroimaging. Then I will specifically highlight the Network-Diffusion model, which seeks to capture network spread via a diffusive process restricted on the structural connectome. We will review the basic network mathematics that governs these diffusion processes. Finally we will show several examples from neuroimaging studies, specifically addressing how the network diffusion model can capture the relationship between structural connctome and functional connectome. Examples of successful network spread modeling in Alzheimer, Parkinson, frontotemporal dementia and aphasias will be presented.
Abstract: In this mini course we will introduce and study 2-step nilpotent Lie algebras, closely related to the Clifford algeras. The pseudo H-type Lie algebras, are generalisations of the Heisenberg type Lie algebras, introduced by Aroldo Kaplan at 1980 for the study of hypoelliptic operators. The mini course will include the following topics. 1. Definition of pseudo H-type Lie algebras. We give equivalent definitions of the pseudo H-type Lie algebras, that originated from the composition of quadratic forms, the representation of the Clifford algebras and isometric properties of the adjoint operator on the Lie algebra. 2. Relation of pseudo H-type Lie algebras and representations of Clifford algebras. Representations of Clifford algebras can be used for the construction of the Lie algebras if and only if the representation space can be endowed with a non degenerate bi-linear symmetric form, making the representation map skew symmetric with respect to this form. We will explain main difficulties of finding such a non-degenerate bi-linear symmetric form and provide several examples of the construction of the pseudo H-type Lie algebras from the Clifford algebra representations, including those that were introduced by A. Kaplan. 3. We will show the method of construction of a special basis for the pseudo H-type Lie algebras, such that the structural constants of the Lie algebras are always 0, 1 or -1. We show that the Bott periodicity of the Clifford algebras are naturally inherited by the pseudo H-type Lie algebras. It allows to reduce the construction to some basic cases. 4. We will show that infinite number of Clifford algebras leads to the infinite number of pseudo H-type Lie algebras. Moreover, the isomorphism of Clifford algebras is not automatically transmitted to the isomorphism of the Lie algebras. We will provide a complete classification of pseudo H-type Lie algebras. 5. The last topic is the description of automorphism groups of the pseudo H-type Lie algebras. It is not a closed topic still, nevertheless, I will inform on some achieved results.
Enabling visually-guided behaviors in artificial agents implies picking-up and organizing appropriate information from the visual signal at multiple levels. The question arises about how to carefully define which feature to extract, or, from a different perspective, which kind of representation to adopt for the visual signal itself. It is well known that receptive fields (RFs) in the early stages of the primary visual cortex behave as band-pass linear filters performing a multichannel representation of the visual signal (cf. the Gabor jets). Typically, visual features are direcly derived, as symbols, from the outputs of such front-end RFs. Here, I want to emphasize the advantages of thinking early visual processes in terms of signal processing, pointing out the key role played by a full harmonic representation of the visual signal and how highly informative properties of the visual signal are efficiently and effectively embedded in the local image phases and their relationships. Accordingly, instead of directly extracting "classic" spatial features (such as edges, corners, etc.) and then looking for correspondences, we can follow a complementary approach: the visual signal is described in frequency bandwidths in terms of local amplitude, phase and orientation, and more complex visual features are derived as "qualities" based on local phase properties e.g., such as phase conguency, phase difference, and phase constancy, for contrast transitions, disparity and motion, respectively. Notably, phase-based interpretation of the visual signal allows direct links between consolidated machine vision computational techniques and the ascertained properties of visual cortical cells. The issue of direct phase-based measurements vs. distributed population coding of visual features will be discussed in relations to motion and stereo perceptual tasks.
COLLOQUIO DI DIPARTIMENTO C’è una rivoluzione in corso, la rivoluzione digitale: la quantità di dati che produciamo raddoppia ogni anno; nel 2016 abbiamo generato tanti dati quanti ne erano stati prodotti nell’intera storia dell’umanità fino al 2015. Con IoT (Internet of Things) entro 10 anni avremo 150 miliardi di sensori connessi in rete, 20 volte più che il numero di persone sulla Terra. Allora la quantità di dati raddoppierà ogni 12 ore. È la quinta rivoluzione dell’IT: dopo i grandi computer, i pc, internet e il web 1.0, i cellulari e il web 2.0, i Big Data – una rivoluzione dovuta allo tsunami di dati, dove tutto quello che facciamo lascia una traccia digitale. Una rivoluzione paragonabile a quella avvenuta con l’invenzione della stampa. I bits faranno molto più di quanto i caratteri mobili di Gutenberg abbiano fatto in termini di spostamento degli equilibri del potere e di trasferimento della conoscenza dalle mani di pochi a comunità sempre più allargate. L’intelligenza artificiale sta facendo progressi impensati, soprattutto attraverso l’analisi dei dati. L’AI non si programma più riga per riga, ma è ora capace di imparare e di automigliorarsi continuamente: sono ormai standard algoritmi in grado di completare compiti che richiedono ‘intelligenza’ meglio degli uomini. Fra il 2020 e il 2060 i super-computer sorpasseranno le capacità umane in moltissime aree. In questo quadro, da un lato i Big Data dall’altro l’AI impongono compiti di manipolazione dei dati che sono strenui sia per la computer science (nuovi paradigmi computazionali; computazione interattiva; la sfida del 'beyond Turing') che per la 'data analytics' (nuove metodologie di approccio al 'data mining'; analisi dei dati topologica; inferenza causale non lineare) per affrontare problemi complessi, nelle scienze di base (scienze della vita, clima, scienze della terra, …) come in quelle sociali, con la data science (A.I., data mining, machine learning, deep learning, teoria topologica del campo dei dati) e la scienza della complessità (teoria delle reti). Ne segue la necessità di una nuova, forte alleanza che combinando metodi e conoscenze della fisica statistica, della matematica, della computer science permetta alla scienza di affrontare in modo vincente questa sfida epocale. Mario Rasetti è Professore Emerito di Fisica Teorica al Politecnico di Torino ed è Presidente di ISI Foundation, Torino e ISI Global Science Foundation, New York.
In this presentation, we will analyze a p-Laplacian problem set in a ball of R^N, with homogeneous Neumann boundary conditions. The equation involves a nonlinearity g which is (p-1)-superlinear at infinity, possibly supercritical in the sense of Sobolev embeddings. The nonlinearity allows the problem to have a constant non-zero solution. In this setting, we prove via shooting method the existence, multiplicity, and oscillatory behavior (around the constant solution) of non-constant, positive, radial solutions. We show that the situation changes drastically depending on p>1. For example, in the prototype case g(s)=s^{q-1}, if p>2, the problem has infinitely many solutions for q>p. While, if p=2, the problem admits at least k non-constant solutions provided that q-2 is bigger than the (k+1)-th radial eigenvalue of the Laplacian with Neumann boundary conditions. Finally, for 1<p<2 a surprising result is found, as non-constant solutions with the same oscillatory behavior appear in couples when the radius of the domain is big enough. We will try to give a unified description and motivation for these three different situations. This is a joint work with Alberto Boscaggin (Università di Torino) and Benedetta Noris (Universitè de Picardie Jules Verne). [A. Boscaggin, F. Colasuonno, B. Noris, Multiple positive solutions for a class of p-Laplacian Neumann problems without growth conditions, preprint] [F. Colasuonno, B. Noris, A p-Laplacian supercritical Neumann problem, Discrete Contin. Dyn. Syst., Vol. 37 n. 6 (2017) 3025-3057]
We provide analytical approximations for the law of the solutions to a certain class of scalar McKean-Vlasov stochastic differential equations (MKV-SDEs) with random initial datum. "Propagation of chaos" results (Sznitman 1991) connect this class of SDEs with the macroscopic limiting behavior of a particle, evolving within a mean-field interaction particle system, as the total number of particles tends to infinity. Here we assume the mean-field interaction only acting on the drift of each particle, this giving rise to a MKV-SDE where the drift coefficient depends on the law of the unknown solution. By perturbing the non-linear forward Kolmogorov equation associated to the MKV-SDE, we perform a two-steps approximating procedure that decouples the McKean-Vlasov interaction from the standard dependence on the state-variables. The first step yields an expansion for the marginal distribution at a given time, whereas the second yields an expansion for the transition density. Both the approximating series turn out to be asymptotically convergent in the limit of short times and small noise, the convergence order for the latter expansion being higher than for the former. The resulting approximation formulas are expressed in semi-closed form and can be then regarded as a viable alternative to the numerical simulation of the large-particle system, which can be computationally very expensive. Moreover, these results pave the way for further extensions of this approach to more general dynamics and to high-dimensional settings.
Along the last years the technological advancements have been fundamental to improve the recording capability from brain areas and neural populations. For example multi-site recordings can be achieved from thousands of channels (sites) with a good spatial and temporal resolution yielding a good description of the underlying network dynamics. Given that, the brain operates on a single trial basis such recordings are becoming important to understand the neural code. As a first step, multi-site recordings allow to quantify the information flow in the network. The anatomical wiring (i.e. Structural Connectivity, SC) clearly plays a fundamental role to understand how cells communicate among them but it is often not well known neither it can by itself explain the overall network activity. Multi-site recordings can be used to infer statistical dependencies (i.e. Functional Connections, FC) among the recorded units and to track the information flow in the network. On the other hand the Effective Connectivity (EC) denotes the directed causal relationship between the recorded sites. Experimentally, the EC is typically estimated by stimulating one cell and studying the effects on the connected elements. Alternatively the EC can also be studied by using a causal mathematical model between the recorded units data. Importantly, multi-site recordings raise some limitations that need to be evaluated carefully before any further analysis. First, the experimental sessions are often limited in time. Second, the high dimensional data sets involve a set of numerical and mathematical problems that would be hard to face even with long enough recording sessions. These issues are common to different fields and have been coined as “curse of dimensionality”. In order to capture nonlinear interactions between even short and noisy time series, we consider an event- based model. Then, we involve the physiological basis of the signal, which is likely to be mainly nonlinear. Specifically, we suppose that we are able to observe the dynamical behaviours of individual components of a neuronal networks and that few of the components may be causally influencing each other. The variables could be time series from different parts of the brain. In order to introduce our method we have considered a simulated cerebellar granule cell network capturing nonlinear interactions between even short and noisy time series. Although the proposed EC algorithm cannot be applied straightforwardly to the experimental data, our preliminary results are quite promising. This is a joint work with G. Aletti, T. Nieus, and M. Moroni.