182 research outputs found
A general representation of dynamical systems for reservoir computing
Dynamical systems are capable of performing computation in a reservoir
computing paradigm. This paper presents a general representation of these
systems as an artificial neural network (ANN). Initially, we implement the
simplest dynamical system, a cellular automaton. The mathematical fundamentals
behind an ANN are maintained, but the weights of the connections and the
activation function are adjusted to work as an update rule in the context of
cellular automata. The advantages of such implementation are its usage on
specialized and optimized deep learning libraries, the capabilities to
generalize it to other types of networks and the possibility to evolve cellular
automata and other dynamical systems in terms of connectivity, update and
learning rules. Our implementation of cellular automata constitutes an initial
step towards a general framework for dynamical systems. It aims to evolve such
systems to optimize their usage in reservoir computing and to model physical
computing substrates.Comment: 5 pages, 3 figures, accepted workshop paper at Workshop on Novel
Substrates and Models for the Emergence of Developmental, Learning and
Cognitive Capabilities at IEEE ICDL-EPIROB 201
Bits from Biology for Computational Intelligence
Computational intelligence is broadly defined as biologically-inspired
computing. Usually, inspiration is drawn from neural systems. This article
shows how to analyze neural systems using information theory to obtain
constraints that help identify the algorithms run by such systems and the
information they represent. Algorithms and representations identified
information-theoretically may then guide the design of biologically inspired
computing systems (BICS). The material covered includes the necessary
introduction to information theory and the estimation of information theoretic
quantities from neural data. We then show how to analyze the information
encoded in a system about its environment, and also discuss recent
methodological developments on the question of how much information each agent
carries about the environment either uniquely, or redundantly or
synergistically together with others. Last, we introduce the framework of local
information dynamics, where information processing is decomposed into component
processes of information storage, transfer, and modification -- locally in
space and time. We close by discussing example applications of these measures
to neural data and other complex systems
From individual behaviour to an evaluation of the collective evolution of crowds along footbridges
This paper proposes a crowd dynamic macroscopic model grounded on microscopic
phenomenological observations which are upscaled by means of a formal
mathematical procedure. The actual applicability of the model to real world
problems is tested by considering the pedestrian traffic along footbridges, of
interest for Structural and Transportation Engineering. The genuinely
macroscopic quantitative description of the crowd flow directly matches the
engineering need of bulk results. However, three issues beyond the sole
modelling are of primary importance: the pedestrian inflow conditions, the
numerical approximation of the equations for non trivial footbridge geometries,
and the calibration of the free parameters of the model on the basis of in situ
measurements currently available. These issues are discussed and a solution
strategy is proposed.Comment: 23 pages, 10 figures in J. Engrg. Math., 201
Mathematical modelling of solid tumour growth: a Dynamical Density Functional Theory-based model
We present a theoretical framework based on an extension of Dynamical Density Functional Theory (DDFT) to describe the structure and dynamics of cells in living tissues and tumours. DDFT is a microscopic statistical mechanical theory for the time evolution of the density distribution of interacting many-particle systems. The theory accounts for cell pair-interactions, different cell types, phenotypes and cell birth and death processes (including cell division), in order to provide a biophysically consistent description of processes bridging across the scales, including the description of the tissue structure down to the level of the individual cells. Analysis of the model is presented for a single species and a two-species cases, the latter describing competition between a cancerous and healthy cells. In suitable parameter regimes, model results are consistent with biological observations. Of particular note, divergent tumour growth behaviour, mirroring metastatic and benign growth characteristics, are shown to be dependent on the cell pair-interaction parameters
- …