49,986 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
Memristive Learning Cellular Automata: Theory and Applications
Memristors are novel non volatile devices that manage to combine storing and
processing capabilities in the same physical place.Their nanoscale dimensions
and low power consumption enable the further design of various nanoelectronic
processing circuits and corresponding computing architectures, like
neuromorhpic, in memory, unconventional, etc.One of the possible ways to
exploit the memristor's advantages is by combining them with Cellular Automata
(CA).CA constitute a well known non von Neumann computing architecture that is
based on the local interconnection of simple identical cells forming
N-dimensional grids.These local interconnections allow the emergence of global
and complex phenomena.In this paper, we propose a hybridization of the CA
original definition coupled with memristor based implementation, and, more
specifically, we focus on Memristive Learning Cellular Automata (MLCA), which
have the ability of learning using also simple identical interconnected cells
and taking advantage of the memristor devices inherent variability.The proposed
MLCA circuit level implementation is applied on optimal detection of edges in
image processing through a series of SPICE simulations, proving its robustness
and efficacy
Artificial life meets computational creativity?
I review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity
Organisational Responses to Discontinuous Innovation: A Case Study Approach
Research that examines entrant-incumbent dynamics often points to the organisational limitations that constrain incumbents from successfully pursuing new technologies or fending off new entrants. Some incumbents are nevertheless able to successfully implement organisational structures and develop routines that overcome these institutional constraints. We provide a case-study analysis of how three firms - Motorola, IBM and Kodak - responded to discontinuous innovations and the associated structural and organisational limitations that are typical to incumbent organisations. Each firm was able to capture gains from new technologies and develop profitable products in emerging markets, although their abilities to sustain these gains varied due to subsequent organisational changes. Drawing from these case studies, we synthesise how firms can institute organisational strategies to continue to capture gains from disruptive innovations. A schema suggests that particular organisational strategies are comparatively optimal for corresponding points along an innovation lifecycle
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