32,307 research outputs found
Predictable Feature Analysis
Every organism in an environment, whether biological, robotic or virtual,
must be able to predict certain aspects of its environment in order to survive
or perform whatever task is intended. It needs a model that is capable of
estimating the consequences of possible actions, so that planning, control, and
decision-making become feasible. For scientific purposes, such models are
usually created in a problem specific manner using differential equations and
other techniques from control- and system-theory. In contrast to that, we aim
for an unsupervised approach that builds up the desired model in a
self-organized fashion. Inspired by Slow Feature Analysis (SFA), our approach
is to extract sub-signals from the input, that behave as predictable as
possible. These "predictable features" are highly relevant for modeling,
because predictability is a desired property of the needed
consequence-estimating model by definition. In our approach, we measure
predictability with respect to a certain prediction model. We focus here on the
solution of the arising optimization problem and present a tractable algorithm
based on algebraic methods which we call Predictable Feature Analysis (PFA). We
prove that the algorithm finds the globally optimal signal, if this signal can
be predicted with low error. To deal with cases where the optimal signal has a
significant prediction error, we provide a robust, heuristically motivated
variant of the algorithm and verify it empirically. Additionally, we give
formal criteria a prediction-model must meet to be suitable for measuring
predictability in the PFA setting and also provide a suitable default-model
along with a formal proof that it meets these criteria
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
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