3,467 research outputs found
A Multi-scale View of the Emergent Complexity of Life: A Free-energy Proposal
We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales
LSTM-based Anomaly Detection for Non-linear Dynamical System
Anomaly detection for non-linear dynamical system plays an important role in
ensuring the system stability. However, it is usually complex and has to be
solved by large-scale simulation which requires extensive computing resources.
In this paper, we propose a novel anomaly detection scheme in non-linear
dynamical system based on Long Short-Term Memory (LSTM) to capture complex
temporal changes of the time sequence and make multi-step predictions.
Specifically, we first present the framework of LSTM-based anomaly detection in
non-linear dynamical system, including data preprocessing, multi-step
prediction and anomaly detection. According to the prediction requirement, two
types of training modes are explored in multi-step prediction, where samples in
a wall shear stress dataset are collected by an adaptive sliding window. On the
basis of the multi-step prediction result, a Local Average with Adaptive
Parameters (LAAP) algorithm is proposed to extract local numerical features of
the time sequence and estimate the upcoming anomaly. The experimental results
show that our proposed multi-step prediction method can achieve a higher
prediction accuracy than traditional method in wall shear stress dataset, and
the LAAP algorithm performs better than the absolute value-based method in
anomaly detection task.Comment: 8 pages, 6 figure
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
Reinforcement learning or active inference?
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain
On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances
In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.Peer Reviewe
Visual motion processing and human tracking behavior
The accurate visual tracking of a moving object is a human fundamental skill
that allows to reduce the relative slip and instability of the object's image
on the retina, thus granting a stable, high-quality vision. In order to
optimize tracking performance across time, a quick estimate of the object's
global motion properties needs to be fed to the oculomotor system and
dynamically updated. Concurrently, performance can be greatly improved in terms
of latency and accuracy by taking into account predictive cues, especially
under variable conditions of visibility and in presence of ambiguous retinal
information. Here, we review several recent studies focusing on the integration
of retinal and extra-retinal information for the control of human smooth
pursuit.By dynamically probing the tracking performance with well established
paradigms in the visual perception and oculomotor literature we provide the
basis to test theoretical hypotheses within the framework of dynamic
probabilistic inference. We will in particular present the applications of
these results in light of state-of-the-art computer vision algorithms
Physical Activity Recognition and Identification System
Background: It is well-established that physical activity is beneficial to health. It is less known how the characteristics of physical activity impact health independently of total amount. This is due to the inability to measure these characteristics in an objective way that can be applied to large population groups. Accelerometry allows for objective monitoring of physical activity but is currently unable to identify type of physical activity accurately. Methods: This thesis details the creation of an activity classifier that can identify type from accelerometer data. The current research in activity classification was reviewed and methodological challenges were identified. The main challenge was the inability of classifiers to generalize to unseen data. Creating methods to mitigate this lack of generalisation represents the bulk of this thesis. Using the review, a classification pipeline was synthesised, representing the sequence of steps that all activity classifiers use. 1. Determination of device location and setting (Chapter 4) 2. Pre-processing (Chapter 5) 3. Segmenting into windows (Chapters 6) 4. Extracting features (Chapters 7,8) 5. Creating the classifier (Chapter 9) 6. Post-processing (Chapter 5) For each of these steps, methods were created and tested that allowed for a high level of generalisability without sacrificing overall performance. Results: The work in this thesis results in an activity classifier that had a good ability to generalize to unseen data. The classifier achieved an F1-score of 0.916 and 0.826 on data similar to its training data, which is statistically equivalent to the performance of current state of the art models (0.898, 0.765). On data dissimilar to its training data, the classifier achieved a significantly higher performance than current state of the art methods (0.759, 0.897 versus 0.352, 0.415). This shows that the classifier created in this work has a significantly greater ability to generalise to unseen data than current methods. Conclusion: This thesis details the creation of an activity classifier that allows for an improved ability to generalize to unseen data, thus allowing for identification of type from acceleration data. This should allow for more detailed investigation into the specific health effects of type in large population studies utilising accelerometers
Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?
Thanks to recent technological advances, it is now possible to track with an
unprecedented precision and for long periods of time the movement patterns of
many living organisms in their habitat. The increasing amount of data available
on single trajectories offers the possibility of understanding how animals move
and of testing basic movement models. Random walks have long represented the
main description for micro-organisms and have also been useful to understand
the foraging behaviour of large animals. Nevertheless, most vertebrates, in
particular humans and other primates, rely on sophisticated cognitive tools
such as spatial maps, episodic memory and travel cost discounting. These
properties call for other modeling approaches of mobility patterns. We propose
a foraging framework where a learning mobile agent uses a combination of
memory-based and random steps. We investigate how advantageous it is to use
memory for exploiting resources in heterogeneous and changing environments. An
adequate balance of determinism and random exploration is found to maximize the
foraging efficiency and to generate trajectories with an intricate
spatio-temporal order. Based on this approach, we propose some tools for
analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio
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