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Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.

 

Neural Computation and the Computational Theory of Cognition

Gualtiero Piccinini, Sonya Bahar

Cognitive Science
Volume 37, Issue 3, pages 453–488, April 2013

http://dx.doi.org/10.1111/cogs.12012


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ComplexInsight's curator insight, April 15, 2013 2:53 AM

Re-reading some of John Holland's work on neural network simulation at present while looking into different models of computation and digital physics, so this is a timely paper.  Looks to be an interesting read.

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Stop Hyping Big Data and Start Paying Attention to 'Long Data' | Wired Opinion | Wired.com

Stop Hyping Big Data and Start Paying Attention to 'Long Data' | Wired Opinion | Wired.com | Information, Complexity, Computation | Scoop.it
Our species can’t seem to escape big data. We have more data inputs, storage, and computing resources than ever, so Homo sapiens naturally does what it has always done when given new tools: it goes even bigger, higher, and bolder.
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Big Data Opportunities for Global Infectious Disease Surveillance

Systems to provide static spatially continuous maps of infectious disease risk and continually updated reports of infectious disease occurrence exist but to-date the two have never been combined.
Novel online data sources, such as social media, combined with epidemiologically relevant environmental information are valuable new data sources that can assist the “real-time” updating of spatial maps.
Advances in machine learning and the use of crowd sourcing open up the possibility of developing a continually updated atlas of infectious diseases.
Freely available dynamic infectious disease risk maps would be valuable to a wide range of health professionals from policy makers prioritizing limited resources to individual clinicians.

 

Hay SI, George DB, Moyes CL, Brownstein JS (2013) Big Data Opportunities for Global Infectious Disease Surveillance. PLoS Med 10(4): e1001413. http://dx.doi.org/10.1371/journal.pmed.1001413


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How bots are taking over the world

How bots are taking over the world | Information, Complexity, Computation | Scoop.it
Dan O'Hara and Luke Robert Mason: It's not just Jon Ronson whose life is being manipulated by internet algorithms – it's all of us. And their power is growing
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Life, but not as we know it: Welcome to a future where man-made organisms build us cities on alien planets

Life, but not as we know it: Welcome to a future where man-made organisms build us cities on alien planets | Information, Complexity, Computation | Scoop.it
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Regularity and Complexity in Dynamical Systems (by Albert C. J. Luo)

Regularity and Complexity in Dynamical Systems describes periodic and chaotic behaviors in dynamical systems, including continuous, discrete, impulsive,discontinuous, and switching systems. In traditional analysis, the periodic and chaotic behaviors in continuous, nonlinear dynamical systems were extensively discussed even if unsolved. In recent years, there has been an increasing amount of interest in periodic and chaotic behaviors in discontinuous dynamical systems because such dynamical systems are prevalent in engineering. Usually,the smoothening of discontinuous dynamical system is adopted in order to use the theory of continuous dynamical systems. However, such technique cannot provide suitable results in such discontinuous systems. In this book, an alternative way is presented to discuss the periodic and chaotic behaviors in discontinuous dynamical systems.

 

 


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Predicting and controlling infectious disease epidemics using temporal networks

Predicting and controlling infectious disease epidemics using temporal networks | Information, Complexity, Computation | Scoop.it

Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

 

Predicting and controlling infectious disease epidemics using temporal networks

Naoki Masuda and Petter Holme

F1000Prime Rep2013, 5:6 (doi: 10.12703/P5-6)

http://f1000.com/prime/reports/b/5/6


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Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement

Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement | Information, Complexity, Computation | Scoop.it

Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise.

 

Beuls K, Steels L (2013) Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement. PLoS ONE 8(3): e58960. http://dx.doi.org/10.1371/journal.pone.0058960


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luiy's curator insight, June 28, 2013 8:34 AM

We presented here the first agent-based models to explore how and why a grammatical agreement system may originate and get culturally transmitted in a process of cultural invention and social learning, based on the hypothesis that agreement systems are useful to avoid combinatorial explosions in parsing and semantic ambiguity in interpretation. Agreement systems thus help to minimize cognitive effort and maximize communicative success. After demonstrating how formal markers could arise, we presented strategies showing how meaningful markers could originate, and how markers could become recruited from existing words. We demonstrated also how recruited words could erode to lead to greater articulatory efficiency, at a cost of giving fewer hints for new language users, and how coercion helps to apply an agreement system more broadly so that fewer agreement markers are needed.

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Unique in the Crowd: The privacy bounds of human mobility

Unique in the Crowd: The privacy bounds of human mobility | Information, Complexity, Computation | Scoop.it

We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.

 

Unique in the Crowd: The privacy bounds of human mobility

Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen & Vincent D. Blondel
Scientific Reports 3, Article number: 1376 http://dx.doi.org/10.1038/srep01376


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Have We Evolved to Be Nasty or Nice?

It is futile to ask whether people are naturally cooperative or selfish. They can be either, depending on the circumstances. Dr. Helbing cites "tragedies of the commons" where open access to a common-pool resource such as a fishery tends to result in overfishing that harms everybody—a sort of extended real-world version of the prisoner's dilemma.


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Math:Rules - Strange Attractors

I wanted to create a series of pictures representing mathematical shapes on white background, like a "tribute to mathematics" that I often use in my work. I chose the "strange attractors" for their dynamic forms and "chaotic feel".

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Complex Adaptive Game Theory

Complex Adaptive Game Theory | Information, Complexity, Computation | Scoop.it
As mentioned last week, a U Maine economist named Sarah Morehead had this idea : Develop an economic environment in which selfish zero-sum-game strategies are maladaptive.
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World faces decades of climate chaos, outgoing chief scientific adviser warns - Telegraph

World faces decades of climate chaos, outgoing chief scientific adviser warns - Telegraph | Information, Complexity, Computation | Scoop.it
The world faces decades of turbulent weather even if it takes drastic action to tackle climate change, the Government's chief scientific adviser said today in a final stark warning as he prepares to step down.
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[1303.5966] The emergence and role of strong ties in time-varying communication networks

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Computers Made Out of DNA, Slime and Other Strange Stuff | Wired Science | Wired.com

Computers Made Out of DNA, Slime and Other Strange Stuff | Wired Science | Wired.com | Information, Complexity, Computation | Scoop.it
Everybody knows a computer is a machine made of metal and plastic, with microchip cores turning streams of electrons into digital reality. A century from now, though, computers could look quite different.
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The Dynamics of Health Behavior Sentiments on a Large Online Social Network

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent.

 

The Dynamics of Health Behavior Sentiments on a Large Online Social Network
Marcel Salathe, Duy Q Vu, Shashank Khandelwal and David R Hunter

EPJ Data Science 2013, 2:4 http://dx.doi.org/10.1140/epjds16

 


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Chaos in Nature (by Christophe Letellier)

Chaos theory deals with the description of motion (in a general sense) which cannot be predicted in the long term although produced by deterministic system, as well exemplified by meteorological phenomena. It directly comes from the Lunar theory -- a three-body problem -- and the difficulty encountered by astronomers to accurately predict the long-term evolution of the Moon using "Newtonian" mechanics. Henri Poincare's deep intuitions were at the origin of chaos theory. They also led the meteorologist Edward Lorenz to draw the first chaotic attractor ever published. But the main idea consists of plotting a curve representative of the system evolution rather than finding an analytical solution as commonly done in classical mechanics. Such a novel approach allows the description of population interactions and the solar activity as well. Using the original sources, the book draws on the history of the concepts underlying chaos theory from the 17th century to the last decade, and by various examples, show how general is this theory in a wide range of applications: meteorology, chemistry, populations, astrophysics, biomedicine, etc.

 

 


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Measuring information interactions on the ordinal pattern of stock time series

The interactions among time series as individual components of complex systems can be quantified by measuring to what extent they exchange information among each other. In many applications, one focuses not on the original series but on its ordinal pattern. In such cases, trivial noises appear more likely to be filtered and the abrupt influence of extreme values can be weakened. Cross-sample entropy and inner composition alignment have been introduced as prominent methods to estimate the information interactions of complex systems. In this paper, we modify both methods to detect the interactions among the ordinal pattern of stock return and volatility series, and we try to uncover the information exchanges across sectors in Chinese stock markets.

 

Xiaojun Zhao, Pengjian Shang, and Jing Wang

Phys. Rev. E 87, 022805 (2013)
http://link.aps.org/doi/10.1103/PhysRevE.87.022805


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The Power of Swarms Can Help Us Fight Cancer, Understand the Brain, and Predict the Future

The Power of Swarms Can Help Us Fight Cancer, Understand the Brain, and Predict the Future | Information, Complexity, Computation | Scoop.it

But those secrets were hidden for decades. Science, in general, is a lot better at breaking complex things into tiny parts than it is at figuring out how tiny parts turn into complex things. When it came to figuring out collectives, nobody had the methods or the math.


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A Computable Universe: Understanding and Exploring Nature as Computation

A Computable Universe: Understanding and Exploring Nature as Computation

~ Hector Zenil (author) More about this product
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This volume, with a foreword by Sir Roger Penrose, discusses the foundations of computation in relation to nature.
It focuses on two main questions:

What is computation?
How does nature compute?
The contributors are world-renowned experts who have helped shape a cutting-edge computational understanding of the universe. They discuss computation in the world from a variety of perspectives, ranging from foundational concepts to pragmatic models to ontological conceptions and philosophical implications.


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Fairy Circles in Africa May Be Work of Termites

Fairy Circles in Africa May Be Work of Termites | Information, Complexity, Computation | Scoop.it
New research posits that the reddish barren spots, known as fairy circles, that dot a narrow belt of African desert could be the work of industrious sand termites.
Eugene Ch'ng's insight:

Stigmergy for certain.

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Interactions between species and environments from incomplete information

There are two contradictory aspects of the adaptive process in evolution. The first is that species must optimally increase their own fitness in a given environment. The second is that species must maintain their variation to be ready to respond to changing environments. In a strict sense, these two aspects might consider to be mutually exclusive. If species are optimally adapted, then the variation in the species that is suboptimal decreases and vice versa. To resolve this dilemma, species must find a balance between optimal adaptation and robust adaptation. Finding the balance between these processes requires both the local and global complete, static information. However, the balance between the processes must be dynamic. In this study, we propose a model that illustrates dynamic negotiation between the global and local information using lattice theory. The dynamic negotiation between these two levels results in an overestimate of fitness for each species. The overestimation of fitness in our model represents the multiplicity of fitness which is sometimes discussed as the exaptation. We show that species in our model demonstrate the power law of the lifespan distribution and 1/f fluctuation for the adaptive process. Our model allows for a balance between optimal adaptation and robust adaptation without any arbitrary parameters.

 

Interactions between species and environments from incomplete information
Takayuki Niizato, Yukio-Pegio Gunji

Biosystems
Volume 111, Issue 3, March 2013, Pages 145–155

http://dx.doi.org/10.1016/j.biosystems.2012.12.003


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Hierarchy in complex systems: the possible and the actual

Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that non-adaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace using {\em in silico} evolved networks.

 

Hierarchy in complex systems: the possible and the actual

Bernat Corominas-Murtra, Joaquín Goñi, Ricard V. Solé, Carlos Rodríguez-Caso

http://arxiv.org/abs/1303.2503


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