Complex Insight - Understanding our world
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Complex Insight  - Understanding our world
A few things the Symbol Research team are reading.  Complex Insight is curated by Phillip Trotter (www.linkedin.com/in/phillip-trotter) from Symbol Research
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Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.

 

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks
Peter Csermely

http://arxiv.org/abs/1511.01238 ;


Via Complexity Digest
Complexity Digest's curator insight, November 18, 2015 6:13 PM

See Also: http://networkdecisions.linkgroup.hu 

António F Fonseca's curator insight, November 23, 2015 3:30 AM

Interesting  paper about fast cores and slow periphery,  conflict in the elite vs democratic consensus.

Marcelo Errera's curator insight, November 24, 2015 11:32 AM

Yes, there must be few fasts and many slows.  It's been predicted by CL in many instances.

 

http://www.researchgate.net/publication/273527384_Constructal_Law_Optimization_as_Design_Evolution

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Scientists discover workings of brain's 'GPS System' - Indian Express

Scientists discover workings of brain's 'GPS System' - Indian Express | Complex Insight  - Understanding our world | Scoop.it
Scientists discover workings of brain's 'GPS System' - Scientists have discovered how the brain's internal system works to determine the body's location as it moves through its sur
Phillip Trotter's insight:

The study from researchers at Princeton University found that certain position-tracking neurons - called grid cells - ramp their activity up and down by working together in a collective way to determine location. Grid cells are neurons that become electrically active, or "fire," as animals travel in an environment. First discovered in the mid-2000s, each cell fires when the body moves to specific locations, for example in a room. The neuronal locations are arranged in a hexagonal pattern like spaces on a Chinese checker board and together the grid cells form a representation of space according to David Tank, Princeton's Henry L Hillman Professor in Molecular Biology and leader of the study. Click on the image or title to learn more.

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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


Via Complexity Digest
Phillip Trotter's insight:

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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How bio-inspired deep learning keeps winning competitions | KurzweilAI

How bio-inspired deep learning keeps winning competitions | KurzweilAI | Complex Insight  - Understanding our world | Scoop.it

Good inverview with Dr. Jürgen Schmidhuber, Director of the Swiss Artificial Intelligence Lab, IDSIA. His research team’s artificial neural networks (NNs) have won many international awards, and recently were the first to achieve human-competitive performance on various benchmark data sets. IDSIA are probably the current world leaders in ANN research and the quality of what they do is rather insipiring. worth reading.

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