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Figure – Neural circuit controller of the virtual ant (page 3, fig. 2). [URL: http://arxiv.org/abs/1507.08467 ]
Intelligence and decision in foraging ants. Individual or Collective? Internal or External? What is the right balance between the two. Can one have internal intelligence without external intelligence? Can one take examples from nature to build in silico artificial lives that present us with interesting patterns? We explore a model of foraging ants in this paper that will be presented in early September in Exeter, UK, at UKCI 2015. (available on arXiv [PDF] and ResearchGate)
Cristian Jimenez-Romero, David Sousa-Rodrigues, Jeffrey H. Johnson, Vitorino Ramos; “A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones“, UKCI 2015 Computational Intelligence – University of Exeter, UK, September 2015.
Abstract: A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food (rewarding) and obstacles (harmful) which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The spiking neural circuits of the ant is trained to identify food and obstacles and move towards the former and avoid the latter. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. In this case the introduction of a double pheromone mechanism (positive and negative reinforcement feedback) yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony. The presented model is an important pedagogical tool as it is also an easy to use library that allows access to the spiking neural network paradigm from inside a Netlogo—a language used mostly in agent based modelling and experimentation with complex systems.
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 A. Abraham and V. Ramos, “Web usage mining using artificial ant colony clustering and linear genetic programming,” in Proceedings of the Congress on Evolutionary Computation. Australia: IEEE Press, 2003, pp. 1384–1391.
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 V. Ramos and J. J. Merelo, “Self-organized stigmergic document maps: Environment as a mechanism for context learning,” in Proceddings of the AEB, Merida, Spain, February 2002. ´
 D. Sousa-Rodrigues and V. Ramos, “Traversing news with ant colony optimisation and negative pheromones,” in European Conference in Complex Systems, Lucca, Italy, Sep 2014.
 E. Bonabeau, G. Theraulaz, and M. Dorigo, Swarm Intelligence: From Natural to Artificial Systems, 1st ed., ser. Santa Fe Insitute Studies In The Sciences of Complexity. 198 Madison Avenue, New York: Oxford University Press, USA, Sep. 1999.
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 C. Jimenez-Romero and J. Johnson, “Accepted abstract: Simulation of agents and robots controlled by spiking neural networks using netlogo,” in International Conference on Brain Engineering and Neuro-computing, Mykonos, Greece, Oct 2015.
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 D. Sousa-Rodrigues, J. Louçã, and V. Ramos, “From standard ˜ to second-order swarm intelligence phase-space maps,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, Sep 2011.
 V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Spatio-temporal ˜ dynamics on co-evolved stigmergy,” in 8th European Conference on Complex Systems, S. Thurner, Ed., Vienna, Austria, 9 2011.
 S. Tisue and U. Wilensky, “Netlogo: A simple environment for modeling complexity,” in International conference on complex systems. Boston, MA, 2004, pp. 16–21.
[…] Analogy is the core of all thinking. – This is the simple but unorthodox premise that Pulitzer Prize-winning author Douglas Hofstadter and French psychologist Emmanuel Sander defend in their new work. Hofstadter has been grappling with the mysteries of human thought for over thirty years. Now, with his trademark wit and special talent for making complex ideas vivid, he has partnered with Sander to put forth a highly novel perspective on cognition. We are constantly faced with a swirling and intermingling multitude of ill-defined situations. Our brain’s job is to try to make sense of this unpredictable, swarming chaos of stimuli. How does it do so? The ceaseless hail of input triggers analogies galore, helping us to pinpoint the essence of what is going on. Often this means the spontaneous evocation of words, sometimes idioms, sometimes the triggering of nameless, long-buried memories.
Why did two-year-old Camille proudly exclaim, “I undressed the banana!”? Why do people who hear a story often blurt out, “Exactly the same thing happened to me!” when it was a completely different event? How do we recognize an aggressive driver from a split-second glance in our rear-view mirror? What in a friend’s remark triggers the offhand reply, “That’s just sour grapes”? What did Albert Einstein see that made him suspect that light consists of particles when a century of research had driven the final nail in the coffin of that long-dead idea? The answer to all these questions, of course, is analogy-making – the meat and potatoes, the heart and soul, the fuel and fire, the gist and the crux, the lifeblood and the wellsprings of thought. Analogy-making, far from happening at rare intervals, occurs at all moments, defining thinking from top to toe, from the tiniest and most fleeting thoughts to the most creative scientific insights.
Like Gödel, Escher, Bach before it, Surfaces and Essences will profoundly enrich our understanding of our own minds. By plunging the reader into an extraordinary variety of colorful situations involving language, thought, and memory, by revealing bit by bit the constantly churning cognitive mechanisms normally completely hidden from view, and by discovering in them one central, invariant core – the incessant, unconscious quest for strong analogical links to past experiences – this book puts forth a radical and deeply surprising new vision of the act of thinking. […] intro to “Surfaces and Essences – Analogy as the fuel and fire of thinking” by Douglas Hofstadter and Emmanuel Sander, Basic Books, NY, 2013 [link] (to be released May 1, 2013).
The importance of Network Topology again… at [PLoS]. Abstract: […] The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems. […] (*)
(*) in Hermundstad AM, Brown KS, Bassett DS, Carlson JM, 2011 Learning, Memory, and the Role of Neural Network Architecture. PLoS Comput Biol 7(6): e1002063. doi:10.1371/journal.pcbi.1002063