I just came back from attending to the ANN SONIC Conference at Los Angeles. I was invited together with Javier Toret by Manuel Castells to present some of the results we have been developing during the last year at the DatAnalysis15M research network.
We presented an analysis of how new large-scale models of social/political organization that have emerged during the last years around the Spanish 15M movement. We described two different types of large-scale organization: a first phase where crowds learn to strategically use social media and other digital tools to modulate collective action, massive emotional contagion and focus of swarm attention; and a second phase where some parts of the network start developing a strong functional specialization (e.g. the PAH network focused on foreclosure evictions, Marea Verde focused in public education, etc.), creating a ecosystem which gives place to the emerge of a long-term robust network organization based on transient moments synchronization, in a similar way of how the dynamic core hypothesis suggest neural self-organization works.
You can see here the slides of our presentation here.
Together with Manuel Bedia, Xabier Barandiaran and Bruno Santos I just published in Frontiers in Computational Neuroscience a paper entitled ‘The situated HKB model: how sensorimotor spatial coupling can alter oscillatory brain dynamics’. This paper explores the role of a situated and embodied interaction with the world in the patterns of neural oscillation. This is explored in what we call the situated HKB model: a robotic model that performs a gradient climbing task and whose “brain” is modeled by the HKB equation. With this model we can precisely quantify and qualitatively describe how the properties of the system, when studied in coupled conditions, radically change in a manner that cannot be deduced from the decoupled ‘brain’ alone.
Aguilera, M, Bedia, M.G., Santos, B.A., Barandiaran, X.E. (2013). The Situated HKB Model: how sensorimotor spatial coupling can alter oscillatory brain dynamics. Frontiers in Computational Neuroscience 7 (2013): 117. doi:10.3389/fncom.2013.00117.
I just got back from the 12th European Conference on Artificial Life, where a presented two different contributions. As well, I attended to some very interesting contributions. One of them was the ‘Artificial Life in Massive Data Flow‘ workshop, where there was presented a very interesting perspective about analyzing huge amounts of data with vast number of dimensions from a complex systems perspective, beyond the simplifications of ‘Big Data’ perspectives.
My first contribution, Quantifying Political Self-Organization in Social Media. Fractal patterns in the Spanish 15M movement on Twitter, with Ignacion Morer, Xabier Barandiaran and Manuel Bedia, was based in the work we have been doing with the Datanalysis15M research network. We have tried to quantify the levels of political self-organization in Twitter data related with the 15M movement by characterizing bursts of self-organized criticality.
The second contribution, Analysis of ultrastability in small dynamical recurrent neural networks, with Eduardo Izquierdo and Randall Beer, is a very interesting research line Eduardo is developing exploring some issues about implementing Ashby’s Ultrastability in a biologically plausible way. I had the opportunity to collaborate with him for this paper during my research visit at Indiana University.
A few months ago I presented a talk entitled “Reappropriating Ashby’s Ultrastability for the Modelling of Neural Assemblies” at the “Cognition and Consciousness” Retecog Summer School 2012. In the talk I analysed homeostatic models derived from R.W. Ashby’s notion of ultrastability and propose a new model inspired in the ideas of homeokinesis and autopoiesis, where the homeostatic variables are the relations between the components of the system instead of the state of the components themselves. The result is a moving and changing homeostatic area which continuously adapt to the agent’s sensorimotor loop.
I recently obtained new results using just three oscillators as a neural controller of a robot in a phototaxis task, which allows us to watch the homeostatic regulation in 3D. The video shows an agent displaying an auto-regulated preference for two different types of light. This autonomous preference emerges as a result of the coordination of homeostatic regulation and sensorimotor activity.