dynamical approach

ENSO 24 — Harry Heft — Places as Emergent Dynamic Structures in Everyday Life

The 2018-2019 ENSO season continues next week with Harry Heft, from Denison University, on “Places as Emergent Dynamic Structures in Everyday Life”.

 3pm UTC, on Thursday 8th November

The details of the talk, including the time in your own timezone (watch the daylight savings change!), can be found on the ENSO webpage.

As ever, if you would like to join us in the live session to participate in the discussion you would be welcome to do so. If you are interested in doing so, please send an email to marek.mcgann@gmail.com, and we will send an invitation link to the YouTube Live session when things kick off. We welcome all researchers with an interest in participating.

The opportunity for discussion will continue on the ENSO webpage after the talk also. (more…)


21st ENSO Seminar: TONY CHEMERO. Radical Embodiment and Real Cognition

The 21st ENSO Seminar will be presented by Tony Chemero!

Next week, Thursday 8th March @ 3pm UTC, Tony will present on Radical Embodiment and Real Cognition. The details of the talk, including the time in your own timezone, can be found on the ENSO webpage.


A persistent criticism of radical embodied cognitive science is that it will be impossible to explain “real cognition” without invoking mental representations. This talk will provide an account of explicit, real-time thinking of the kind we engage in when we imagine counter-factual situations, remember the past, and plan for the future. We will first present a very general non-representational account of explicit thinking, based on pragmatist philosophy of science. Then we will present a more detailed instantiation of this general account drawing on nonlinear dynamics and ecological psychology. This talk is based on a paper co-authored with Gui Sanches de Oliveira and Vicente Raja.



ALIFE XV Late Breaking Abstract

Can we incorporate sleep-like interruptions into evolutionary robotics?

Mario A. Zarco-Lopez and Tom Froese

Traditional use of Hopfield networks can be divided into two main categories: (1) constraint satisfaction based on predefined a weight space, and (2) model induction based on a training set of patterns. Recently, Watson et al. (2011) have demonstrated that combining these two aspects, i.e. by inducing a model of the network’s attractors by applying Hebbian learning after constraint satisfaction, can lead to self-optimization of network connectivity. A key element of their approach is a repeated randomized reset and relaxation of network state, which has been interpreted as similar to the function of sleep (Woodward, Froese, & Ikegami, 2015). This perspective might give rise to an alternative “wake-sleep” algorithm (Hinton, Dayan, Frey, & Neal, 1995). All of this research, however, has taken place with isolated artificial neural networks, which goes against decades of work on situated robotics (Cliff, 1991). We consider the challenges involved in extending this work on sleep-like self-optimization to the dynamical approach to cognition, in which behavior is seen as emerging from the interactions of brain, body and environment (Beer, 2000).

Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91-99.

Cliff, D. (1991). Computational neuroethology: A provisional manifesto. In J.-A. Meyer & S. W. Wilson (Eds.), From Animals to Animats (pp. 29-39). MIT Press.

Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The “wake-sleep” algorithm for unsupervised neural networks. Science, 268, 1158-1161.

Watson, R. A., Buckley, C. L., & Mills, R. (2011). Optimization in “self-modeling” complex adaptive systems. Complexity, 16(5), 17-26.

Woodward, A., Froese, T., & Ikegami, T. (2015). Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model. Neural Networks, 62, 39-46.