Quantifying Morphological Computation

The field of embodied intelligence emphasises the importance of the morphology and environment with respect to the behaviour of a cognitive system. The contribution of the morphology to the behaviour, commonly known as morphological computation, is well-recognised in this community. We believe that the field would benefit from a formalisation of this concept as we would like to ask how much the morphology and the environment contribute to an embodied agent's behaviour, or how an embodied agent can maximise the exploitation of its morphology within its environment. In this work we derive two concepts of measuring morphological computation, and we discuss their relation to the Information Bottleneck Method. The first concepts asks how much the world contributes to the overall behaviour and the second concept asks how much the agent's action contributes to a behaviour. Various measures are derived from the concepts and validated in two experiments which highlight their strengths and weaknesses

Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping

In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be performed by the brain. Exploiting environmental and morphological properties is an important feature of embodied systems. The main reason is that it allows to significantly reduce the controller complexity. An important aspect of morphological computation is that it cannot be assigned to an embodied system per se, but that it is, as we show, behavior- and state-dependent. In this work, we evaluate two different measures of morphological computation that can be applied in robotic systems and in computer simulations of biological movement. As an example, these measures were evaluated on muscle and DC-motor driven hopping models. We show that a state-dependent analysis of the hopping behaviors provides additional insights that cannot be gained from the averaged measures alone. This work includes algorithms and computer code for the measures.Comment: 10 pages, 4 figures, 1 table, 5 algorithm

Computing the Unique Information

Given a pair of predictor variables and a response variable, how much information do the predictors have about the response, and how is this information distributed between unique, redundant, and synergistic components? Recent work has proposed to quantify the unique component of the decomposition as the minimum value of the conditional mutual information over a constrained set of information channels. We present an efficient iterative divergence minimization algorithm to solve this optimization problem with convergence guarantees and evaluate its performance against other techniques.Comment: To appear in 2018 IEEE International Symposium on Information Theory (ISIT); 18 pages; 4 figures, 1 Table; Github link to source code: https://github.com/infodeco/computeU

Quantifying galaxy morphology

How do the different shapes of galaxies arise? Milena Pawlik describes work to identify the role of galaxy mergers and starbursts in galactic evolution.Publisher PDFPeer reviewe

Bars in early- and late-type disks in COSMOS

We investigate the (large-scale) bar fraction in a mass-complete sample of M > 10^10.5 Msun disk galaxies at 0.2 < z < 0.6 in the COSMOS field. The fraction of barred disks strongly depends on mass, disk morphology, and specific star formation rate (SSFR). At intermediate stellar mass (10^10.5 < M < 10^11 Msun) the bar fraction in early-type disks is much higher, at all redshifts, by a factor ~2, than that in late-type disks. This trend is reversed at higher stellar mass (M > 10^11 Msun), where the fraction of bars in early-type disks becomes significantly lower, at all redshifts, than that in late-type disks. The bar fractions for galaxies with low and high SSFRs closely follow those of the morphologically-selected early-type and late-type populations, respectively. This indicates a close correspondence between morphology and SSFR in disk galaxies at these earlier epochs. Interestingly, the total bar fraction in 10^10.5 < M < 10^11 Msun disks is built up by a factor of ~2 over the redshift interval explored, while for M > 10^11 Msun disks it remains roughly constant. This indicates that, already by z ~ 0.6, spectral and morphological transformations in the most massive disk galaxies have largely converged to the familiar Hubble sequence that we observe in the local Universe, while for intermediate mass disks this convergence is ongoing until at least z ~ 0.2. Moreover, these results highlight the importance of employing mass-limited samples for quantifying the evolution of barred galaxies. Finally, the evolution of the barred galaxy populations investigated does not depend on the large-scale environmental density (at least, on the scales which can be probed with the available photometric redshifts).Comment: 10 pages, 4 figures, updated to reflect version accepted by MNRA