Lessons Learned from 55 (or More) Years of Professional Experience in Urban Planning and Development

Reflecting on the many debates over the years on changing urbanization processes, on the towns and cities of yesterday, today, and tomorrow, the main challenge will be listening to lessons of wisdom from the past and adapting these to our future professional work. When Chief Seattle said that the Earth does not belong to us, we belong to the Earth, he called for more humility and respect so as to plan for the needs of today and tomorrow, and not for the greed of a few. The doomsday scenarios of overpopulation only make sense if we continue to exploit our planet the way we do today, as if we have an infinite reservoir of resources. Already back in the 1960s, Barbara Ward, John F. C. Turner, and particularly Kenneth Boulding taught me to rethink our whole perception of Spaceship Earth. I have seen many towns and cities grow as if resources were limitless; I myself have seen and worked on efforts to focus on spatial quality, respecting nature whenever possible for a growing number of people, recognizing resources as being precious and scarce, and yet guaranteeing equitable access to a good quality of urban life. Such objectives are not evident, when models in education, schools of thought, professional planners, and greedy developers are often geared towards the contrary: the higher the skyscrapers, the better; the more egotripping by architects, the more the rich like it; the more people are stimulated to consume, the better the world will be. Such narrow visions will no longer help. At several global urban planning and developments events (1976, 1992, 1996, 2016, etc.), new ideas and agendas have been put forward. Whether the present Covid-19 crisis may induce a more rapid change in vision and practice is still too early to confirm, but luckily, several towns and cities, and a few visionary planners and decision makers are showing some promising examples

A Model of the Ventral Visual System Based on Temporal Stability and Local Memory

The cerebral cortex is a remarkably homogeneous structure suggesting a rather generic computational machinery. Indeed, under a variety of conditions, functions attributed to specialized areas can be supported by other regions. However, a host of studies have laid out an ever more detailed map of functional cortical areas. This leaves us with the puzzle of whether different cortical areas are intrinsically specialized, or whether they differ mostly by their position in the processing hierarchy and their inputs but apply the same computational principles. Here we show that the computational principle of optimal stability of sensory representations combined with local memory gives rise to a hierarchy of processing stages resembling the ventral visual pathway when it is exposed to continuous natural stimuli. Early processing stages show receptive fields similar to those observed in the primary visual cortex. Subsequent stages are selective for increasingly complex configurations of local features, as observed in higher visual areas. The last stage of the model displays place fields as observed in entorhinal cortex and hippocampus. The results suggest that functionally heterogeneous cortical areas can be generated by only a few computational principles and highlight the importance of the variability of the input signals in forming functional specialization

Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality

What does the informational complexity of dynamical networked systems tell us about intrinsic mechanisms and functions of these complex systems? Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions. While several numerical schemes for estimating network integrated information exist, it is instructive to pursue an analytic approach that computes integrated information as a function of network weights. Our formulation of integrated information uses a Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Implementing stochastic Gaussian dynamics, we perform computations for several prototypical network topologies. Our findings show increased informational complexity near criticality, which remains consistent across network topologies. Spectral decomposition of the system's dynamics reveals how informational complexity is governed by eigenmodes of both, the network's covariance and adjacency matrices. We find that as the dynamics of the system approach criticality, high integrated information is exclusively driven by the eigenmode corresponding to the leading eigenvalue of the covariance matrix, while sub-leading modes get suppressed. The implication of this result is that it might be favorable for complex dynamical networked systems such as the human brain or communication systems to operate near criticality so that efficient information integration might be achieved

Robot regulatory behaviour based on fundamental homeostatic and allostatic principles

Animals in their ecological context behave not only in response to external events, such as opportunities and threats but also according to their internal needs. As a result, the survival of the organism is achieved through regulatory behaviour. Although homeostatic and allostatic principles play an important role in such behaviour, how an animal's brain implements these principles is not fully understood yet. In this paper, we propose a new model of regulatory behaviour inspired by the functioning of the medial Reticular Formation (mRF). This structure is spread throughout the brainstem and has shown generalized Central Nervous System (CNS) arousal control and fundamental action-selection properties. We propose that a model based on the mRF allows the flexibility needed to be implemented in diverse domains, while it would allow integration of other components such as place cells to enrich the agent's performance. Such a model will be implemented in a mobile robot that will navigate replicating the behaviour of the sand-diving lizard, a benchmark for regulatory behaviour. © 2020 Elsevier B.V.. All rights reserved

Neuromuse: training your brain through musical interaction

Presented at the 15th International Conference on Auditory Display (ICAD2009), Copenhagen, Denmark, May 18-22, 2009Human aural system is arguably one of the most refined sensor we posess. It is sensitive to such highly complex stimuli as conversa- tions or musical pieces. Be it a speaking voice or a band playing live, we are able to easily perceive relaxed or agitated states in an auditory stream. In turn, our own state of agitation can now be detected via electroencephalography technologies. In this pa- per we propose to explore both ideas in the form of a framework for conscious learning of relaxation through sonic feedback. Af- ter presenting the general paradigm of neurofeedback, we describe a set of tools to analyze electroencephalogram (EEG) data in real- time and we introduce a carefully designed, perceptually-grounded interactive music feedback system that helps the listener keeping track of and modulate her agitation state as measured by EEG