Blending adaptive governance and institutional theory to explore urban resilience and sustainability strategies in the Rome Metropolitan Area, Italy

Adaptive governance is an emerging theory in natural resource management. This paper addresses a gap in the literature by exploring the potential of adaptive governance for delivering resilience and sustainability in the urban context. We explore emerging challenges to transitioning to urban resilience and sustainability: bringing together multiple scales and institutions; facilitating a social-ecological-systems approach and; embedding social and environmental equity into visions of urban sustainability and resilience. Current approaches to adaptive governance could be helpful for addressing these first two challenges but not in addressing the third. Therefore, this paper proposes strengthening the institutional foundations of adaptive governance by engaging with institutional theory. We explore this through empirical research in the Rome Metropolitan Area, Italy. We argue that explicitly engaging with these themes could lead to a more substantive urban transition strategy and contribute to adaptive governance theory.

Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

Renaturing cities using a regionally-focused biodiversity-led multifunctional benefits approach to urban green infrastructure

If a ‘Renaturing of Cities’ strategy is to maximise the ecosystem service provision of urban green infrastructure (UGI), then detailed consideration of a habitat services, biodiversity-led approach and multifunctionality are necessary rather than relying on the assumed benefits of UGI per se. The paper presents preliminary data from three case studies, two in England and one in Germany, that explore how multifunctionality can be achieved, the stakeholders required, the usefulness of an experimental approach for demonstrating transformation, and how this can be fed back into policy. We argue that incorporating locally contextualised biodiversity-led UGI design into the planning and policy spheres contributes to the functioning and resilience of the city and provides the adaptability to respond to locally contextualised challenges, such as overheating, flooding, air pollution, health and wellbeing as well as biodiversity loss. Framing our research to encompass both the science of biodiversity-led UGI and co-developing methods for incorporating a strategic approach to implementation of biodiversity-led UGI by planners and developers addresses a gap in current knowledge and begins to address barriers to UGI implementation. By combining scientific with policy learning and defined urban environmental targets with community needs, our research to date has begun to demonstrate how nature-based solutions to building resilience and adaptive governance can be strategically incorporated within cities through UGI.