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Advances in data science and artificial intelligence (AI) offer unprecedented opportunities to provide actionable insights, drive innovative solutions, and create long-term strategies for sustainable development in response to the triple existential crises facing humanity: climate change, pollution, and biodiversity loss. The rapid development of AI models has been the subject of extensive debate and is high on the political agenda, but at present the vast potential for AI to contribute positively to informed decision making, improved environmental risk management, and the development of technological solutions to sustainability challenges remains underdeveloped. In this paper, we consider four inter-dependent areas in which data science and AI can make a substantial contribution to developing sustainable future interactions with the environment: (i) quantification and tracking progress towards the United Nations Sustainable Development Goals; (ii) embedding AI technologies to reduce emissions at source; (iii) developing systems to increase our resilience to natural hazards; (iv) Net Zero and the built environment. We also consider the wider challenges associated with the widespread use of AI, including data access and discoverability, trust and regulation, inference and decision making, and the sustainable use of AI. 

Spatially distributed excitation and inhibition collectively shape a visual neuron’s receptive field (RF) properties. In the direction-selective circuit of the mammalian retina, the role of strong null-direction inhibition of On-Off direction-selective ganglion cells (On-Off DSGCs) on their direction selectivity is well-studied. However, how excitatory inputs influence the On-Off DSGC’s visual response is underexplored. Here, we report that On-Off DSGCs have a spatially displaced glutamatergic receptive field along their horizontal preferred-null motion axes. This displaced receptive field contributes to DSGC null-direction spiking during interrupted motion trajectories. Theoretical analyses indicate that population responses during interrupted motion may help populations of On-Off DSGCs signal the spatial location of moving objects in complex, naturalistic visual environments. Our study highlights that the direction-selective circuit exploits separate sets of mechanisms under different stimulus conditions, and these mechanisms may help encode multiple visual features.