The mammalian neocortex is characterized by a variety of neuronal cell types and precise arrangements of synaptic connections, but the processes that generate this diversity are poorly understood. Here we examine how a pool of embryonic progenitor cells consisting of apical intermediate progenitors (aIPs) contribute to diversity within the upper layers of mouse cortex. In utero labeling combined with single-cell RNA-sequencing reveals that aIPs can generate transcriptionally defined glutamatergic cell types, when compared to neighboring neurons born from other embryonic progenitor pools. Whilst sharing layer-associated morphological and functional properties, simultaneous patch clamp recordings and optogenetic studies reveal that aIP-derived neurons exhibit systematic biases in both their intralaminar monosynaptic connectivity and the post-synaptic partners that they target within deeper layers of cortex. Multiple cortical progenitor pools therefore represent an important factor in establishing diversity amongst local and long-range fine-scale glutamatergic connectivity, which generates subnetworks for routing excitatory synaptic information.

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With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery at every step of the scientific method. Perhaps their most valuable application lies in the speeding up of what has traditionally been the slowest and most challenging step of coming up with a hypothesis. Powerful representations are now being learned from large volumes of data to generate novel hypotheses, which is making a big impact on scientific discovery applications ranging from material design to drug discovery. The GT4SD [Team, 2022] (https://github.com/GT4SD/gt4sd-core) is an extensible open-source library that enables scientists, developers and researchers to train and use state-of-the-art generative models for hypothesis generation in scientific discovery. GT4SD supports a variety of uses of generative models across material science and drug discovery, including molecule discovery and design based on properties related to target proteins, omic profiles, scaffold distances, binding energies and more.Keywords Generative Models • Scientific Discovery • Accelerated Discovery • Open Source Humanity's progress has been characterised by a delicate balance between curiosity and creativity. Science is no exception with its long evolution through trial and error. While remarkably successful, the scientific method can be a slow iterative process that can be inadequate when faced with important and pressing needs, e.g., the need to swiftly develop drugs and antibiotics or design novel materials and processes to mitigate climate change effects. Indeed, it can take almost a decade to discover a new material and cost upwards of $10-$100 million. One of the most daunting challenges in materials discovery is hypothesis generation, where it is extremely challenging to identify and select novel and useful candidates in search spaces that are overwhelming in size, e.g., the chemical space for drug-like molecules is estimated to contain 10 33 structures [Polishchuk et al., 2013].To overcome this problem, in recent years, generative models have emerged as an effective approach to design and discover molecules with desired properties. Generative models more efficiently and effectively navigate and explore vast search spaces that are learned from data based on user-defined criteria. Starting from a series of seminal works [Gómez-

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