Policy entrepreneurship in the development of public sector strategy: the case of London health reform

The development of health policy is recognized as complex; however, there has been little development of the role of agency in this process. Kingdon developed the concept of policy entrepreneur (PE) within his ‘windows’ model. He argued inter-related ‘policy streams' must coincide for important issues to become addressed. The conjoining of these streams may be aided by a policy entrepreneur. We contribute by clarifying the role of the policy entrepreneur and highlighting the translational processes of key actors in creating and aligning policy windows. We analyse the work in London of Professor Sir Ara Darzi as a policy entrepreneur. An important aspect of Darzi's approach was to align a number of important institutional networks to conjoin related problems. Our findings highlight how a policy entrepreneur not only opens policy windows but also yokes together a network to make policy agendas happen. Our contribution reveals the role of clinical leadership in health reform.

Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups.

To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation.

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.