Current advances in artificial intelligence (AI) are supported by artificial neural networks (ANNs). Such networks are inspired by the connectivity in biological networks, but communicate using real valued mathematical functions. By contrast, real biological networks are known to communicate using short electrical bursts, orspikes. Networks constructed to emulate this mechanism are known as spiking neural networks (SNNs). SNNs have arisen from the neuroscience community as a means to understand the function of the brain, and as tempting solutions to physiological functions such as speech and image processing. They have also been addressed by the neuromorphic community, hoping to build circuits that mimic the brain whilst also being powerefficient.MORPhyN arises from work at Idiap building on these concepts applied to speech processing, showing that SNNs can be freely mixed with ANNs. We have found that SNNs present two distinct mathematical problems that have not been fully addressed. The first concerns the large number of hyperparameters; it is beyond what can be reasonably handled by hand. The second is the tendency for the training to get stuck due to the binary activation function.Bayesian optimisation (BO) presents an attractive solution to the hyperparameter problem in SNNs. It is a principled approach to searching the multi-dimensional hyperparameter space that has already shown promise in enhancing the performance and efficiency of ANNs.However, spike-based learning rules and discrete events introduce challenges in modelling and optimising parameters. Research is required to address these challenges and, e.g., to determine the most suitable Gaussian process kernels for effectively modelling non-stationarity within SNNs.The core of MORPhyN is built around the application of the BO expertise from Mahidol to the SNNs for which expertise exists at Idiap.In addition, the Idiap side brings expertise in Bayesian learning, which promises to address the parameters themselves to smooth the loss landscape associated with the binary nature of SNNs. The Mahidol side also brings expertise in the vehicle routing problem, a classical NP-hard application, essentially a generalisation of the travelling salesman problem. ANNs have been shown to be good heuristic solutions to routing via their graph processing abilities; SNNs promise to improve on this.In MORPhyN, we propose to combine the above four techniques in three threads representing three objectives:1. In a core collaborative thread, we aim to enable BO for SNNs. By better handling the hyperparameters, we aim advance the state of the art in both fields, giving SNNs the same or better performance compared to current ANNs.2. In an application oriented thread, researchers primarily at Mahidol will enable the use of both ANNs and SNNs in the context of vehicle routing. Routing is more analytic than speech recognition, promising deeper insight into SNN function. A more general aim will be to show that SNNs are beneficial for applications without the physiological bases of speech and image processing.3. In a more theoretical thread, researchers primarily at Idiap will apply Bayesian learning techniques to SNNs to both smooth out error landscapes and better interface to BO. This will yield not only better SNN performance, but also a complete theory of their operation.Overall, we aim to put SNNs on a par with ANNs in terms of utility and accessibility, allowing the community to freely choose between ANN and SNN architectures depending on the application. This in turn will not only allow new applications being particularly suited to SNNs, but will allow more energy efficient implementation of current applications on neuromorphic hardware.In addition to being complementary in terms of technical capabilities, the partners take advantage of the SPIRIT program to address gender equality. Whilst Idiap, as an ICT-focused institute, suffers from male bias, the partnership with Mahidol brings the opportunity of a female PI and a more balanced student cohort.