We introduce Norse: An open-source library for gradient-based training of spiking neural networks. In contrast to neuron simulators which mainly target computational neuroscientists, our library seamlessly integrates with the existing PyTorch ecosystem using abstractions familiar to the machine learning community. This has immediate benefits in that it provides a familiar interface, hardware accelerator support and, most importantly, the ability to use gradient-based optimization. While many parallel efforts in this direction exist, Norse emphasizes flexibility and usability in three ways. Users can conveniently specify feed-forward (convolutional) architectures, as well as arbitrarily connected recurrent networks. We strictly adhere to a functional and class-based API such that neuron primitives and, for example, plasticity rules composes. Finally, the functional core API ensures compatibility with the PyTorch JIT and ONNX infrastructure. We have made progress to support network execution on the SpiNNaker platform and plan to support other neuromorphic architectures in the future. While the library is useful in its present state, it also has limitations we will address in ongoing work. In particular, we aim to implement event-based gradient computation, using the EventProp algorithm, which will allow us to support sparse event-based data efficiently, as well as work towards support of more complex neuron models. With this library, we hope to contribute to a joint future of computational neuroscience and neuromorphic computing.

Alzheimer's disease (AD) is the most common cause of dementia, and its health and socioeconomic burdens are of major concern. Presently, a definite diagnosis of AD is established by examining brain tissue after death. These examinations focus on two major pathological hallmarks of AD in the brain: (i) amyloid plaques consisting of aggregated amyloid beta (Aβ) peptides and (ii) neurofibrillary tangles made of abnormally phosphorylated tau protein. In living individuals, AD diagnosis relies on two main approaches: (i) brain imaging of tau tangles and Aβ plaques using a technique called positron emission tomography (PET) and (ii) measuring biochemical changes in tau (including phosphorylated tau at threonine-181 [p-tau181]) and the Aβ42 peptide metabolized into CSF. Unlike Aβ42, CSF p-tau181 is highly specific for AD but its usability is restricted by the need of a lumbar puncture. Moreover, PET imaging is expensive and only available in specialised medical centres. Due to these shortcomings, a simple blood test that can detect disease-related changes in the brain is a high priority for AD research, clinical care and therapy testing. In this webinar, I will discuss the discovery of p-tau biomarkers in blood and the biochemistry of how these markers differ from those found in CSF. Furthermore, I will critically review the performance of blood p-tau biomarkers across the AD pathological process and how they associate with and predict Aβ and tau pathophysiological and neuropathological changes. Furthermore, I will evaluate the potential advantages, challenges and context of use of blood p-tau in clinical practice, therapeutic trials and population screening.

By studying the neural correlates of hand augmentation, we are exploring the boundaries of neuroplasticity seeing how it can be harnessed to improve the usability and control of prosthetic devices. Tamar Makin and Dani Clode each discuss their research and perspectives within the field of prosthetics that has led to this unique collaboration and exploration of motor augmentation and the brain.