Neural engineering

…challenges in neurotechnology, neural engineering, bioelectronics and biological interfaces. Invited talks cover materials and devices for neuroelectronics and bioinspired materials for tissue engineering. Editor panels, an IDEA League focus session and posters provide opportunities to compare approaches and develop collaborations. A poster award is sponsored by Advanced Electronic Materials. In-person at the Central Library, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany. Starts Novem

Neural Interfaces brings together researchers, clinicians, engineers and other stakeholders working on neurotechnology, brain-computer interfaces, neuroprosthetics and neuromodulation. The programme is developed through community proposals and original research abstracts. Its scope spans clinical applications, neuroscience and neural engineering, data and AI, device technology, translation and outcomes, and ethics, policy and workforce questions. The meeting connects foundational research with implementation and adoption of neural technologies. The speaker-proposal deadline was 12 October 2026

…increasingly common abstraction is the neural ‘factor’. Factors are ce

EEG has several applications in neuroscience, neural engineering, and diagnosis of neurological disorders. It records the electrical activity of the brain tissues resulting from the interaction of distinct neuronal populations. Various methods have been developed for the automatic classification of EEG signals. These algorithms operate by acquiring unique and non-redundant features from EEG data. However, a large portion of the proposed algorithms usually use temporal features while ignoring the dense spatial network structure generating the EEG. We propose a classification pipeline that utili

The Neural Engineering Framework has been used to create a wide variety of biologically realistic brain simulations that are capable of performing simple cognitive tasks (remembering a list, counting, etc.). This includes the largest existing functional brain model. This talk will describe this method, and show some examples of using it to take high-level cognitive algorithms and convert them into a neural network that implements those algorithms. Overall, this approach gives us new ways of thinking about how the brain works and what sorts of algorithms it is capable of performing.