Gedistribueerde signaalverwerkingsalgoritmen voor identificatie van actiepotentialen in volgende-generatie hoge-densiteit neuroprobes

Neurons communicate with each other through action potentials or so-called ‘spikes’. When an electrode is inserted into the brain, it records spikes of all the neurons in its close vicinity. To decode brain processes, all these spikes have to be sorted according to their underlying neuronal source, aided by so-called 'spike sorting' (SS) algorithms. Recent advances in silicon technology have paved the way for neuroprobes with high-density (HD) electrode grids. These HD grids provide more spatial information, but it is unclear how -and to what extent- this can be exploited by SS algorithms. Furthermore, the full exploitation of HD electrode grids is hampered due to several fundamental hardware (HW) and software (SW) limitations. On the
HW side, bandwidth and wiring constraints make it impossible to extract all electrode signals. On the SW side, the standard machine-learning algorithms for SS are not designed to (optimally) exploit spatial information, and their computational complexity scales poorly with the number of channels.
Therefore, this project aims to
1) explore the added value and fundamental limits of HD electrode grids based on physiological models,
2) develop novel SS algorithms that optimally exploit this spatial information, and
3) overcome the current HW/SW limitations based on distributed signal processing and distributed probe architectures.
If successful, the project could elicit a fundamental paradigm shift in the design of HD neurorecording technology.