Background: One popular approach for understanding brain function is by studying electrophysiological recordings from animals. One class of neural recording involves acquiring Local-Field potentials (LFPs), which are transient electrical signals generated in nervous and other tissues by the summed and synchronous electrical activity of the neurons in that tissue. LFPs are typically recorded with a high-impedance microelectrode placed in the midst of the population of cells generating it. For high-resolution recordings (i.e., the ones using multi-electrode arrays or MEAs), the volume of the recorded neural data becomes significant, which makes it difficult to transmit it wirelessly to the outside world. Thus, neuroscientists currently rely on wired setups to transmit this data, which limit the free movement of the animals. To solve this problem, we recently developed a kernel that performs dimensionality reduction on the sparse LFP data in order to efficiently transfer this information to an external setup for offline analysis.
Thesis goal: The goal of this thesis is to port the LFP post-processing kernel to an FPGA and a memristor-based crossbar (ReRAM) in order to achieve small-form-factor dimensionality reduction for brain-machine interfaces (BMIs).
Keywords: FPGA, memristor, brain-machine interface
Prerequisites: Signal processing (basic), hardware design
Miscellaneous: This is topic offered jointly by the Erasmus Medical Center (Neuroscience department) and the Delft University of Technology (Quantum & Computer Engineering department). It capitalizes on the Convergence between the two universities and offers dual working locations (Rotterdam, Delft), access to extended resources and a truly interdisciplinary environment for conducting research.