Melvin Vaupel

Neural spiketrains and population vectors entangle neural representations

The paper introduces methods to disentangle latent variables driving neural activity by identifying bidirectional local matrices in neural data, applied to reveal movement-driven structures in motor cortex.

A topological perspective on the dual nature of the neural state space and the correlation structure

The paper compares neural correlations and state space approaches using topology to infer neural representations, showing their complementary value in characterizing covariate spaces.