Mohammad Reza Keshtkaran

TL;DR: A robust and computationally efficient algorithm for removing power line interference from neural recordings, which features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment.

TL;DR: Zrezak et al. as discussed by the authors presented a robust and computationally efficient algorithm for removing power line interference from neural recordings, which includes four steps: first, an adaptive notch filter is used to estimate the fundamental frequency of the interference.

TL;DR: This study predicts the coexistence of two components embedded in neural data dynamics, one in the exponential form (noise) and the other in the power form (neural spikes), which has been confirmed in experiments of in vivo sequences recorded from the hippocampus, cortex surface, and spinal cord.

TL;DR: Comparative results demonstrate that the proposed noise-robust and unsupervised spike sorting algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets.

TL;DR: In this article, a large-scale, automated model-tuning framework that can characterize dynamics in diverse brain areas without regard to behavior is presented. But, applying such methods to less-structured behaviors, or in brain areas that are not well-modeled by autonomous dynamics, is far more challenging, because deep learning methods often require careful hand-tuning of complex model hyperparameters (HPs).