Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics. However, the sheer volume of data and its dynamical complexity are critical barriers to uncovering and interpreting these dynamics. Deep learning methods are a promising approach due to their ability to uncover meaningful relationships from large, complex, and noisy datasets. When applied to high-D spiking data from motor cortex (M1) during stereotyped behaviors, they offer improvements in the ability to uncover dynamics and their relation to subjects’ behaviors on a millisecond timescale. However, 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). Here we demonstrate AutoLFADS, a large-scale, automated model-tuning framework that can characterize dynamics in diverse brain areas without regard to behavior. AutoLFADS uses distributed computing to train dozens of models simultaneously while using evolutionary algorithms to tune HPs in a completely unsupervised way. This enables accurate inference of dynamics out-of-the-box on a variety of datasets, including data from M1 during stereotyped and free-paced reaching, somatosensory cortex during reaching with perturbations, and frontal cortex during cognitive timing tasks. We present a cloud software package and comprehensive tutorials that enable new users to apply the method without needing dedicated computing resources.

By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Abstract.Objective Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated through using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results The algorithm does not require any reference signal, and can track the frequency, phase, and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed, and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (< 100 ms) and substantial interference rejection (output SNR > 30 dB) in different conditions of interference strengths (input SNR from −30 dB to 30 dB), power line frequencies (45-65 Hz), and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power, and the sampling rate. A prototype was fabricated in a 65-nm CMOS process and tested. The MATLAB implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement, and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.