Online spike sorting via deep contractive autoencoder.

Spike sorting - the process of separating spikes from different neurons - is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets.

Brain-behavior relationships in the perceptual decision-making process through cognitive processing stages.

Perceptual decision making - the process of detecting and categorizing information - has been studied extensively over the last two decades. In this study, we aim to bridge the gap between neural and behavioral representations of the perceptual decision-making process. The neural characterization of decision-making was investigated by evaluating the duration and neural signature of the information processing stages. We further evaluated the processing stages of the decision-making process at the behavioral level by estimating the drift rate and non-decision time parameters. We asked whether the neural and behavioral characterizations of the decision-making process provided consistent results under different stimulus coherency levels and spatial attention. Our statistical analysis revealed that, at both representational levels, decision-making was affected more by the coherency factor. We further found that among different information processing stages, the decision stage had the highest role in the performance of the decision-making process. Such that, the shorter decision stage duration at the neural level and higher drift rate at the behavioral level lead to faster decision-making. Through our consistent neural and behavioral results, we have shown that the decision-making components at these two representational levels were significantly associated. Moreover, the neural signature of the processing stages gave information about the regions that contributed more to the decision-making process. Our overall results demonstrate that uncovering the cognitive processing stages provided more insights into the decision-making process.