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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. • A novel algorithm for spike sorting based on a Contractive Auto-encoder. • Produce representations of spike waveforms that are robust to additive noise. • Reliably classify spikes for small and large datasets. • Outperform SOTA approaches in various online and offline spike-sorting applications. [ABSTRACT FROM AUTHOR]

A fundamental component to managing a marketing campaign is identifying prospects and selection of leads. Current lead generation models focus on predicting the intention of a customer to purchase a product, however with financial products, particularly loans, this can be insufficient as there are many factors to consider, such as risk, utility, and financial maturity. Developing a marketing campaign for loan prospecting should consider not only customers who need a loan, but rather clients who need a loan and will also be approved. Otherwise, the marketing effort is deemed ineffective, if the lead cannot be converted into a sale. Although, a low response rate is expected for a marketing campaign for loans, we highlight a better approach in managing resources while maintaining a shortlist of high-quality leads. This manuscript introduces a bi-level approach to handle the complex nature of loan products. Two classifiers are built, one modelling loan intention and the other one modelling loan eligibility. We adopt convex combination to control weights of both problems, which in most cases results in an improved performance for identifying future successful loan applicants when compared to baseline models. Rank-based evaluation measures are also adopted to explore the performance of customer rankings. We find that soft classifiers, such as deep learning techniques, are ideal for ranking customers, achieving a superior performance when compared to other machine learning techniques. In addition, we conclude that an ideal cut-off for K customers is estimated to be between 20 to 25 customers, however our best model can maintain an Average Precision of greater than 0.85 when K approaches 50. • Lead generation for loans presents a challenging classification problem. • Existing methods for predicting leads have lacked consideration of loan approval. • Combining binary classifiers with varying objectives can improve model accuracy. • Rank-based measures are preferred for evaluating the quality of predicted leads. • Deep learning models are ideal for lead selection when ranking is considered. [ABSTRACT FROM AUTHOR]