Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey.

Objective. Spike sorting is a set of techniques used to analyze extracellular neural recordings, attributing individual spikes to individual neurons. This field has gained significant interest in neuroscience due to advances in implantable microelectrode arrays, capable of recording thousands of neurons simultaneously. High-density electrodes, combined with efficient and accurate spike sorting systems, are essential for various applications, including brain machine interfaces (BMIs), experimental neural prosthetics, real-time neurological disorder monitoring, and neuroscience research. However, given the resource constraints of modern applications, relying solely on algorithmic innovation is not enough. Instead, a co-optimization approach that combines hardware and spike sorting algorithms must be taken to develop neural recording systems suitable for resource-constrained environments, such as wearable devices and BMIs. This co-design requires careful consideration when selecting appropriate spike-sorting algorithms that match specific hardware and use cases.Approach. We investigated the recent literature on spike sorting, both in terms of hardware advancements and algorithms innovations. Moreover, we dedicated special attention to identifying suitable algorithm-hardware combinations, and their respective real-world applicabilities.Main results. In this review, we first examined the current progress in algorithms, and described the recent departure from the conventional '3-step' algorithms in favor of more advanced template matching or machine-learning-based techniques. Next, we explored innovative hardware options, including application-specific integrated circuits, field-programmable gate arrays, and in-memory computing devices (IMCs). Additionally, the challenges and future opportunities for spike sorting are discussed.Significance. This comprehensive review systematically summarizes the latest spike sorting techniques and demonstrates how they enable researchers to overcome traditional obstacles and unlock novel applications. Our goal is for this work to serve as a roadmap for future researchers seeking to identify the most appropriate spike sorting implementations for various experimental settings. By doing so, we aim to facilitate the advancement of this exciting field and promote the development of innovative solutions that drive progress in neural engineering research.

Solution-processed flexible polymer solar cells with silver nanowire electrodes.

The conventional anode for organic photovoltaics (OPVs), indium tin oxide (ITO), is expensive and brittle, and thus is not suitable for use in roll-to-roll manufacturing of OPVs. In this study, fully solution-processed polymer bulk heterojunction (BHJ) solar cells with anodes made from silver nanowires (Ag NWs) have been successfully fabricated with a configuration of Ag NWs/poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/polymer:phenyl-C(61)-butyric acid methyl ester (PCBM)/Ca/Al. Efficiencies of 2.8 and 2.5% are obtained for devices with Ag NW network on glass and on poly(ethylene terephthalate) (PET), respectively. The efficiency of the devices is limited by the low work function of the Ag NWs/PEDOT:PSS film and the non-ideal ohmic contact between the Ag NW anode and the active layer. Compared with devices based on the ITO anode, the open-circuit voltage (V(oc)) of solar cells based on the Ag NW anode is lower by ~0.3 V. More importantly, highly flexible BHJ solar cells have been firstly fabricated on Ag NWs/PET anode with recoverable efficiency of 2.5% under large deformation up to 120°. This study indicates that, with improved engineering of the nanowires/polymer interface, Ag NW electrodes can serve as a low-cost, flexible alternative to ITO, and thereby improve the economic viability and mechanical stability of OPVs.