A.1 Power Flow Problem Recall the power balance equations:

New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds to thousands of channels. Interpreting these recordings requires scalable and accurate automated methods for spike sorting, which should minimize the time required for manual curation of the results. Here we introduce KiloSort, a new integrated spike sorting framework that uses template matching both during spike detection and during spike clustering. KiloSort models the electrical voltage as a sum of template waveforms triggered on the spike times, which allows overlapping spikes to be identified and resolved. Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms. Processing times are faster than in previous algorithms thanks to batch-based optimization on GPUs. We compare KiloSort to an established algorithm and show favorable performance, at much reduced processing times. A novel post-clustering merging step based on the continuity of the templates further reduced substantially the number of manual operations required on this data, for the neurons with nearzero error rates, paving the way for fully automated spike sorting of multichannel electrode recordings.

Energy-efficient computing is of primary importance to the effective deployment of deep neural networks (DNNs), particularly in edge devices and in on-chip AI systems. Increasing DNN computation's energy efficiency and lowering its carbon footprint require iterative efforts from both chip designers and algorithm developers.

Recent progress in the development of voltage indicators [1-8] has brought us closer to a longstanding goal incellular neuroscience: imaging the full spatiotemporal voltageonadendritic tree. These recordings have the potential (pun not intended) to resolve fundamental questions about the computations performed by dendrites -- questions that have remained open for more than a century[9,10].

Archaeologists say they located a Black Death burial site containing some of a village's 12,000 dead. Breakthroughs, discoveries, and DIY tips sent six days a week. The Black Death () killed as much as half of Europe's total population between 1346 and 1353, so there are a of bodies buried across the continent. For example, contemporary accounts from Thuringia--a state in central Germany--report that about 12,000 plague victims died around Erfurt amid the city's outbreak in 1350. But despite multiple accounts attesting to this devastation, none of the 11 mass graves could be pinpointed for centuries.

Recent advances in optical voltage sensors have brought us closer to a critical goal in cellular neuroscience: imaging the full spatiotemporal voltage on a dendritic tree. However, current sensors and imaging approaches still face significant limitations in SNR and sampling frequency; therefore statistical denoising and interpolation methods remain critical for understanding single-trial spatiotemporal dendritic voltage dynamics. Previous denoising approaches were either based on an inadequate linear voltage model or scaled poorly to large trees. Here we introduce a scalable fully Bayesian approach. We develop a generative nonlinear model that requires few parameters per compartment of the cell but is nonetheless flexible enough to sample realistic spatiotemporal data. The model captures different dynamics in each compartment and leverages biophysical knowledge to constrain intra-and inter-compartmental dynamics. We obtain a full posterior distribution over spatiotemporal voltage via an augmented Gibbs sampling algorithm. The nonlinear smoother model outperforms previously developed linear methods, and scales to much larger systems than previous methods based on sequential Monte Carlo approaches.

Spiking Neural Networks (SNNs) as Machine Learning (ML) models have recently received a lot of attention as a potentially more energy-efficient alternative to conventional Artificial Neural Networks. The non-differentiability and sparsity of the spiking mechanism can make these models very difficult to train with algorithms based on propagating gradients through the spiking non-linearity. We address this problem by adapting the paradigm of Random Feature Methods (RFMs) from Artificial Neural Networks (ANNs) to Spike Response Model (SRM) SNNs. This approach allows training of SNNs without approximation of the spike function gradient. Concretely, we propose a novel data-driven, fast, high-performance, and interpretable algorithm for end-to-end training of SNNs inspired by the SWIM algorithm for RFM-ANNs, which we coin S-SWIM. We provide a thorough theoretical discussion and supplementary numerical experiments showing that S-SWIM can reach high accuracies on time series forecasting as a standalone strategy and serve as an effective initialisation strategy before gradient-based training. Additional ablation studies show that our proposed method performs better than random sampling of network weights.