摘要： Conscious perception of limb movements depends on proprioceptive neural responses in the somatosensory cortex. In contrast to tactile sensations, proprioceptive cortical coding is barely studied in the mammalian brain and practically non-existent in rodent research. To understand the cortical representation of this important sensory modality we developed a passive forelimb displacement paradigm in behaving mice and also trained them to perceptually discriminate where their limb is moved in space. We delineated the rodent proprioceptive cortex with wide-field calcium imaging and optogenetic silencing experiments during behavior. Our results reveal that proprioception is represented in both sensory and motor cortical areas. In addition, behavioral measurements and responses of layer 2/3 neurons imaged with two-photon microscopy reveal that passive limb movements are both perceived and encoded in the mouse cortex as a spatial direction vector that interfaces the limb with the body’s peripersonal space. 想解決的問題 該研究旨在了解哺乳動物大腦中重要的感覺模式 - 本體感覺的皮質編碼，尤其是在老鼠大腦中的表徵方式和空間感知。 使用的方法 該研究使用了被動性的前肢位移範式，訓練老鼠在空間中感知肢體的移動，並使用廣域鈣成像和光遺傳學靜默實驗來描繪老鼠本體感覺皮質。此外，研究還使用了雙光子顯微鏡成像來觀察第2/3層神經元的行為測量和反應，以揭示被動肢體運動在老鼠大腦皮質中的表徵方式和空間方向向量。 最終的成果

摘要： Deep brain stimulation (DBS) is a therapeutic option for intractable neurological and psychiatric disorders, including Parkinson's disease and major depression. Because of the heterogeneity of brain tissues where electrodes are placed, it has been challenging to elucidate the relevant target cell types or underlying mechanisms of DBS. We used optogenetics and solid-state optics to systematically drive or inhibit an array of distinct circuit elements in freely moving parkinsonian rodents and found that therapeutic effects within the subthalamic nucleus can be accounted for by direct selective stimulation of afferent axons projecting to this region. In addition to providing insight into DBS mechanisms, these results demonstrate an optical approach for dissection of disease circuitry and define the technological toolbox needed for systematic deconstruction of disease circuits by selectively controlling individual components. 想解決的問題 這篇論文想要解決的問題是，如何使用光學技術來系統性地研究帕金森病神經回路，並找出深部腦部刺激治療的相關目標細胞類型或機制。 使用的方法 這篇論文使用了光遺傳學和固態光學的技術，對自由行動的帕金森病老鼠的不同神經回路元素進行系統性的刺激或抑制，並觀察其治療效果。研究人員發現，直接選擇性刺激投射到亞視腦核的傳入軸突可以解釋治療效果。此外，這些結果還展示了一種光學方法，用於解剖疾病神經回路，並定義了系統性拆解疾病回路所需的技術工具箱，通過選擇性地控制個別組件。這些技術的使用可以幫助我們更好地理解DBS的機制，並為系統性解剖疾病回路提供了一種新的方法。 最終的成果

摘要： The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1. 想解決的問題 該論文想解決的問題是機器翻譯模型中常見的復雜架構和長時間訓練時間的問題，提出一種基於注意力機制的簡單網絡架構，並證明其在質量、可並行性和訓練時間方面的優勢。 使用的方法 該論文提出了一種基於注意力機制的簡單網絡架構，稱為Transformer，用於機器翻譯。該模型不使用復雜的循環神經網絡或卷積神經網絡，僅使用注意力機制連接編碼器和解碼器，以實現序列轉換。該模型的訓練時間更短，且可並行化，同時在兩個機器翻譯任務上均取得了比現有最佳模型更優的結果，證明了其在質量和效率方面的優勢。 最終的成果

摘要： Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals—that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations. 想解決的問題 這篇論文想要解決的問題是如何從局部神經電路的部分記錄中推斷循環動力學的本質，並通過對神經殘差動力學的細粒度分析來限制前額葉皮質在決策和眼球運動生成中的可能貢獻。 使用的方法 這篇論文使用了神經殘差動力學的細粒度分析方法，即對於特定任務條件下神經群體平均軌跡周圍的試驗之間變異性進行分析，來克服從神經電路的部分記錄中推斷循環動力學本質所面臨的挑戰。此外，他們還使用了大規模神經記錄和有針對性的因果干擾，以限制前額葉皮質對決策和眼球運動生成的可能貢獻。 最後的成果

摘要： Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural representations1,2,3. In particular, although neural latent embeddings can reveal underlying correlates of behaviour, we lack nonlinear techniques that can explicitly and flexibly leverage joint behaviour and neural data to uncover neural dynamics3,4,5. Here, we fill this gap with a new encoding method, CEBRA, that jointly uses behavioural and neural data in a (supervised) hypothesis- or (self-supervised) discovery-driven manner to produce both consistent and high-performance latent spaces. We show that consistency can be used as a metric for uncovering meaningful differences, and the inferred latents can be used for decoding. We validate its accuracy and demonstrate our tool’s utility for both calcium and electrophysiology datasets, across sensory and motor tasks and in simple or complex behaviours across species.It allows leverage of single- and multi-session datasets for hypothesis testing or can be used label free. Lastly, we show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, for the production of consistent latent spaces across two-photon and Neuropixels data, and can provide rapid, high-accuracy decoding of natural videos from visual cortex. 想解決的問題 這篇論文旨在解決神經科學中的一個基本問題，即如何將行為動作與神經活動相互映射。隨著記錄大量神經和行為數據的能力增強，越來越多的人開始對建模自適應行為期間的神經動力學進行興趣，以探究神經表示。然而，目前缺乏一種能夠明確靈活地利用聯合行為和神經數據來揭示神經動態的非線性技術。因此，本文提出了一種新的編碼方法CEBRA，以聯合使用行為和神經數據的方式來生成一致且高性能的潛在空間，並展示了其在神經科學研究中的應用價值。 使用的方法 本文提出了一種新的編碼方法CEBRA，它以聯合使用行為和神經數據的方式來生成一致且高性能的潛在空間。該方法可以以監督式的假設或自我監督的發現驅動方式進行操作，並可用於解碼。此外，CEBRA還可以用於空間映射、解密自然視覺皮層的自然影片等其他應用。 最後的成果

摘要： Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed in the past – without any prior information on how they interact with the target. Several deep learning methods have been proposed, but they are typically ‘black-box’ models that do not shed light on how they use the full range of inputs present in practical scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) – a novel attention-based architecture that combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, TFT uses recurrent layers for local processing and interpretable self-attention layers for long-term dependencies. TFT utilizes specialized components to select relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of scenarios. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and highlight three practical interpretability use cases of TFT. 想解決的問題 這篇論文想要解決的問題是在多時間預測中，如何有效地處理包含靜態、已知未來和過去時間序列等複雜的輸入，並提供可解釋性的模型來揭示模型如何使用這些輸入。該論文提出了一種名為Temporal Fusion Transformer (TFT)的新型注意力模型，並在多個真實世界數據集上展示了其顯著的性能優勢和可解釋性。 使用的方法 該論文提出了一種名為Temporal Fusion Transformer (TFT)的新型注意力模型，用於多時間預測。TFT使用循環層進行局部處理，使用可解釋的自注意層進行長期依賴關係的學習，並利用專門的組件選擇相關特徵和一系列閘控層抑制不必要的組件，以實現在各種實際情況下的高性能。該論文在多個真實世界數據集上展示了TFT相對於現有基準的顯著性能提升，並突出了TFT的三種實用的可解釋性應用案例。 最後的成果

摘要： Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, complicated by the non-stationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To solve the spike sorting problem, we have continuously developed over the past eight years a framework known as Kilosort. This paper describes the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a new version with substantially improved performance due to new clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework which uses densely sampled electrical fields from real experiments to generate non-stationary spike waveforms and realistic noise. We find that nearly all versions of Kilosort outperform other algorithms on a variety of simulated conditions, and Kilosort4 performs best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions. 想解決的問題 這篇論文想要解決神經科學中的一個重要但困難的問題，即如何從局部電場的記錄中提取單個神經元的發射時間，這個問題被稱為"spike sorting"。這個問題的複雜性來自於記錄的非靜態性和附近神經元之間電場的緊密重疊。 使用的方法 為了解決spike sorting問題，作者們運用了一個連續八年不斷發展的框架，稱為Kilosort。本文描述了Kilosort不同版本中引入的各種算法步驟。作者們還報告了Kilosort4的開發，這是一個新版本，由於受到基於圖形的方法啟發的新聚類算法的影響，其性能有了顯著的改進。為了測試Kilosort的性能，作者們開發了一個逼真的模擬框架，使用了從真實實驗中獲取的密集採樣的電場，生成非靜態的spike波形和逼真的噪聲。結果顯示，Kilosort的幾乎所有版本在各種模擬條件下都優於其他算法，而Kilosort4在所有情況下表現最佳，可以正確識別出在高漂移條件下振幅低且空間範圍小的神經元。 最後的成果

摘要： Loss of dopamine in Parkinson's disease is hypothesized to impede movement by inducing hypo- and hyperactivity in striatal spiny projection neurons (SPNs) of the direct (dSPNs) and indirect (iSPNs) pathways in the basal ganglia, respectively. The opposite imbalance might underlie hyperkinetic abnormalities, such as dyskinesia caused by treatment of Parkinson’s disease with the dopamine precursor L-DOPA. Here we monitored thousands of SPNs in behaving mice, before and after dopamine depletion and during L-DOPA-induced dyskinesia. Normally, intermingled clusters of dSPNs and iSPNs coactivated before movement. Dopamine depletion unbalanced SPN activity rates and disrupted the movement-encoding iSPN clusters. Matching their clinical efficacy, L-DOPA or agonism of the D2 dopamine receptor reversed these abnormalities more effectively than agonism of the D1 dopamine receptor. The opposite pathophysiology arose in L-DOPA-induced dyskinesia, during which iSPNs showed hypoactivity and dSPNs showed unclustered hyperactivity. Therefore, both the spatiotemporal profiles and rates of SPN activity appear crucial to striatal function, and next-generation treatments for basal ganglia disorders should target both facets of striatal activity. 想解決的問題 這篇論文的研究旨在探討帕金森氏症和運動異常的神經機制，並評估特定藥物對這些病症的影響。研究者監測了大量的神經元活動，發現多巴胺缺乏導致基底核直接通路和間接通路神經元活動失衡，而多巴胺前驅物L-DOPA的治療可以逆轉這種失衡，但在某些情況下也會導致運動異常。因此，研究者建議下一代治療基底核疾病的方法應該針對神經元活動的時空特徵和活動率兩個方面。 使用的方法 這篇論文使用了行為學監測和神經元活動監測的方法，研究了帕金森氏症和運動異常的神經機制，以及特定藥物對這些病症的影響。研究者監測了成千上萬個神經元的活動，並比較了多巴胺缺乏、多巴胺前驅物L-DOPA治療和L-DOPA誘導的運動異常狀態下的神經元活動差異。 最後的成果

摘要： Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics. 想解決的問題 這篇論文的主要目的是希望能夠透過新的深度學習方法，從單次神經射頻數據中推斷出潛在的神經元動態，以便更深入地了解神經元群體動態。此方法可以準確預測觀察到的行為變量、提取精確的神經動態單次射頻估計、推斷與行為選擇相關的動態干擾，並結合跨越數月的非重疊記錄會話的數據，以提高對潛在動態的推斷。 使用的方法 這篇論文使用了一種名為"latent factor analysis via dynamical systems"的深度學習方法，以從單次神經射頻數據中推斷出潛在的神經元動態。這種方法通過將單次神經射頻數據轉換為潛在因子空間，並使用動態系統模型來描述因子之間的動態關係，從而推斷出神經元群體的動態。該方法可以準確預測觀察到的行為變量，提取精確的神經動態單次射頻估計，並推斷與行為選擇相關的動態干擾。此外，該方法還可以結合跨越數月的非重疊記錄會話的數據，以提高對潛在動態的推斷。這種方法的優勢在於它可以從單次神經射頻數據中推斷出神經元群體動態，這對於更深入地了解神經元群體動態和行為之間的關係具有重要意義。 最後的成果