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論文分析：Optical Deconstruction of Parkinsonian Neural Circuitry

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

這篇論文的最終成果是成功利用光學技術解剖出帕金森病神經回路，並發現直接刺激投射到亞視腦核的傳入軸突可以解釋治療效果。這些結果不僅有助於我們更好地理解DBS的機制，還為系統性解剖疾病回路提供了一種新的方法。除此之外，研究人員還定義了一個技術工具箱，通過選擇性地控制個別組件，可以系統性地拆解疾病回路。這個工具箱可以幫助治療帕金森病等疾病，是一個具有重要應用價值的成果。

關鍵字

Deep brain stimulation, optogenetics, disease circuitry.

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RaisoLiu
email: raiso@gate.sinica.edu.tw
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論文分析：Peripersonal encoding of forelimb proprioception in the mouse somatosensory cortex

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

Jun 8, 2023
論文分析：Attention is All you Need

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

May 20, 2023
論文分析：Residual dynamics resolves recurrent contributions to neural computation

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

May 20, 2023
論文分析：Learnable latent embeddings for joint behavioural and neural analysis

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

May 20, 2023
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