Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex

We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and “fast” initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial co..., Subjects
Experiments were performed using two adult male macaque monkeys (Macaca Mulatta; monkey T, 7 years, 14 kg & monkey O, 11 years, 15.5 kg) trained to touch visual targets for a juice reward. Monkeys were housed in a social vivarium with a normal day/night cycle. Protocols for the experiment were approved by the Stanford University Institutional Animal Care and Use Committee.  Animals were initially trained to come out of their housing and to sit comfortably in a chair. After initial training (as described in Chandrasekaran et al, 2017), monkeys underwent sterile surgery where cylindrical head restraint holders (Crist Instrument Co., Inc., Hagerstown, MD, United States) and standard circular recording cylinders (19 mm diameter, Crist Instrument Co., Inc.) were implanted. Cylinders were placed surface normal to the cortex and were centered over caudal dorsal premotor cortex (PMdc; +16, 15 stereotaxic coordinates). The skull within the cylinder was covered with a thin layer of d..., Matlab is needed in order to open the data files., # Data for \"Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex\"

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This Dryad repository contains data used to construct the figures for \"Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex\".

Experimental data is organized based on the figures in the manuscript, and code to reproduce the figures can be found on Github ([https://github.com/chand-lab/Dynamics2023](https://github.com/chand-lab/Dynamics2023)).

Please see below for further details about the dataset, see the Dryad description ([https://doi.org/10.5061/dryad.9cnp5hqn0](https://doi.org/10.5061/dryad.9cnp5hqn0)) or the article itself for further methodological and analytical details.

**Description of the data and file structure**

This dataset contains various .mat files for recreating the 8 main and 22 supplementary figures in the Nature Communications paper (Boucher PO, Wang T, Carceroni L, Kane G, Shenoy KV, Chandramou...

我们采用动力学系统视角来解析与决策相关的神经活动——这是一个长期未得到根本解决的科学问题。该视角提出，随时间变化的神经活动可由带有初始条件的状态方程描述，且在每一时间步通过循环活动与输入的结合随时间演化。我们提出了多种决策的动力学机制假说，并在模型中对其进行仿真以推导预测结果，随后通过分析执行知觉决策任务的猴子背侧前运动皮层（dorsal premotor cortex, PMd）内神经元的放电率，对这些预测进行了验证。刺激前神经活动（即初始条件）可预测刺激后的神经轨迹，其与反应时（reaction time, RT）及前一试次的结果存在共变关系，但与决策选择无关。刺激后动力学同时依赖于感觉证据与初始条件：更简单的刺激与“快速”初始条件会催生最快的与决策相关的动力学过程。综上，上述结果表明初始共……

## 实验对象
本研究使用两只成年雄性猕猴（Macaca Mulatta）：猴T，7岁，体重14kg；猴O，11岁，体重15.5kg。二者均经过训练，可通过触碰视觉目标以获取果汁奖励。猕猴饲养于符合正常昼夜节律的群居饲养笼中。本实验的所有操作流程均已获得斯坦福大学实验动物护理与使用委员会（Stanford University Institutional Animal Care and Use Committee）的批准。实验动物首先接受训练，学会从饲养笼中走出并舒适地坐在固定椅上。在完成初始训练（详见Chandrasekaran等人2017年的研究）后，猕猴接受无菌外科手术，植入圆柱形头部固定支架（美国马里兰州黑格斯敦市Crist仪器公司生产）与标准圆形记录套筒（直径19mm，Crist仪器公司生产）。套筒以垂直于皮层表面的角度植入，且中心对准尾侧背侧前运动皮层（caudal dorsal premotor cortex, PMdc；立体坐标+16,15）。套筒内的颅骨被一层薄的d……

若要打开数据文件，需使用Matlab软件。
# 题为"初始条件与感觉证据结合可诱导前运动皮层内决策相关动力学过程"的研究数据

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本Dryad知识库包含用于构建上述论文（"初始条件与感觉证据结合可诱导前运动皮层内决策相关动力学过程"）中所有图表的实验数据。实验数据按照论文手稿中的图表进行分类整理，复现图表所需的代码可在GitHub平台获取：[https://github.com/chand-lab/Dynamics2023](https://github.com/chand-lab/Dynamics2023)。

若需了解数据集的更多细节，可查阅Dryad平台的数据集描述页面（[https://doi.org/10.5061/dryad.9cnp5hqn0](https://doi.org/10.5061/dryad.9cnp5hqn0)）或原文以获取更多实验方法与分析细节。

**数据与文件结构说明**

本数据集包含多个.mat格式文件，用于复现发表于《Nature Communications》的论文中的8幅主图与22幅补充图（作者：Boucher PO、Wang T、Carceroni L、Kane G、Shenoy KV、Chandramou……）