Brain-state mediated modulation of inter-laminar dependencies in visual cortex

Spatial attention is critical for recognizing behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids the hierarchical computations of object recognition remains unclear. We investigated this in the laminar cortical network of visual area V4, an area strongly modulated by attention. We found that deployment of attention strengthened unique dependencies in neural activity across cortical layers. On the other hand, shared dependencies were reduced within the excitatory population of a layer. Surprisingly, attention strengthened unique dependencies within a laminar population. Crucially, these modulation patterns were also observed during successful behavioral outcomes that are thought to be mediated by internal brain state fluctuations. Successful behavioral outcomes were also associated with phases of reduced neural excitability, suggesting a mechanism for enhanced information transfer during optimal states. Our results suggest common comput..., Synthetic Neural Network
Synthetic neural network models were constructed using stochastic spiking neurons. Individual neurons in the model were treated as coupled, continuous-time, two-state (active and quiescent) Markov processes. The active state represents a neuron firing an action potential and its accompanying refractory period, whereas the quiescent states represent a neuron at rest. The transition probability for the i-th neuron to decay from active to quiescent state in time dt was Pi(active → quiescent) = αiδ(dt), where αi represented the decay rate of the active state of the neuron. Parameter αi sets the upper bound on firing rate of the stochastically spiking neuron, akin to a refractory period. The transition probability for the i-th neuron to change from quiescent to active state (i.e., spike) was Pi(active → quiescent) = βiG(Si)δ(dt),. This caused the firing probability to be a function of the input, with βi as its peak value. Parameter Si was the total synaptic input to..., , # Data from: Brain-state mediated modulation of inter-laminar dependencies in visual cortex

[https://doi.org/10.5061/dryad.ffbg79d2w](https://doi.org/10.5061/dryad.ffbg79d2w)

## Data Structure

**Attention**

* LayersOnly (58 files)
* Files contain data tables where neurons were organized according to layer (superficial/input/deep). Files have the format layersonly_*monkey_yyyymmdd_attentionCond.csv.* *Monkey* indicates the monkey identity (a or c), *yyyymmdd* indicates the date of electrophysiological recording, and *attentionCond* indicates whether the monkey was attending towards the receptive field of the recording neurons (attendin) or away from the receptive field of recorded neurons (attendaway). Each csv is a data table where the column rows indicate the layer identity of the neurons (lyr1 - superficial, lyr2 - input, lyr3 - deep) and the time lag (t1 - t7).
* LayersClass (58 files)
* Files contain data tables where neurons were organized according to layer (su...

空间注意对于在杂乱环境中识别行为相关物体至关重要。空间注意的部署如何助力物体识别的层级计算，目前仍不明确。我们在视觉皮层V4区的层状皮层网络中对此展开了研究——该脑区会受到注意的强烈调节。我们发现，注意的部署会强化皮层各层神经元活动间的独特依赖关系；与此同时，单一层内的兴奋性神经元群体的共享依赖关系则有所减弱。令人意外的是，注意还会强化单一层内神经元群体的独特依赖关系。至关重要的是，这些调节模式同样出现在被认为由大脑内部状态波动介导的成功行为结果中。成功的行为表现还与神经兴奋性降低的阶段相关，这提示了在最优状态下信息传递增强的潜在机制。我们的研究结果提示了通用的计算...
合成神经网络（Synthetic Neural Network）
合成神经网络模型采用随机脉冲神经元构建。模型中的单个神经元被建模为耦合的连续时间两态（激活态与静息态）马尔可夫过程。其中激活态对应神经元发放动作电位及其伴随的不应期，静息态则对应神经元处于静息状态。第i个神经元从激活态向静息态衰变的时间dt内的转移概率为$P_i(激活→静息)=α_iδ(dt)$，其中$α_i$代表神经元激活态的衰变速率，该参数设定了随机脉冲神经元发放频率的上限，类似于不应期的作用。第i个神经元从静息态转变为激活态（即产生脉冲）的转移概率为$P_i(静息→激活)=β_iG(S_i)δ(dt)$。这使得发放概率成为输入的函数，其中$β_i$为其峰值参数。$S_i$为神经元的总突触输入……
# 数据来源：视觉皮层层间依赖关系的脑状态介导调节
[https://doi.org/10.5061/dryad.ffbg79d2w](https://doi.org/10.5061/dryad.ffbg79d2w)

## 数据结构
**注意任务（Attention）**
* 仅分层数据集（LayersOnly，共58个文件）
* 文件内存储数据表，其中神经元按照皮层分层（表层/输入层/深层）进行分组。文件命名格式为`layersonly_*monkey_yyyymmdd_attentionCond.csv`，其中`monkey`表示实验猴编号（a或c），`yyyymmdd`为电生理记录的日期，`attentionCond`表示猴子的注意朝向：朝向记录神经元的感受野（attendin）或背离记录神经元的感受野（attendaway）。每个CSV文件为数据表，列字段分别代表神经元的分层标识（lyr1-表层，lyr2-输入层，lyr3-深层）与时延（t1至t7）。
* 分层分类数据集（LayersClass，共58个文件）
* 文件内存储数据表，其中神经元按照皮层分层进行组织（原文截断为su...）