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...