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 computation goals of optimal sensory states that are attained by either task demands or internal fluctuations.

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 P_i(active → quiescent) = α_iδ(dt)_, where α_i represented the decay rate of the active state of the neuron. Parameter α_i 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 P_i(active → quiescent) = β_iG(S_i)δ(dt)_,. This caused the firing probability to be a function of the input, with β_i as its peak value. Parameter S_i was the total synaptic input to neuron i, given as S_i = N_i(t) + I_i(t), where N_i was the net input from other neurons in the local network and I_i was the net external input to the neuron. The network input was N_i = ∑_jw_ijA_j(t), where w_ij are the weights of the synapses. The activity variable A_j(t) was set to one if the jth neuron was active at time t and zero otherwise. The model neurons had no intrinsic capacity to oscillate because the inter-spike interval was the sum of two independent exponential random variables with parameters α_i and β_iG(S_i), respectively. The model parameters were chosen as follows:  Excitatory (E) and inhibitory (I) neurons in the network were differentiated based on two model parameters: α_E = 0.075 ms, α_I = 0.4 ms; and β_E = 1, β_I = 2.

Six-population network

A synthetic laminar network consisted of simulating 45 neurons in total, 15 in each cortical layer (superficial, input, deep). Each layer contained 10 excitatory and 5 inhibitory neurons, giving a total of 6 populations (3 layers x 2 neuron types). The network topology for synaptic connectivity is depicted in Fig 2e of the primary article. The weights for synaptic connections are 1 for the inter-laminar connections, 1.5 for intra-laminar connections where the presynaptic unit is an E unit, and -2 for intra-laminar connections where the presynaptic unit is an I unit. 1000 trials of spiking data, each 2000 ms long, were simulated using this model.

Preprocessing for MTwDBN Analysis

Data from single units was grouped by population in each model simulation.  The multi-unit spiking activity of these populations was used for the analysis. Before aggregating activities of single units into populations, d % (d being a pre-selected number, see Effect of sub-sampling in synthetic laminar model) of single units in each population were dropped (not used for analysis) from the laminar model data. The data from each trial were discretized into 1.2ms bins of either 0, 1, or 2 to denote no spikes, one spike, or multiple spikes in a time bin. The data were lagged 2 times to give 3 time slices (2.4, 1.2, 0 ms). The data from all trials were concatenated together to generate a single data table with 6 or 18 columns (2 or 6 populations x 3 time slices). The binned and discretized spiking activity of a single population in a single time slice was viewed as a variable in either the pairwise regression or DBN framework. Data with structure as produced by this preprocessing step are termed binned-and-sliced data tables. 

Attention Data

Behavioral Task

Well-isolated single units were recorded from area V4 of two rhesus macaques during an attention-demanding orientation change detection task. The task design and the experimental procedures are described in detail in a previous study^15,88. While the monkey maintained fixation, two oriented Gabor stimuli were flashed on for 200 ms and off for variable intervals (randomly chosen between 200 and 400 ms). The contrast of the stimulus was randomly chosen from a uniform distribution of 6 contrasts (c = [10%, 18%, 26%, 34%, 42%, and 50%]). One of the stimuli was located at the receptive field overlap region (Attend In) and the other at an equally eccentric location across the vertical meridian (Attend Away). At the beginning of a block of trials, we presented instruction trials where the monkey was spatially cued to the covertly attend to one of two stimulus locations. One of the two stimuli changed in orientation at an unpredictable time (minimum 1s, maximum 5s, mean 3s). The monkey was rewarded for making a saccade to the location of orientation change. 95% of the orientation changes occur at the cued location, and 5% occur at the uncued location (foil trials). We observed impaired performance and slower reaction times for the foil trials, suggesting that the monkey was indeed using the spatial cue to perform the task. The difficulty of the task was controlled by changing the degree of orientation change (randomly chosen from the following: 1°, 2°, 3°, 4°, 6°, 8°, 10°, and 12°). If no change occurred before 5 s, the monkey was rewarded for holding fixation (catch trial, 13% of trials). 

Electrophysiological recording

While the monkey was performing the attention task (Fig 3a), we used artificial dura chambers to facilitate the insertion of 16-channel linear array electrodes (laminar probes, Plexon, Plexon V-probe) into cortical sites near the center of the prelunate gyrus. Neuronal signals were recorded, filtered, and stored using the Multichannel Acquisition Processor system (Plexon). Neuronal signals were classified as either isolated single units or multiunit clusters by the Plexon Offline Sorter program. Data is available upon request.

Laminar boundaries: For the data collected from linear array electrodes, we used current source density analysis to identify the superficial (Layers 1-3), input (Layer 4), and deep (Layers 5 and 6) layers of the cortex based on the second derivative of the flash-triggered LFPs. The resulting time-varying traces of current across the depth of the cortex can be visualized as CSD maps (Fig 3b). Red regions depict current sinks in the corresponding region of the cortical laminae, while blue regions depict current sources. The input layer was identified as the first current sink followed by a reversal to current source. The superficial and deep layers had the opposite sink-source pattern i.e. source followed by sink.

SU classification: Cell bodies of single units with bi-phasic action potential waveforms were assigned to the same layer in which the electrode channel was situated during recordings. Units that had tri-phasic waveforms or other shapes were excluded from analyses. Units with peak-to-trough duration greater than 225s were classified as broad-spiking putative excitatory neurons; units with peak-to-trough duration less than 225s were classified as narrow-spiking putative inhibitory neurons (Fig 3c). Extracellular data were collected over 32 sessions (23 sessions in monkey A, 9 in monkey C) yielding 337 single units in total. Unit yield per session was considerably higher in monkey C than monkey A, resulting in a roughly equal contribution of both monkeys toward the population data.

Data Selection

All analyses in this study were performed on spiking data during an interval of 60-260 ms after stimulus onset excluding orientation changes. Only single units whose spike waveforms were successfully classified as broad or narrow and for whom the layer identity could be successfully discerned were used in the analysis. There were 29 sessions which had one or more such units recorded. For layer-wise analyses, only sessions with at least one unit from each layer were included (18 sessions). For broad- and narrow-spiking layer-wise (layer-class) analyses, only sessions with at least one unit from two or more populations were included (27 sessions). Pairwise and network-based dependency analysis were performed on each session separately.

Analysis: Attention Conditions

Data from Attend In and Attend Away trials were analyzed independently. For each attention condition, only data from trials where the animal successfully detected the orientation change or from catch trials where the animal maintained fixation were used.

Analysis: Behavioral Performance

We fit the behavioral data with a logistic function and defined the threshold condition as the orientation change that was closest to the 50% threshold of the fitted psychometric function for that session. This subset of trials from within the attend-in condition in which the animal was equally likely to correctly detect (Hit) or fail to detect (Miss) the orientation change was used for our analysis. Data from Hit and Miss trials were analyzed independently.

Preprocessing for MTwDBN analysis

Single units were grouped according to neocortical layer (superficial/input/deep) for layer-wise (3 populations) analyses and additionally by spike waveform (narrow/broad) for layer+class (3x2 populations) analyses. The multi-unit spiking activity of these populations was used for the analysis. The data from each stimulus presentation (60 - 260 ms after stimulus onset) were discretized into 15 ms bins and 6 lags to give 7 time slices (-90, -75, -60, -45, -30, -15, 0 ms). The spiking activity of each population in each bin was discretized to 1 or 0 to denote if there were spikes or not. The data from all stimulus presentations in a session/condition combination were concatenated together. Data tables had 21 columns for layer-wise analyses (3 layers x 7 time slices) and 42 columns for layer-class analyses (6 layer-class populations x 7 time slices). The binned and discretized spiking activity of a single population in a single time slice was viewed as a variable in either the pairwise regression or DBN framework. Data from each session/condition were preprocessed separately. To keep analyses consistent across conditions being compared (Attend-In vs. Away OR Hit vs. Miss), the size of the bootstraps was equal to the maximum number of rows of the two conditions.