Developmental loss of ErbB4 in PV interneurons disrupts state-dependent cortical circuit dynamics.

GABAergic inhibition plays an important role in the establishment and maintenance of cortical circuits during development. Neuregulin 1 (Nrg1) and its interneuron-specific receptor ErbB4 are key elements of a signaling pathway critical for the maturation and proper synaptic connectivity of interneurons. Using conditional deletions of the ERBB4 gene in mice, we tested the role of this signaling pathway at two developmental timepoints in parvalbumin-expressing (PV) interneurons, the largest subpopulation of cortical GABAergic cells. Loss of ErbB4 in PV interneurons during embryonic, but not late postnatal development leads to alterations in the activity of excitatory and inhibitory cortical neurons, along with severe disruption of cortical temporal organization. These impairments emerge by the end of the second postnatal week, prior to the complete maturation of the PV interneurons themselves. Early loss of ErbB4 in PV interneurons also results in profound dysregulation of excitatory pyramidal neuron dendritic architecture and a redistribution of spine density at the apical dendritic tuft. In association with these deficits, excitatory cortical neurons exhibit normal tuning for sensory inputs, but a loss of state-dependent modulation of the gain of sensory responses. Together these data support a key role for early developmental Nrg1/ErbB4 signaling in PV interneurons as a powerful mechanism underlying the maturation of both the inhibitory and excitatory components of cortical circuits.

SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns.

Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multi-neuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore, SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-Purpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.

Spike-based coupling between single neurons and populations across rat sensory cortices, perirhinal cortex, and hippocampus.

Cortical computations require coordination of neuronal activity within and across multiple areas. We characterized spiking relationships within and between areas by quantifying coupling of single neurons to population firing patterns. Single-neuron population coupling (SNPC) was investigated using ensemble recordings from hippocampal CA1 region and somatosensory, visual, and perirhinal cortices. Within-area coupling was heterogeneous across structures, with area CA1 showing higher levels than neocortical regions. In contrast to known anatomical connectivity, between-area coupling showed strong firing coherence of sensory neocortices with CA1, but less with perirhinal cortex. Cells in sensory neocortices and CA1 showed positive correlations between within- and between-area coupling; these were weaker for perirhinal cortex. All four areas harbored broadcasting cells, connecting to multiple external areas, which was uncorrelated to within-area coupling strength. When examining correlations between SNPC and spatial coding, we found that, if such correlations were significant, they were negative. This result was consistent with an overall preservation of SNPC across different brain states, suggesting a strong dependence on intrinsic network connectivity. Overall, SNPC offers an important window on cell-to-population synchronization in multi-area networks. Instead of pointing to specific information-coding functions, our results indicate a primary function of SNPC in dynamically organizing communication in systems composed of multiple, interconnected areas.

Coherence fails to reliably capture inter-areal interactions in bidirectional neural systems with transmission delays.

Accurately measuring and quantifying the underlying interactions between brain areas is crucial for understanding the flow of information in the brain. Of particular interest in the field of electrophysiology is the analysis and characterization of the spectral properties of these interactions. Coherence and Granger-Geweke causality are well-established, commonly used methods for quantifying inter-areal interactions, and are thought to reflect the strength of inter-areal interactions. Here we show that the application of both methods to bidirectional systems with transmission delays is problematic, especially for coherence. Under certain circumstances, coherence can be completely abolished despite there being a true underlying interaction. This problem occurs due to interference caused in the computation of coherence, and is an artifact of the method. We motivate an understanding of the problem through computational modelling and numerical simulations. In addition, we have developed two methods that can recover the true bidirectional interactions in the presence of transmission delays.

Attentional modulation of inter-areal coherence explained by frequency shifts.

Inter-areal coherence has been hypothesized as a mechanism for inter-areal communication. Indeed, empirical studies have observed an increase in inter-areal coherence with attention. Yet, the mechanisms underlying changes in coherence remain largely unknown. Both attention and stimulus salience are associated with shifts in the peak frequency of gamma oscillations in V1, which suggests that the frequency of oscillations may play a role in facilitating changes in inter-areal communication and coherence. In this study, we used computational modeling to investigate how the peak frequency of a sender influences inter-areal coherence. We show that changes in the magnitude of coherence are largely determined by the peak frequency of the sender. However, the pattern of coherence depends on the intrinsic properties of the receiver, specifically whether the receiver integrates or resonates with its synaptic inputs. Because resonant receivers are frequency-selective, resonance has been proposed as a mechanism for selective communication. However, the pattern of coherence changes produced by a resonant receiver is inconsistent with empirical studies. By contrast, an integrator receiver does produce the pattern of coherence with frequency shifts in the sender observed in empirical studies. These results indicate that coherence can be a misleading measure of inter-areal interactions. This led us to develop a new measure of inter-areal interactions, which we refer to as Explained Power. We show that Explained Power maps directly to the signal transmitted by the sender filtered by the receiver, and thus provides a method to quantify the true signals transmitted between the sender and receiver. Together, these findings provide a model of changes in inter-areal coherence and Granger-causality as a result of frequency shifts.

Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer.

What determines the aesthetic appeal of artworks? Recent work suggests that aesthetic appeal can, to some extent, be predicted from a visual artwork's image features. Yet a large fraction of variance in aesthetic ratings remains unexplained and may relate to individual preferences. We hypothesized that an artwork's aesthetic appeal depends strongly on self-relevance. In a first study (N = 33 adults, online replication N = 208), rated aesthetic appeal for real artworks was positively predicted by rated self-relevance. In a second experiment (N = 45 online), we created synthetic, self-relevant artworks using deep neural networks that transferred the style of existing artworks to photographs. Style transfer was applied to self-relevant photographs selected to reflect participant-specific attributes such as autobiographical memories. Self-relevant, synthetic artworks were rated as more aesthetically appealing than matched control images, at a level similar to human-made artworks. Thus, self-relevance is a key determinant of aesthetic appeal, independent of artistic skill and image features.

Improved visualization of high-dimensional data using the distance-of-distance transformation.

Dimensionality reduction tools like t-SNE and UMAP are widely used for high-dimensional data analysis. For instance, these tools are applied in biology to describe spiking patterns of neuronal populations or the genetic profiles of different cell types. Here, we show that when data include noise points that are randomly scattered within a high-dimensional space, a "scattering noise problem" occurs in the low-dimensional embedding where noise points overlap with the cluster points. We show that a simple transformation of the original distance matrix by computing a distance between neighbor distances alleviates this problem and identifies the noise points as a separate cluster. We apply this technique to high-dimensional neuronal spike sequences, as well as the representations of natural images by convolutional neural network units, and find an improvement in the constructed low-dimensional embedding. Thus, we present an improved dimensionality reduction technique for high-dimensional data containing noise points.

Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks.

Hierarchy is a major organizational principle of the cortex and underscores modern computational theories of cortical function. The local microcircuit amplifies long-distance inter-areal input, which show distance-dependent changes in their laminar profiles. Statistical modeling of these changes in laminar profiles demonstrates that inputs from multiple hierarchical levels to their target areas show remarkable consistency, allowing the construction of a cortical hierarchy based on a principle of hierarchical distance. The statistical modeling that is applied to structure can also be applied to laminar differences in the oscillatory coherence between areas thereby determining a functional hierarchy of the cortex. Close examination of the anatomy of inter-areal connectivity reveals a dual counterstream architecture with well-defined distance-dependent feedback and feedforward pathways in both the supra- and infragranular layers, suggesting a multiplicity of feedback pathways with well-defined functional properties. These findings are consistent with feedback connections providing a generative network involved in a wide range of cognitive functions. A dynamical model constrained by connectivity data sheds insight into the experimentally observed signatures of frequency-dependent Granger causality for feedforward versus feedback signaling. Concerted experiments capitalizing on recent technical advances and combining tract-tracing, high-resolution fMRI, optogenetics and mathematical modeling hold the promise of a much improved understanding of lamina-constrained mechanisms of neural computation and cognition. However, because inter-areal interactions involve cortical layers that have been the target of important evolutionary changes in the primate lineage, these investigations will need to include human and non-human primate comparisons.

Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure.

Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern "noise" spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.

Alterations in Functional Cortical Hierarchy in Hemiparkinsonian Rats.

Parkinson's disease and experimentally induced hemiparkinsonism are characterized by increased beta synchronization between cortical and subcortical areas. This change in beta connectivity might reflect either a symmetric increase in interareal influences or asymmetric changes in directed influences among brain areas. We assessed patterns of functional and directed connectivity within and between striatum and six cortical sites in each hemisphere of the hemiparkinsonian rat model. LFPs were recorded in resting and walking states, before and after unilateral 6-hydroxydopamine lesion. The hemiparkinsonian state was characterized by increased oscillatory activity in the 20-40 Hz range in resting and walking states, and increased interhemispheric coupling (phase lag index) that was more widespread at rest than during walking. Spectral Granger-causality analysis revealed that the change in symmetric functional connectivity comprised profound reorganization of hierarchical organization and directed influence patterns. First, in the lesioned hemisphere, the more anterior, nonprimary motor areas located at the top of the cortical hierarchy (i.e., receiving many directed influences) tended to increase their directed influence onto the posterior primary motor and somatosensory areas. This enhanced influence of "higher" areas may be related to the loss of motor control due to the 6-OHDA lesion. Second, the drive from the nonlesioned toward the lesioned hemisphere (in particular to striatum) increased, most prominently during walking. The nature of these adaptations (disturbed signaling or compensation) is discussed. The present study demonstrates that hemiparkinsonism is associated with a profound reorganization of the hierarchical organization of directed influence patterns among brain areas, perhaps reflecting compensatory processes.SIGNIFICANCE STATEMENT Parkinson's disease classically first becomes manifest in one hemibody before affecting both sides, suggesting that degeneration is asymmetrical. Our results suggest that asymmetrical degeneration of the dopaminergic system induces an increased drive from the nonlesioned toward the lesioned hemisphere and a profound reorganization of functional cortical hierarchical organization, leading to a stronger directed influence of hierarchically higher placed cortical areas over primary motor and somatosensory cortices. These changes may represent a compensatory mechanism for loss of motor control as a consequence of dopamine depletion.

Reward Expectancy Strengthens CA1 Theta and Beta Band Synchronization and Hippocampal-Ventral Striatal Coupling.

The use of information from the hippocampal memory system in motivated behavior depends on its communication with the ventral striatum. When an animal encounters cues that signal subsequent reward, its reward expectancy is raised. It is unknown, however, how this process affects hippocampal dynamics and their influence on target structures, such as ventral striatum. We show that, in rats, reward-predictive cues result in enhanced hippocampal theta and beta band rhythmic activity during subsequent action, compared with uncued goal-directed navigation. The beta band component, also labeled theta's harmonic, involves selective hippocampal CA1 cell groups showing frequency doubling of firing periodicity relative to theta rhythmicity and it partitions the theta cycle into segments showing clear versus poor spike timing organization. We found that theta phase precession occurred over a wider range than previously reported. This was apparent from spikes emitted near the peak of the theta cycle exhibiting large "phase precessing jumps" relative to spikes in foregoing cycles. Neither this phenomenon nor the regular manifestation of theta phase precession was affected by reward expectancy. Ventral striatal neuronal firing phase-locked not only to hippocampal theta, but also to beta band activity. Both hippocampus and ventral striatum showed increased synchronization between neuronal firing and local field potential activity during cued compared with uncued goal approaches. These results suggest that cue-triggered reward expectancy intensifies hippocampal output to target structures, such as the ventral striatum, by which the hippocampus may gain prioritized access to systems modulating motivated behaviors. SIGNIFICANCE STATEMENT: Here we show that temporally discrete cues raising reward expectancy enhance both theta and beta band activity in the hippocampus once goal-directed navigation has been initiated. These rhythmic activities are associated with increased synchronization of neuronal firing patterns in the hippocampus and the connected ventral striatum. When transmitted to downstream target structures, this expectancy-related state of intensified processing in the hippocampus may modulate goal-directed action.

Spike-Based Functional Connectivity in Cerebral Cortex and Hippocampus: Loss of Global Connectivity Is Coupled to Preservation of Local Connectivity During Non-REM Sleep.

UNLABELLED: Behavioral states are commonly considered global phenomena with homogeneous neural determinants. However, recent studies indicate that behavioral states modulate spiking activity with neuron-level specificity as a function of brain area, neuronal subtype, and preceding history. Although functional connectivity also strongly depends on behavioral state at a mesoscopic level and is globally weaker in non-REM (NREM) sleep and anesthesia than wakefulness, it is unknown how neuronal communication is modulated at the cellular level. We hypothesize that, as for neuronal activity, the influence of behavioral states on neuronal coupling strongly depends on type, location, and preceding history of involved neurons. Here, we applied nonlinear, information-theoretical measures of functional connectivity to ensemble recordings with single-cell resolution to quantify neuronal communication in the neocortex and hippocampus of rats during wakefulness and sleep. Although functional connectivity (measured in terms of coordination between firing rate fluctuations) was globally stronger in wakefulness than in NREM sleep (with distinct traits for cortical and hippocampal areas), the drop observed during NREM sleep was mainly determined by a loss of inter-areal connectivity between excitatory neurons. Conversely, local (intra-area) connectivity and long-range (inter-areal) coupling between interneurons were preserved during NREM sleep. Furthermore, neuronal networks that were either modulated or not by a behavioral task remained segregated during quiet wakefulness and NREM sleep. These results show that the drop in functional connectivity during wake-sleep transitions globally holds true at the cellular level, but confine this change mainly to long-range coupling between excitatory neurons. SIGNIFICANCE STATEMENT: Studies performed at a mesoscopic level of analysis have shown that communication between cortical areas is disrupted in non-REM sleep and anesthesia. However, the neuronal determinants of this phenomenon are not known. Here, we applied nonlinear, information-theoretical measures of functional coupling to multi-area tetrode recordings from freely moving rats to investigate whether and how brain state modulates coordination between individual neurons. We found that the previously observed drop in functional connectivity during non-REM (NREM) sleep can be explained by a decrease in coupling between excitatory neurons located in distinct brain areas. Conversely, intra-area communication and coupling between interneurons are preserved. Our results provide significant new insights into the neuron-level mechanisms responsible for the loss of consciousness occurring in NREM sleep.

Stimulus repetition modulates gamma-band synchronization in primate visual cortex.

When a sensory stimulus repeats, neuronal firing rate and functional MRI blood oxygen level-dependent responses typically decline, yet perception and behavioral performance either stay constant or improve. An additional aspect of neuronal activity is neuronal synchronization, which can enhance the impact of neurons onto their postsynaptic targets independent of neuronal firing rates. We show that stimulus repetition leads to profound changes of neuronal gamma-band (∼40-90 Hz) synchronization. Electrocorticographic recordings in two awake macaque monkeys demonstrated that repeated presentations of a visual grating stimulus resulted in a steady increase of visually induced gamma-band activity in area V1, gamma-band synchronization between areas V1 and V4, and gamma-band activity in area V4. Microelectrode recordings in area V4 of two additional monkeys under the same stimulation conditions allowed a direct comparison of firing rates and gamma-band synchronization strengths for multiunit activity (MUA), as well as for isolated single units, sorted into putative pyramidal cells and putative interneurons. MUA and putative interneurons showed repetition-related decreases in firing rate, yet increases in gamma-band synchronization. Putative pyramidal cells showed no repetition-related firing rate change, but a decrease in gamma-band synchronization for weakly stimulus-driven units and constant gamma-band synchronization for strongly driven units. We propose that the repetition-related changes in gamma-band synchronization maintain the interareal stimulus signaling and sharpen the stimulus representation by gamma-synchronized pyramidal cell spikes.

Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex.

Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3-7 Hz (theta) and 12-20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations.

Effects of Arc/Arg3.1 gene deletion on rhythmic synchronization of hippocampal CA1 neurons during locomotor activity and sleep.

The activity-regulated cytoskeletal-associated protein/activity regulated gene (Arc/Arg3.1) is crucial for long-term synaptic plasticity and memory formation. However, the neurophysiological substrates of memory deficits occurring in the absence of Arc/Arg3.1 are unknown. We compared hippocampal CA1 single-unit and local field potential (LFP) activity in Arc/Arg3.1 knockout and wild-type mice during track running and flanking sleep periods. Locomotor activity, basic firing and spatial coding properties of CA1 cells in knockout mice were not different from wild-type mice. During active behavior, however, knockout animals showed a significantly shifted balance in LFP power, with a relative loss in high-frequency (beta-2 and gamma) bands compared to low-frequency bands. Moreover, during track-running, knockout mice showed a decrease in phase locking of spiking activity to LFP oscillations in theta, beta and gamma bands. Sleep architecture in knockout mice was not grossly abnormal. Sharp-wave ripples, which have been associated with memory consolidation and replay, showed only minor differences in dynamics and amplitude. Altogether, these findings suggest that Arc/Arg3.1 effects on memory formation are not only manifested at the level of molecular pathways regulating synaptic plasticity, but also at the systems level. The disrupted power balance in theta, beta and gamma rhythmicity and concomitant loss of spike-field phase locking may affect memory encoding during initial storage and memory consolidation stages.

How to detect the Granger-causal flow direction in the presence of additive noise?

Granger-causality metrics have become increasingly popular tools to identify directed interactions between brain areas. However, it is known that additive noise can strongly affect Granger-causality metrics, which can lead to spurious conclusions about neuronal interactions. To solve this problem, previous studies have proposed the detection of Granger-causal directionality, i.e. the dominant Granger-causal flow, using either the slope of the coherency (Phase Slope Index; PSI), or by comparing Granger-causality values between original and time-reversed signals (reversed Granger testing). We show that for ensembles of vector autoregressive (VAR) models encompassing bidirectionally coupled sources, these alternative methods do not correctly measure Granger-causal directionality for a substantial fraction of VAR models, even in the absence of noise. We then demonstrate that uncorrelated noise has fundamentally different effects on directed connectivity metrics than linearly mixed noise, where the latter may result as a consequence of electric volume conduction. Uncorrelated noise only weakly affects the detection of Granger-causal directionality, whereas linearly mixed noise causes a large fraction of false positives for standard Granger-causality metrics and PSI, but not for reversed Granger testing. We further show that we can reliably identify cases where linearly mixed noise causes a large fraction of false positives by examining the magnitude of the instantaneous influence coefficient in a structural VAR model. By rejecting cases with strong instantaneous influence, we obtain an improved detection of Granger-causal flow between neuronal sources in the presence of additive noise. These techniques are applicable to real data, which we demonstrate using actual area V1 and area V4 LFP data, recorded from the awake monkey performing a visual attention task.

A long-range fronto-parietal 5- to 10-Hz network predicts "top-down" controlled guidance in a task-switch paradigm.

The capacity to rapidly adjust behavioral strategies according to changing task demands is closely associated with coordinated activity in lateral and medial prefrontal cortices. Subdivisions within prefrontal cortex are implicated to encode attentional task sets and to update changing task rules, particularly when changing task demands require top-down control. Here, we tested whether these top-down processes precede stimulus processing and constitute a preparatory attentional state that functionally couples with parietal cortex. We examined this functional coupling by recording from intracranial EEG electrodes in macaques during performance of a task-switching paradigm that separates task performance that is based on controlled top-down guidance from automatic, stimulus-triggered processing modes. We identify a prefrontal-parietal network that phase synchronizes at 5-10 Hz, particularly during preparatory states that indicate top-down controlled task-processing modes. Phase relations in the network suggest that medial and lateral prefrontal cortices synchronize bidirectionally, with medial prefrontal cortex showing a phase-lead relative to left parietal recorded 5- to 10-Hz preparatory signals. These findings reveal a 5- to 10-Hz coordinated, long-range fronto-parietal network prior to actual task-relevant stimulus processing, particularly when subjects engage in controlled task processing modes.

Orientation selectivity and noise correlation in awake monkey area V1 are modulated by the gamma cycle.

Gamma-band synchronization adjusts the timing of excitatory and inhibitory inputs to a neuron. Neurons in the visual cortex are selective for stimulus orientation because of dynamic interactions between excitatory and inhibitory inputs. We hypothesized that these interactions and hence also orientation selectivity vary during the gamma cycle. We determined for each spike its phase relative to the gamma cycle. As a function of gamma phase, we then determined spike rates and their orientation selectivity. Orientation selectivity was modulated by gamma phase. The firing rate of spiking activity that occurred close to a neuron's mean gamma phase of firing was most orientation selective. This stimulus-selective signal could best be conveyed to postsynaptic neurons if it were not corrupted by noise correlations. Noise correlations between firing rates were modulated by gamma phase such that they were not statistically detectable for the spiking activity occurring close to a neuron's mean gamma phase of firing. Thus, gamma-band synchronization produces spiking activity that carries maximal stimulus selectivity and minimal noise correlation in its firing rate, and at the same time synchronizes this spiking activity for maximal impact on postsynaptic targets.

Specific contributions of ventromedial, anterior cingulate, and lateral prefrontal cortex for attentional selection and stimulus valuation.

Attentional control ensures that neuronal processes prioritize the most relevant stimulus in a given environment. Controlling which stimulus is attended thus originates from neurons encoding the relevance of stimuli, i.e. their expected value, in hand with neurons encoding contextual information about stimulus locations, features, and rules that guide the conditional allocation of attention. Here, we examined how these distinct processes are encoded and integrated in macaque prefrontal cortex (PFC) by mapping their functional topographies at the time of attentional stimulus selection. We find confined clusters of neurons in ventromedial PFC (vmPFC) that predominantly convey stimulus valuation information during attention shifts. These valuation signals were topographically largely separated from neurons predicting the stimulus location to which attention covertly shifted, and which were evident across the complete medial-to-lateral extent of the PFC, encompassing anterior cingulate cortex (ACC), and lateral PFC (LPFC). LPFC responses showed particularly early-onset selectivity and primarily facilitated attention shifts to contralateral targets. Spatial selectivity within ACC was delayed and heterogeneous, with similar proportions of facilitated and suppressed responses during contralateral attention shifts. The integration of spatial and valuation signals about attentional target stimuli was observed in a confined cluster of neurons at the intersection of vmPFC, ACC, and LPFC. These results suggest that valuation processes reflecting stimulus-specific outcome predictions are recruited during covert attentional control. Value predictions and the spatial identification of attentional targets were conveyed by largely separate neuronal populations, but were integrated locally at the intersection of three major prefrontal areas, which may constitute a functional hub within the larger attentional control network.

Distinct feedforward and feedback pathways for cell-type specific attention effects.

Selective attention is thought to depend on enhanced firing activity in extrastriate areas. Theories suggest that this enhancement depends on selective inter-areal communication via gamma (30-80 Hz) phase-locking. To test this, we simultaneously recorded from different cell types and cortical layers of macaque V1 and V4. We find that while V1-V4 gamma phase-locking between local field potentials increases with attention, the V1 gamma rhythm does not engage V4 excitatory-neurons, but only fast-spiking interneurons in L4 of V4. By contrast, attention enhances V4 spike-rates in both excitatory and inhibitory cells, most strongly in L2/3. The rate increase in L2/3 of V4 precedes V1 in time. These findings suggest enhanced signal transmission with attention does not depend on inter-areal gamma phase-locking and show that the endogenous gamma rhythm has cell-type- and layer-specific effects on downstream target areas. Similar findings were made in the mouse visual system, based on opto-tagging of identified interneurons.