Perceptual learning reduces interneuronal correlations in macaque visual cortex

A complex acoustical environment during development enhances auditory perception and coding efficiency in the zebra finch

Sensory experience during development has lasting effects on perception and neural processing. Exposing juvenile animals to artificial stimuli influences the tuning and functional organization of the auditory cortex, but less is known about how the rich acoustical environments experienced by vocal communicators affect the processing of complex vocalizations. Here, we show that in zebra finches ( Taeniopygia guttata ), a colonial-breeding songbird species, exposure to a naturalistic social-acoustical environment during development has a profound impact on auditory perceptual behavior and on cortical-level auditory responses to conspecific song. Compared to birds raised by pairs in acoustic isolation, male and female birds raised in a breeding colony were better in an operant discrimination task at recognizing conspecific songs with and without masking colony noise. Neurons in colony-reared birds had higher average firing rates, selectivity, and discriminability, especially in the narrow-spiking, putatively inhibitory neurons of a higher-order auditory area, the caudomedial nidopallium (NCM). Neurons in colony-reared birds were also less correlated in their tuning and more efficient at encoding the spectrotemporal structure of conspecific song, and better at filtering out masking noise. These results suggest that the auditory cortex adapts to noisy, complex acoustical environments by strengthening inhibitory circuitry, functionally decoupling excitatory neurons while maintaining overall excitatory-inhibitory balance.

Significance Statement

The statistics of the sensory inputs animals experience during postnatal development shape cortical circuits and their functional properties, but most studies examining experience-dependent plasticity in the auditory system has employed artificial stimuli with limited relevance to acoustic communication. Here, we examined how the natural social-acoustical environment experienced by zebra finches, a social songbird that breeds in large colonies, influences the development of auditory perception and the underlying auditory cortical circuits. Compared to birds raised in a more impoverished environment, colony-reared birds were better at recognizing songs of other zebra finches and had higher firing rates in the avian homolog to auditory cortex, along with changes to functional connectivity that resulted in more efficient and robust coding of conspecific song.

Time courses of brain plasticity underpinning visual motion perceptual learning

Visual perceptual learning (VPL) refers to a long-term improvement of visual task performance through training or experience, reflecting brain plasticity even in adults. In human subjects, VPL has been mostly studied using functional magnetic resonance imaging (fMRI). However, due to the low temporal resolution of fMRI, how VPL affects the time course of visual information processing is largely unknown. To address this issue, we trained human subjects to perform a visual motion direction discrimination task. Their behavioral performance and magnetoencephalography (MEG) signals responding to the motion stimuli were measured before, immediately after, and two weeks after training. Training induced a long-lasting behavioral improvement for the trained direction. Based on the MEG signals from occipital sensors, we found that, for the trained motion direction, VPL increased the motion direction decoding accuracy, reduced the motion direction decoding latency, enhanced the direction-selective channel response, and narrowed the tuning profile. Following the MEG source reconstruction, we showed that VPL enhanced the cortical response in early visual cortex (EVC) and strengthened the feedforward connection from EVC to V3A. These VPL-induced neural changes co-occurred in 160-230 ms after stimulus onset. Complementary to previous fMRI findings on VPL, this study provides a comprehensive description on the neural mechanisms of visual motion perceptual learning from a temporal perspective and reveals how VPL shapes the time course of visual motion processing in the adult human brain.

Recent Visual Experience Reshapes V4 Neuronal Activity and Improves Perceptual Performance

Recent visual experience heavily influences our visual perception, but how neuronal activity is reshaped to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while two male rhesus macaque monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in the mid-level visual cortex.

Network state changes in sensory thalamus represent learned outcomes

Thalamic brain areas play an important role in adaptive behaviors. Nevertheless, the population dynamics of thalamic relays during learning across sensory modalities remain unknown. Using a cross-modal sensory reward-associative learning paradigm combined with deep brain two-photon calcium imaging of large populations of auditory thalamus (medial geniculate body, MGB) neurons in male mice, we identified that MGB neurons are biased towards reward predictors independent of modality. Additionally, functional classes of MGB neurons aligned with distinct task periods and behavioral outcomes, both dependent and independent of sensory modality. During non-sensory delay periods, MGB ensembles developed coherent neuronal representation as well as distinct co-activity network states reflecting predicted task outcome. These results demonstrate flexible cross-modal ensemble coding in auditory thalamus during adaptive learning and highlight its importance in brain-wide cross-modal computations during complex behavior.

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Decision-related activity and movement selection in primate visual cortex

Fluctuations in the activity of sensory neurons often predict perceptual decisions. This connection can be quantified with a metric called choice probability (CP), and there is a longstanding debate about whether CP reflects a causal influence on decisions or an echo of decision-making activity elsewhere in the brain. Here, we show that CP can reflect a third variable, namely, the movement used to indicate the decision. In a standard visual motion discrimination task, neurons in the middle temporal (MT) area of primate cortex responded more strongly during trials that involved a saccade toward their receptive fields. This variability accounted for much of the CP observed across the neuronal population, and it arose through training. Moreover, pharmacological inactivation of MT biased behavioral responses away from the corresponding visual field locations. These results demonstrate that training on a task with fixed sensorimotor contingencies introduces movement-related activity in sensory brain regions and that this plasticity can shape the neural circuitry of perceptual decision-making.

Learning leaves a memory trace in motor cortex

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.

Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion

How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.

Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks

Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, such as those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural covariance in neural networks with dropout has not been studied, and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phases. We found that trial-by-trial correlation between neurons (i.e., noise correlation) is positive and low dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. This evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked aspect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.

Signatures of task learning in neural representations

While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.

Temporal and spatial properties of vestibular signals for perception of self-motion

It is well recognized that the vestibular system is involved in numerous important cognitive functions, including self-motion perception, spatial orientation, locomotion, and vector-based navigation, in addition to basic reflexes, such as oculomotor or body postural control. Consistent with this rationale, vestibular signals exist broadly in the brain, including several regions of the cerebral cortex, potentially allowing tight coordination with other sensory systems to improve the accuracy and precision of perception or action during self-motion. Recent neurophysiological studies in animal models based on single-cell resolution indicate that vestibular signals exhibit complex spatiotemporal dynamics, producing challenges in identifying their exact functions and how they are integrated with other modality signals. For example, vestibular and optic flow could provide congruent and incongruent signals regarding spatial tuning functions, reference frames, and temporal dynamics. Comprehensive studies, including behavioral tasks, neural recording across sensory and sensory-motor association areas, and causal link manipulations, have provided some insights into the neural mechanisms underlying multisensory self-motion perception.

Representational Geometries Reveal Differential Effects of Response Correlations on Population Codes in Neurophysiology and Functional Magnetic Resonance Imaging

Two sensory neurons usually display trial-by-trial spike-count correlations given the repeated representations of a stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has become the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations among voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex (five males, one female) and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in empirical neurophysiological studies. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist within the primate visual system. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high-dimensional representational space. Interestingly, response correlations simultaneously reduce and enhance information on higher- and lower-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effects of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.SIGNIFICANCE STATEMENT Despite the vast research interest in the effect of spike-count noise correlations on population codes in neurophysiology, it remains unclear how the response correlations between voxels influence MVPA in human imaging. We used an information-theoretic approach and showed that unlike the detrimental effects of response correlations reported in neurophysiology, voxelwise response correlations generally improve sensory coding. We conducted a series of in-depth analyses and demonstrated that neuronal and voxel response correlations can coexist within the visual system and share some common computational mechanisms. These results shed new light on how the population codes of sensory information can be evaluated via different neural measurements.

Topological insights into the neural basis of flexible behavior

It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects' performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease.

Correlated variability in primate superior colliculus depends on functional class

Correlated variability in neuronal activity (spike count correlations, r_SC) can constrain how information is read out from populations of neurons. Traditionally, r_SC is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of r_SC that are not captured by the population r_SC. We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of r_SC. "Delay class" neurons displayed the highest r_SC, especially during saccades that relied on working memory. Such dependence of r_SC on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.

Dyadic visual perceptual learning on orientation discrimination

The belief that learning can be modulated by social context is mainly supported by high-level value-based learning studies. However, whether social context can even modulate low-level learning such as visual perceptual learning (VPL) is still unknown. Unlike traditional VPL studies in which participants were trained singly, here, we developed a novel dyadic VPL paradigm in which paired participants were trained with the same orientation discrimination task and could monitor each other's performance. We found that the social context (i.e., dyadic training) led to a greater behavioral performance improvement and a faster learning rate compared with the single training. Interestingly, the facilitating effects could be modulated by the performance difference between paired participants. Functional magnetic resonance imaging (fMRI) results showed that, compared with the single training, social cognition areas including bilateral parietal cortex and dorsolateral prefrontal cortex displayed a different activity pattern and enhanced functional connectivities to early visual cortex (EVC) during the dyadic training. Furthermore, the dyadic training resulted in more refined orientation representation in primary visual cortex (V1), which was closely associated with the greater behavioral performance improvement. Taken together, we demonstrate that the social context, learning with a partner, can remarkably augment the plasticity of low-level visual information process by means of reshaping the neural activities in EVC and social cognition areas, as well as their functional interplays.

Size tuning of neural response variability in laminar circuits of macaque primary visual cortex

A defining feature of the cortex is its laminar organization, which is likely critical for cortical information processing. For example, visual stimuli of different size evoke distinct patterns of laminar activity. Visual information processing is also influenced by the response variability of individual neurons and the degree to which this variability is correlated among neurons. To elucidate laminar processing, we studied how neural response variability across the layers of macaque primary visual cortex is modulated by visual stimulus size. Our laminar recordings revealed that single neuron response variability and the shared variability among neurons are tuned for stimulus size, and this size-tuning is layer-dependent. In all layers, stimulation of the receptive field (RF) reduced single neuron variability, and the shared variability among neurons, relative to their pre-stimulus values. As the stimulus was enlarged beyond the RF, both single neuron and shared variability increased in supragranular layers, but either did not change or decreased in other layers. Surprisingly, we also found that small visual stimuli could increase variability relative to baseline values. Our results suggest multiple circuits and mechanisms as the source of variability in different layers and call for the development of new models of neural response variability.

Minimum perturbation theory of deep perceptual learning

Perceptual learning (PL) involves long-lasting improvement in perceptual tasks following extensive training and is accompanied by modified neuronal responses in sensory cortical areas in the brain. Understanding the dynamics of PL and the resultant synaptic changes is important for causally connecting PL to the observed neural plasticity. This is theoretically challenging because learning-related changes are distributed across many stages of the sensory hierarchy. In this paper, we modeled the sensory hierarchy as a deep nonlinear neural network and studied PL of fine discrimination, a common and well-studied paradigm of PL. Using tools from statistical physics, we developed a mean-field theory of the network in the limit of a large number of neurons and large number of examples. Our theory suggests that, in this thermodynamic limit, the input-output function of the network can be exactly mapped to that of a deep linear network, allowing us to characterize the space of solutions for the task. Surprisingly, we found that modifying synaptic weights in the first layer of the hierarchy is both sufficient and necessary for PL. To address the degeneracy of the space of solutions, we postulate that PL dynamics are constrained by a normative minimum perturbation (MP) principle, which favors weight matrices with minimal changes relative to their prelearning values. Interestingly, MP plasticity induces changes to weights and neural representations in all layers of the network, except for the readout weight vector. While weight changes in higher layers are not necessary for learning, they help reduce overall perturbation to the network. In addition, such plasticity can be learned simply through slow learning. We further elucidate the properties of MP changes and compare them against experimental findings. Overall, our statistical mechanics theory of PL provides mechanistic and normative understanding of several important empirical findings of PL.

Calibrating Vision: Concepts and Questions

The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.

Parallel processing, hierarchical transformations, and sensorimotor associations along the 'where' pathway

Visually guided behaviors require the brain to transform ambiguous retinal images into object-level spatial representations and implement sensorimotor transformations. These processes are supported by the dorsal ‘where’ pathway. However, the specific functional contributions of areas along this pathway remain elusive due in part to methodological differences across studies. We previously showed that macaque caudal intraparietal (CIP) area neurons possess robust 3D visual representations, carry choice- and saccade-related activity, and exhibit experience-dependent sensorimotor associations (Chang et al., 2020b). Here, we used a common experimental design to reveal parallel processing, hierarchical transformations, and the formation of sensorimotor associations along the ‘where’ pathway by extending the investigation to V3A, a major feedforward input to CIP. Higher-level 3D representations and choice-related activity were more prevalent in CIP than V3A. Both areas contained saccade-related activity that predicted the direction/timing of eye movements. Intriguingly, the time course of saccade-related activity in CIP aligned with the temporally integrated V3A output. Sensorimotor associations between 3D orientation and saccade direction preferences were stronger in CIP than V3A, and moderated by choice signals in both areas. Together, the results explicate parallel representations, hierarchical transformations, and functional associations of visual and saccade-related signals at a key juncture in the ‘where’ pathway.

The structures and functions of correlations in neural population codes

The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.

Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex

The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.

A general decoding strategy explains the relationship between behavior and correlated variability

Improvements in perception are frequently accompanied by decreases in correlated variability in sensory cortex. This relationship is puzzling because overall changes in correlated variability should minimally affect optimal information coding. We hypothesize that this relationship arises because instead of using optimal strategies for decoding the specific stimuli at hand, observers prioritize generality: a single set of neuronal weights to decode any stimuli. We tested this using a combination of multineuron recordings in the visual cortex of behaving rhesus monkeys and a cortical circuit model. We found that general decoders optimized for broad rather than narrow sets of visual stimuli better matched the animals’ decoding strategy, and that their performance was more related to the magnitude of correlated variability. In conclusion, the inverse relationship between perceptual performance and correlated variability can be explained by observers using a general decoding strategy, capable of decoding neuronal responses to the variety of stimuli encountered in natural vision.

Internally generated population activity in cortical networks hinders information transmission

How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity. The wiring structure is capable of producing rich population-wide shared neuronal variability that agrees with many features of recorded cortical activity. While both the spatial scales of feedforward and recurrent projections strongly affect noise correlations, only recurrent projections, and in particular inhibitory projections, can introduce correlations that limit the stimulus information available to a decoder. Using a spatial neural field model, we relate the recurrent circuit conditions for information limiting noise correlations to how recurrent excitation and inhibition can form spatiotemporal patterns of population-wide activity.

Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales.

How learning unfolds in the brain: toward an optimization view

How do changes in the brain lead to learning? To answer this question, consider an artificial neural network (ANN), where learning proceeds by optimizing a given objective or cost function. This "optimization framework" may provide new insights into how the brain learns, as many idiosyncratic features of neural activity can be recapitulated by an ANN trained to perform the same task. Nevertheless, there are key features of how neural population activity changes throughout learning that cannot be readily explained in terms of optimization and are not typically features of ANNs. Here we detail three of these features: (1) the inflexibility of neural variability throughout learning, (2) the use of multiple learning processes even during simple tasks, and (3) the presence of large task-nonspecific activity changes. We propose that understanding the role of these features in the brain will be key to describing biological learning using an optimization framework.

Modulation of Spike Count Correlations Between Macaque Primary Visual Cortex Neurons by Difficulty of Attentional Task

Studies have shown that spatial attention remarkably affects the trial-to-trial response variability shared between neurons. Difficulty in the attentional task adjusts how much concentration we maintain on what is currently important and what is filtered as irrelevant sensory information. However, how task difficulty mediates the interactions between neurons with separated receptive fields (RFs) that are attended to or attended away is still not clear. We examined spike count correlations between single-unit activities recorded simultaneously in the primary visual cortex (V1) while monkeys performed a spatial attention task with two levels of difficulty. Moreover, the RFs of the two neurons recorded were non-overlapping to allow us to study fluctuations in the correlated responses between competing visual inputs when the focus of attention was allocated to the RF of one neuron. While increasing difficulty in the spatial attention task, spike count correlations were either decreased to become negative between neuronal pairs, implying competition among them, with one neuron (or none) exhibiting attentional enhancement of firing rate, or increased to become positive, suggesting inter-neuronal cooperation, with one of the pair showing attentional suppression of spiking responses. Besides, the modulation of spike count correlations by task difficulty was independent of the attended locations. These findings provide evidence that task difficulty affects the functional interactions between different neuronal pools in V1 when selective attention resolves the spatial competition.

Stimulation of the ventral tegmental area induces visual cortical plasticity at the neuronal level

fMRI studies have shown that pairing a task-irrelevant visual feature with electrical micro-stimulation of the ventral tegmental area (VTA-EM) is sufficient to increase the sensory cortical representation of the paired feature and to improve perceptual performance. However, since fMRI provides an indirect measure of neural activity, the neural response changes underlying the fMRI activations are unknown. Here, we pair a task-irrelevant grating orientation with VTA-EM while attention is directed to a difficult orthogonal task. We examine the changes in neural response properties in macaques by recording spiking activity in the posterior inferior temporal cortex, the locus of fMRI-defined plasticity in previous studies. We observe a relative increase in mean spike rate and preference for the VTA-EM paired orientation compared to an unpaired orientation, which is unrelated to attention. These results demonstrate that VTA-EM-stimulus pairing is sufficient to induce sensory cortical plasticity at the spiking level in nonhuman primates.

Visual exposure enhances stimulus encoding and persistence in primary cortex

Experience shapes sensory responses, already at the earliest stages of cortical processing. We provide evidence that, in the primary visual cortex of anesthetized cats, brief repetitive exposure to a set of simple, abstract stimuli expands the range and decreases the variability of neuronal responses that persist after stimulus offset. These refinements increase the stimulus-specific clustering of neuronal population responses and result in a more efficient encoding of both stimulus identity and stimulus structure, thus potentially benefiting simple readouts in higher cortical areas. Similar results can be achieved via local plasticity mechanisms in recurrent networks, through self-organized refinements of internal dynamics that do not require changes in firing amplitudes.

The brain adapts to the sensory environment. For example, simple sensory exposure can modify the response properties of early sensory neurons. How these changes affect the overall encoding and maintenance of stimulus information across neuronal populations remains unclear. We perform parallel recordings in the primary visual cortex of anesthetized cats and find that brief, repetitive exposure to structured visual stimuli enhances stimulus encoding by decreasing the selectivity and increasing the range of the neuronal responses that persist after stimulus presentation. Low-dimensional projection methods and simple classifiers demonstrate that visual exposure increases the segregation of persistent neuronal population responses into stimulus-specific clusters. These observed refinements preserve the representational details required for stimulus reconstruction and are detectable in postexposure spontaneous activity. Assuming response facilitation and recurrent network interactions as the core mechanisms underlying stimulus persistence, we show that the exposure-driven segregation of stimulus responses can arise through strictly local plasticity mechanisms, also in the absence of firing rate changes. Our findings provide evidence for the existence of an automatic, unguided optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.

Modulation of the dynamical state in cortical network models

Cortical neural responses can be modulated by various factors, such as stimulus inputs and the behavior state of the animal. Understanding the circuit mechanisms underlying modulations of network dynamics is important to understand the flexibility of circuit computations. Identifying the dynamical state of a network is an important first step to predict network responses to external stimulus and top-down modulatory inputs. Models in stable or unstable dynamical regimes require different analytic tools to estimate the network responses to inputs and the structure of neural variability. In this article, I review recent cortical models of state-dependent responses and their predictions about the underlying modulatory mechanisms.

Learning nonnative speech sounds changes local encoding in the adult human cortex

Adults can learn to identify nonnative speech sounds with training, albeit with substantial variability in learning behavior. Increases in behavioral accuracy are associated with increased separability for sound representations in cortical speech areas. However, it remains unclear whether individual auditory neural populations all show the same types of changes with learning, or whether there are heterogeneous encoding patterns. Here, we used high-resolution direct neural recordings to examine local population response patterns, while native English listeners learned to recognize unfamiliar vocal pitch patterns in Mandarin Chinese tones. We found a distributed set of neural populations in bilateral superior temporal gyrus and ventrolateral frontal cortex, where the encoding of Mandarin tones changed throughout training as a function of trial-by-trial accuracy ("learning effect"), including both increases and decreases in the separability of tones. These populations were distinct from populations that showed changes as a function of exposure to the stimuli regardless of trial-by-trial accuracy. These learning effects were driven in part by more variable neural responses to repeated presentations of acoustically identical stimuli. Finally, learning effects could be predicted from speech-evoked activity even before training, suggesting that intrinsic properties of these populations make them amenable to behavior-related changes. Together, these results demonstrate that nonnative speech sound learning involves a wide array of changes in neural representations across a distributed set of brain regions.

Bridging neuronal correlations and dimensionality reduction

Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.

Noise Correlations for Faster and More Robust Learning

Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented by stimulus-independent noise correlations that constrain learning to task-relevant dimensions. We test this idea in a set of neural networks that learn to perform a perceptual discrimination task. Correlations among similarly tuned units were manipulated independently of an overall population signal-to-noise ratio to test how the format of stored information affects learning. Higher noise correlations among similarly tuned units led to faster and more robust learning, favoring homogenous weights assigned to neurons within a functionally similar pool, and could emerge through Hebbian learning. When multiple discriminations were learned simultaneously, noise correlations across relevant feature dimensions sped learning, whereas those across irrelevant feature dimensions slowed it. Our results complement the existing theory on noise correlations by demonstrating that when such correlations are produced without significant degradation of the signal-to-noise ratio, they can improve the speed of readout learning by constraining it to appropriate dimensions.SIGNIFICANCE STATEMENT Positive noise correlations between similarly tuned neurons theoretically reduce the representational capacity of the brain, yet they are commonly observed, emerge dynamically in complex tasks, and persist even in well-trained animals. Here we show that such correlations, when embedded in a neural population with a fixed signal-to-noise ratio, can improve the speed and robustness with which an appropriate readout is learned. In a simple discrimination task such correlations can emerge naturally through Hebbian learning. In more complex tasks that require multiple discriminations, correlations between neurons that similarly encode the task-relevant feature improve learning by constraining it to the appropriate task dimension.

Learning is shaped by abrupt changes in neural engagement

Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.

High-precision coding in visual cortex

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35° and 0.37°, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

Scaling of sensory information in large neural populations shows signatures of information-limiting correlations

How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.

Spike Train Coactivity Encodes Learned Natural Stimulus Invariances in Songbird Auditory Cortex

The capacity for sensory systems to encode relevant information that is invariant to many stimulus changes is central to normal, real-world, cognitive function. This invariance is thought to be reflected in the complex spatiotemporal activity patterns of neural populations, but our understanding of population-level representational invariance remains coarse. Applied topology is a promising tool to discover invariant structure in large datasets. Here, we use topological techniques to characterize and compare the spatiotemporal pattern of coactive spiking within populations of simultaneously recorded neurons in the secondary auditory region caudal medial neostriatum of European starlings (Sturnus vulgaris). We show that the pattern of population spike train coactivity carries stimulus-specific structure that is not reducible to that of individual neurons. We then introduce a topology-based similarity measure for population coactivity that is sensitive to invariant stimulus structure and show that this measure captures invariant neural representations tied to the learned relationships between natural vocalizations. This demonstrates one mechanism whereby emergent stimulus properties can be encoded in population activity, and shows the potential of applied topology for understanding invariant representations in neural populations.SIGNIFICANCE STATEMENT Information in neural populations is carried by the temporal patterns of spikes. We applied novel mathematical tools from the field of algebraic topology to quantify the structure of these temporal patterns. We found that, in a secondary auditory region of a songbird, these patterns reflected invariant information about a learned stimulus relationship. These results demonstrate that topology provides a novel approach for characterizing neural responses that is sensitive to invariant relationships that are critical for the perception of natural stimuli.

Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates

Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain-computer interfaces (BCIs; Zhao et al., 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson's correlation coefficient to measure shared variability between simultaneously recorded units (32-112) and found significantly higher noise-correlation and positive correlation between the populations' signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted a mean matched noise-correlation analysis to decouple known interactions between event-triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however, the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning (RL) towards autonomous BCI updating.

Amplitude modulation encoding in the auditory cortex: comparisons between the primary and middle lateral belt regions

In macaques, the middle lateral auditory cortex (ML) is a belt region adjacent to the primary auditory cortex (A1) and believed to be at a hierarchically higher level. Although ML single-unit responses have been studied for several auditory stimuli, the ability of ML cells to encode amplitude modulation (AM)-an ability that has been widely studied in A1-has not yet been characterized. Here, we compared the responses of A1 and ML neurons to amplitude-modulated (AM) noise in awake macaques. Although several of the basic properties of A1 and ML responses to AM noise were similar, we found several key differences. ML neurons were less likely to phase lock, did not phase lock as strongly, and were more likely to respond in a nonsynchronized fashion than A1 cells, consistent with a temporal-to-rate transformation as information ascends the auditory hierarchy. ML neurons tended to have lower temporally (phase-locking) based best modulation frequencies than A1 neurons. Neurons that decreased their firing rate in response to AM noise relative to their firing rate in response to unmodulated noise became more common at the level of ML than they were in A1. In both A1 and ML, we found a prevalent class of neurons that usually have enhanced rate responses relative to responses to the unmodulated noise at lower modulation frequencies and suppressed rate responses relative to responses to the unmodulated noise at middle modulation frequencies.NEW & NOTEWORTHY ML neurons synchronized less than A1 neurons, consistent with a hierarchical temporal-to-rate transformation. Both A1 and ML had a class of modulation transfer functions previously unreported in the cortex with a low-modulation-frequency (MF) peak, a middle-MF trough, and responses similar to unmodulated noise responses at high MFs. The results support a hierarchical shift toward a two-pool opponent code, where subtraction of neural activity between two populations of oppositely tuned neurons encodes AM.

Flexible coding of object motion in multiple reference frames by parietal cortex neurons

Neurons represent spatial information in diverse reference frames, but it remains unclear whether neural reference frames change with task demands and whether these changes can account for behavior. We examined how neurons represent the direction of a moving object during self-motion, while monkeys switched, from trial to trial, between reporting object direction in head- and world-centered reference frames. Self-motion information is needed to compute object motion in world coordinates, but should be ignored when judging object motion in head coordinates. Neural responses in the ventral intraparietal area are modulated by the task reference frame, such that population activity represents object direction in either reference frame. In contrast, responses in the lateral portion of the medial superior temporal area primarily represent object motion in head coordinates. Our findings demonstrate a neural representation of object motion that changes with task requirements.

Stimulus-choice (mis)alignment in primate area MT

For stimuli near perceptual threshold, the trial-by-trial activity of single neurons in many sensory areas is correlated with the animal's perceptual report. This phenomenon has often been attributed to feedforward readout of the neural activity by the downstream decision-making circuits. The interpretation of choice-correlated activity is quite ambiguous, but its meaning can be better understood in the light of population-wide correlations among sensory neurons. Using a statistical nonlinear dimensionality reduction technique on single-trial ensemble recordings from the middle temporal (MT) area during perceptual-decision-making, we extracted low-dimensional latent factors that captured the population-wide fluctuations. We dissected the particular contributions of sensory-driven versus choice-correlated activity in the low-dimensional population code. We found that the latent factors strongly encoded the direction of the stimulus in single dimension with a temporal signature similar to that of single MT neurons. If the downstream circuit were optimally utilizing this information, choice-correlated signals should be aligned with this stimulus encoding dimension. Surprisingly, we found that a large component of the choice information resides in the subspace orthogonal to the stimulus representation inconsistent with the optimal readout view. This misaligned choice information allows the feedforward sensory information to coexist with the decision-making process. The time course of these signals suggest that this misaligned contribution likely is feedback from the downstream areas. We hypothesize that this non-corrupting choice-correlated feedback might be related to learning or reinforcing sensory-motor relations in the sensory population.

Exogenous attention facilitates perceptual learning in visual acuity to untrained stimulus locations and features

Visual perceptual learning (VPL) refers to the improvement in performance on a visual task due to practice. A hallmark of VPL is specificity, as improvements are often confined to the trained retinal locations or stimulus features. We have previously found that exogenous (involuntary, stimulus-driven) and endogenous (voluntary, goal-driven) spatial attention can facilitate the transfer of VPL across locations in orientation discrimination tasks mediated by contrast sensitivity. Here, we investigated whether exogenous spatial attention can facilitate such transfer in acuity tasks that have been associated with higher specificity. We trained observers for 3 days (days 2-4) in a Landolt acuity task (Experiment 1) or a Vernier hyperacuity task (Experiment 2), with either exogenous precues (attention group) or neutral precues (neutral group). Importantly, during pre-tests (day 1) and post-tests (day 5), all observers were tested with neutral precues; thus, groups differed only in their attentional allocation during training. For the Landolt acuity task, we found evidence of location transfer in both the neutral and attention groups, suggesting weak location specificity of VPL. For the Vernier hyperacuity task, we found evidence of location and feature specificity in the neutral group, and learning transfer in the attention group-similar improvement at trained and untrained locations and features. Our results reveal that, when there is specificity in a perceptual acuity task, exogenous spatial attention can overcome that specificity and facilitate learning transfer to both untrained locations and features simultaneously with the same training. Thus, in addition to improving performance, exogenous attention generalizes perceptual learning across locations and features.

Moderating effects of visual attention and action video game play on perceptual learning with the texture discrimination task

There is currently substantial controversy regarding the reliability of observed patterns of perceptual learning. Contributing to this controversy are a lack of accounting for individual differences and how variations in training can give rise to different patterns of learning. Here we sought to investigate the impact of individual differences in attention, as measured with the Useful Field of View (UFOV) task, and action video game use on perceptual learning in a large sample of subjects trained on a Texture Discrimination Task (TDT). We examined baseline performance on the TDT, learning on the initially trained TDT stimuli and transfer to a subsequently trained background orientation. We find that participants showing better performance on the UFOV task performed better on the TDT, and also showed greater learning and transfer to an untrained background orientation. On the other hand, self-report of action video game play only inconsistently related performance, learning or transfer on the TDT. Further, we failed to replicate previous findings that training with different backgrounds gives rise to interference on the TDT. Together these results suggest that, while differences between individuals and differences in task structure play a role in perceptual learning, previous findings on the impact of action video game use and interference between training stimuli in perceptual learning may be idiosyncratic.

Information-Limiting Correlations in Large Neural Populations

Understanding the neural code requires understanding how populations of neurons code information. Theoretical models predict that information may be limited by correlated noise in large neural populations. Nevertheless, analyses based on tens of neurons have failed to find evidence of saturation. Moreover, some studies have shown that noise correlations can be very small, and therefore may not affect information coding. To determine whether information-limiting correlations exist, we implanted eight Utah arrays in prefrontal cortex (PFC; area 46) of two male macaque monkeys, recording >500 neurons simultaneously. We estimated information in PFC about saccades as a function of ensemble size. Noise correlations were, on average, small (∼10^-3). However, information scaled strongly sublinearly with ensemble size. After shuffling trials, destroying noise correlations, information was a linear function of ensemble size. Thus, we provide evidence for the existence of information-limiting noise correlations in large populations of PFC neurons.SIGNIFICANCE STATEMENT Recent theoretical work has shown that even small correlations can limit information if they are "differential correlations," which are difficult to measure directly. However, they can be detected through decoding analyses on recordings from a large number of neurons over a large number of trials. We have achieved both by collecting neural activity in dorsal-lateral prefrontal cortex of macaques using eight microelectrode arrays (768 electrodes), from which we were able to compute accurate information estimates. We show, for the first time, strong evidence for information-limiting correlations. Despite pairwise correlations being small (on the order of 10^-3), they affect information coding in populations on the order of 100 s of neurons.

Adaptive heading performance during self-motion perception

Previous studies have documented that the perception of self-motion direction can be extracted from the patterns of image motion on the retina (also termed optic flow). Self-motion perception remains stable even when the optic-flow information is distorted by concurrent gaze shifts from body/eye rotations. This has been interpreted that extraretinal signals-efference copies of eye/body movements-are involved in compensating for retinal distortions. Here, we tested an alternative hypothesis to the extraretinal interpretation. We hypothesized that accurate self-motion perception can be achieved from a purely optic-flow-based visual strategy acquired through experience, independent of extraretinal mechanism. To test this, we asked human subjects to perform a self-motion direction discrimination task under normal optic flow (fixation condition) or distorted optic flow resulted from either realistic (pursuit condition) or simulated (simulated condition) eye movements. The task was performed either without (pre- and posttraining) or with (during training) the feedback about the correct answer. We first replicated the previous observation that before training, direction perception was greatly impaired in the simulated condition where the optic flow was distorted and extraretinal eye movement signals were absent. We further showed that after a few training sessions, the initial impairment in direction perception was gradually improved. These results reveal that behavioral training can enforce the exploitation of retinal cues to compensate for the distortion, without the contribution from the extraretinal signals. Our results suggest that self-motion perception is a flexible and adaptive process which might depend on neural plasticity in relevant cortical areas.

Altered functional interactions between neurons in primary visual cortex of macaque monkeys with experimental amblyopia

Amblyopia, a disorder in which vision through one of the eyes is degraded, arises because of defective processing of information by the visual system. Amblyopia often develops in humans after early misalignment of the eyes (strabismus) and can be simulated in macaque monkeys by artificially inducing strabismus. In such amblyopic animals, single-unit responses in primary visual cortex (V1) are appreciably reduced when evoked by the amblyopic eye compared with the other (fellow) eye. However, this degradation in single V1 neuron responsivity is not commensurate with the marked losses in visual sensitivity and resolution measured behaviorally. Here we explored the idea that changes in patterns of coordinated activity across populations of V1 neurons may contribute to degraded visual representations in amblyopia, potentially making it more difficult to read out evoked activity to support perceptual decisions. We studied the visually evoked activity of V1 neuronal populations in three macaques (Macaca nemestrina) with strabismic amblyopia and in one control animal. Activity driven through the amblyopic eye was diminished, and these responses also showed more interneuronal correlation at all stimulus contrasts than responses driven through the fellow eye or responses in the control animal. A decoding analysis showed that responses driven through the amblyopic eye carried less visual information than other responses. Our results suggest that part of the reduced visual capacity of amblyopes may be due to changes in the patterns of functional interaction among neurons in V1.NEW & NOTEWORTHY Previous work on the neurophysiological basis of amblyopia has largely focused on relating behavioral deficits to changes in visual processing by single neurons in visual cortex. In this study, we recorded simultaneously from populations of primary visual cortical (V1) neurons in macaques with amblyopia. We found changes in the strength and pattern of shared response variability between neurons. These changes in neuronal interactions could impair the visual representations of V1 populations driven by the amblyopic eye.

Weaker Inter-hemispheric and Local Functional Connectivity of the Somatomotor Cortex During a Motor Skill Acquisition Is Associated With Better Learning

Recently, an increasing interest in investigating interactions between brain regions using functional connectivity (FC) methods has shifted the initial focus of cognitive neuroimaging research from localizing functional circuits based on task activation to mapping brain networks based on intrinsic FC dynamics. Leveraging the advantages of the latter approach, it has been shown that despite primarily invariant intrinsic organization of the large-scale functional networks, interactions between and within these networks significantly differ between various behavioral and cognitive states. These differences presumably indicate transient reconfiguration of functional connections-an instantaneous process that flexibly mediates and calibrates human behavior according to momentary demands of the environment. Nevertheless, the specificity of these reconfigured FC patterns to the task at hand and their relevance to adaptive processes during learning remain elusive. To address this knowledge gap, we investigated (1) to what extent FC within the somatomotor network is reconfigured during motor skill practice, and (2) how these changes are related to learning. We applied a seed-driven FC approach to data collected during a continuous task-free condition, so-called resting state, and during a motor sequence learning task using functional magnetic resonance imaging. During the task, participants repeatedly performed a short five-element sequence with their non-dominant (left) hand. As predicted, such unimanual sequence production was associated with lateralized activation of the right somatomotor cortex (SMC). Using this "active" region as a seed, here we show that unimanual performance of the motor sequence relies on functional segregation between the two SMC and selective integration between the "active" SMC and supplementary motor area. Whereas, greater segregation between the two SMC was associated with gains in performance rate, greater segregation within the "active" SMC itself was associated with more consistent performance by the end of training. Nether the resting-state FC patterns within the somatomotor network nor their relative modulation by the task state predicted these behavioral benefits of learning. Our results suggest that task-induced FC changes reflect reconfiguration of the connectivity patterns within the somatomotor network rather than a simple amplification or silencing of its intrinsic dynamics. Such reconfiguration not only supports motor behavior but may also predict learning.