Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4

A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning

Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.

Perceptual learning improves discrimination but does not reduce distortions in appearance

Human perceptual sensitivity often improves with training, a phenomenon known as "perceptual learning." Another important perceptual dimension is appearance, the subjective sense of stimulus magnitude. Are training-induced improvements in sensitivity accompanied by more accurate appearance? Here, we examined this question by measuring both discrimination (sensitivity) and estimation (appearance) responses to near-horizontal motion directions, which are known to be repulsed away from horizontal. Participants performed discrimination and estimation tasks before and after training in either the discrimination or the estimation task or none (control group). Human observers who trained in either discrimination or estimation exhibited improvements in discrimination accuracy, but estimation repulsion did not decrease; instead, it either persisted or increased. Hence, distortions in perception can be exacerbated after perceptual learning. We developed a computational observer model in which perceptual learning arises from increases in the precision of underlying neural representations, which explains this counterintuitive finding. For each observer, the fitted model accounted for discrimination performance, the distribution of estimates, and their changes with training. Our empirical findings and modeling suggest that learning enhances distinctions between categories, a potentially important aspect of real-world perception and perceptual learning.

Effect of muscarinic blockade on the speed of attention shifting, read-out delays and learning

The study aimed to investigate to what extent blockade of muscarinic receptors affects the speed of endogenous versus exogenous attentional shift times, and how it affects learning of attention shifting, cue detection and signal readout. Subjects viewed an array of 10 moving clocks and reported the time a clock indicated when cued. Target clocks were indicated by peripheral or central cues, including conditions of pre-cuing. For peripheral and central cuing, it yielded a compound measure of how long it took to detect the cue, shift attention to the relevant clock and read the time on the clock. For the pre-cue condition it yielded a measure of how long it took to detect the cue and read the time on the clock when attention could have been pre-allocated to the target clock. In study 1, each subject participated in 2 sessions (scopolamine/placebo), whereby the order of drug intake was counterbalanced across subjects, and subjects were blinded to conditions. Scopolamine/placebo was administered before a psychophysical experiment was conducted. In study 2, the effect of muscarinic blockade on learning induced improvements of cue detection, attention shift times (for exogenous and endogenous conditions), and signal readout was investigated. Here scopolamine/placebo was administered immediately after the first (of two) psychophysical sessions, whereby a given subject either received scopolamine or placebo pills. Confirming previous results, we show that pre-cuing resulted in the shortest read-out delays, followed by exogenous cuing, with endogenous read-out delays being slowest. Scopolamine application increased readout-delays in a dose dependent manner. This was mainly driven by increased readout-delays for pre-cue conditions, and to some extent for exogenous cue conditions. It suggests that muscarinic blockade affected the ability to pre-allocated attention to a cued location, as well as to react to peripheral cues. Additionally, blockade of muscarinic receptors immediately after the first session reduced learning dependent improvement of read-out delays. These results demonstrate that muscarinic receptors play an important in detecting cues, and fast read-out of cued information, and they contribute to the learning thereof.

cTBS over ventral cortex enhances depth perception

Stereoscopic capacities vary widely across the normal population. It has become increasingly apparent, however, that mechanisms underlying stereoscopic depth perception retain a considerable degree of plasticity through adulthood. Here, we contrast the capacity for neurostimulation in the form of continuous theta-burst stimulation (cTBS) over strategically-chosen sites in the visual cortex to bring about improvements in stereoscopic depth perception. cTBS was delivered to occipital cortex (V1/V2), lateral occipital complex (LOC), along with a control site (Cz). We measured performance on depth and luminance discrimination tasks before and after stimulation. We found a significant improvement in depth (but not luminance) discrimination performance following cTBS over LOC. By contrast, cTBS over occipital cortex and Cz did not affect performance on either task. These findings suggest that ventral (lateral-occipital) cortex is a key node for governing plasticity of stereoscopic vision in visually normal human observers. We speculate that cTBS exerts inhibitory influences that may suppress internal noise within the nervous system, leading to an improved read-out of depth features.

An essential role for the latero-medial secondary visual cortex in the acquisition and retention of visual perceptual learning in mice

Perceptual learning refers to any change in discrimination abilities as a result of practice, a fundamental process that improves the organism's response to the external environment. Visual perceptual learning (vPL) is supposed to rely on functional rearrangements in brain circuity occurring at early stages of sensory processing, with a pivotal role for the primary visual cortex (V1). However, top-down inputs from higher-order visual areas (HVAs) have been suggested to play a key part in vPL, conveying information on attention, expectation and the precise nature of the perceptual task. A direct assessment of the possibility to modulate vPL by manipulating top-down activity in awake subjects is still missing. Here, we used a combination of chemogenetics, behavioral analysis and multichannel electrophysiological assessments to show a critical role in vPL acquisition and retention for neuronal activity in the latero-medial secondary visual cortex (LM), the prime source for top-down feedback projections reentering V1.

Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.

Improving Visual Working Memory with Cholinergic Deep Brain Stimulation

Acetylcholine is a critical modulatory neurotransmitter for cognitive function. Cholinergic drugs improve cognitive performance and enhance neuronal activity in the sensory and association cortices. An alternative means of improving cognitive function is through the use of deep brain stimulation. Prior animal studies have demonstrated that stimulation of the nucleus basalis of Meynert through DBS improves cognitive performance on a visual working memory task to the same degree as cholinesterase inhibitors. Additionally, unlike current pharmacological treatments for neurocognitive disorders, DBS does not lose efficacy over time and adverse effects are rare. These findings suggest that DBS may be a promising alternative for treating cognitive impairments in neurodegenerative disorders such as Alzheimer's disease. Thus, further research and human trials should be considered to assess the potential of DBS as a therapeutic treatment for these disorders.

Tissue-type plasminogen activator induces conditioned receptive field plasticity in the mouse auditory cortex

Tissue-type plasminogen activator (tPA) is a serine protease that is expressed in various compartments in the brain. It is involved in neuronal plasticity, learning and memory, and addiction. We evaluated whether tPA, exogenously applied, could influence neuroplasticity within the mouse auditory cortex. We used a frequency-pairing paradigm to determine whether neuronal best frequencies shift following the pairing protocol. tPA administration significantly affected the best frequency after pairing, whereby this depended on the pairing frequency relative to the best frequency. When the pairing frequency was above the best frequency, tPA caused a best frequency shift away from the conditioned frequency. tPA significantly widened auditory tuning curves. Our data indicate that regional changes in proteolytic activity within the auditory cortex modulate the fine-tuning of auditory neurons, supporting the function of tPA as a modulator of neuronal plasticity.

Context-based modulations of 3D vision are expertise dependent

An object's identity can influence depth-position judgments. The mechanistic underpinnings underlying this phenomenon are largely unknown. Here, we asked whether context-dependent modulations of stereoscopic depth perception are expertise dependent. In 2 experiments, we tested whether training that attaches meaning (i.e. classification labels) to otherwise novel, stereoscopically presented objects changes observers' sensitivity for judging their depth position. In Experiment 1, observers were randomly assigned to 3 groups: a Greeble-classification training group, an orientation-discrimination training group, or a no-training group, and were tested on their stereoscopic depth sensitivity before and after training. In Experiment 2, participants were tested before and after training while fMRI responses were concurrently imaged. Behaviorally, stereoscopic performance was significantly better following Greeble-classification (but not orientation-discrimination, or no-) training. Using the fMRI data, we trained support vector machines to predict whether the data were from the pre- or post-training sessions. Results indicated that classification accuracies in V4 were higher for the Greeble-classification group as compared with the orientation-discrimination group for which accuracies were at chance level. Furthermore, classification accuracies in V4 were negatively correlated with response times for Greeble identification. We speculate that V4 is implicated in an expertise-dependent, object-tuning manner that allows it to better guide stereoscopic depth retrieval.

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.

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.

Enhancement of visual perception by combining transcranial electrical stimulation and visual perceptual training

The visual system remains highly malleable even after its maturity or impairment. Our visual function can be enhanced through many ways, such as transcranial electrical stimulation (tES) and visual perceptual learning (VPL). TES can change visual function rapidly, but its modulation effect is short-lived and unstable. By contrast, VPL can lead to a substantial and long-lasting improvement in visual function, but extensive training is typically required. Theoretically, visual function could be further improved in a shorter time frame by combining tES and VPL than by solely using tES or VPL. Vision enhancement by combining these two methods concurrently is both theoretically and practically significant. In this review, we firstly introduced the basic concept and possible mechanisms of VPL and tES; then we reviewed the current research progress of visual enhancement using the combination of two methods in both general and clinical population; finally, we discussed the limitations and future directions in this field. Our review provides a guide for future research and application of vision enhancement and restoration by combining VPL and tES.

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.

Perceptual learning as a result of concerted changes in prefrontal and visual cortex

Our perceptual ability remarkably improves with training. Some studies on visual perceptual learning have shown refined neural representation of the trained stimulus in the visual cortex, whereas others have exclusively argued for improved readout and decision-making processes in the frontoparietal cortex. The mixed results have rendered the underlying neural mechanisms puzzling and hotly debated. By simultaneously recording from monkey visual area V4 and ventrolateral prefrontal cortex (PFC) implanted with microelectrode arrays, we dissected learning-induced cortical changes over the course of training the monkeys in a global form detection task. Decoding analysis dissociated two distinct components of neuronal population codes that were progressively and markedly enhanced in both V4 and PFC. One component was closely related to the target stimulus feature and was subject to task-dependent top-down modulation; it emerged earlier in V4 than PFC and its enhancement was specific to the trained configuration of the target stimulus. The other component of the neural code was entirely related to the animal's behavioral choice; it emerged earlier in PFC than V4 and its enhancement completely generalized to an untrained stimulus configuration. These results implicate two concurrent and synergistic learning processes: a perceptual process that is specific to the details of the trained stimulus feature and a cognitive process that is dependent on the total amount of learning experience in the trained task. When combined, these two learning processes were well predictive of the animal's learning behavior.

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.

Nucleus basalis stimulation enhances working memory by stabilizing stimulus representations in primate prefrontal cortical activity

Acetylcholine plays a critical role in the neocortex. Cholinergic agonists and acetylcholinesterase inhibitors can enhance cognitive functioning, as does intermittent electrical stimulation of the cortical source of acetylcholine, the nucleus basalis (NB) of Meynert. Here we show in two male monkeys how NB stimulation affects working memory and alters its neural code. NB stimulation increases dorsolateral prefrontal activity during the delay period of spatial working memory tasks and broadens selectivity for stimuli but does not strengthen phasic responses to each neuron's optimal visual stimulus. Paradoxically, despite this decrease in neuronal selectivity, performance improves in many task conditions, likely indicating increased delay period stability. Performance under NB stimulation does decline if distractors similar to the target are presented, consistent with reduced prefrontal selectivity. Our results indicate that stimulation of the cholinergic forebrain increases prefrontal neural activity, and this neuromodulatory tone can improve cognitive performance, subject to a stability-accuracy tradeoff.

Correlated structure of neuronal firing in macaque visual cortex limits information for binocular depth discrimination

Variability in cortical neural activity potentially limits sensory discriminations. Theoretical work shows that information required to discriminate two similar stimuli is limited by the correlation structure of cortical variability. We investigated these information-limiting correlations by recording simultaneously from visual cortical areas primary visual cortex (V1) and extrastriate area V4 in macaque monkeys performing a binocular, stereo depth discrimination task. Within both areas, noise correlations on a rapid temporal scale (20–30 ms) were stronger for neuron pairs with similar selectivity for binocular depth, meaning that these correlations potentially limit information for making the discrimination. Between-area correlations (V1 to V4) were different, being weaker for neuron pairs with similar tuning and having a slower temporal scale (100+ ms). Fluctuations in these information-limiting correlations just prior to the detection event were associated with changes in behavioral accuracy. Although these correlations limit the recovery of information about sensory targets, their impact may be curtailed by integrative processing of signals across multiple brain areas.

NEW & NOTEWORTHY Correlated noise reduces the stimulus information in visual cortical neurons during experimental performance of binocular depth discriminations. The temporal scale of these correlations is important. Rapid (20–30 ms) correlations reduce information within and between areas V1 and V4, whereas slow (>100 ms) correlations between areas do not. Separate cortical areas appear to act together to maintain signal fidelity. Rapid correlations reduce the neuronal signal difference between stimuli and adversely affect perceptual discrimination.

Stimulus-dependent relationships between behavioral choice and sensory neural responses

Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.

Coherent resonance in the distributed cortical network during sensory information processing

Neuronal brain network is a distributed computing system, whose architecture is dynamically adjusted to provide optimal performance of sensory processing. A small amount of visual information needed effortlessly be processed, activates neural activity in occipital and parietal areas. Conversely, a visual task which requires sustained attention to process a large amount of sensory information, involves a set of long-distance connections between parietal and frontal areas coordinating the activity of these distant brain regions. We demonstrate that while neural interactions result in coherence, the strongest connection is achieved through coherence resonance induced by adjusting intrinsic brain noise.

A common probabilistic framework for perceptual and statistical learning

System-level learning of sensory information is traditionally divided into two domains: perceptual learning that focuses on acquiring knowledge suitable for fine discrimination between similar sensory inputs, and statistical learning that explores the mechanisms that develop complex representations of unfamiliar sensory experiences. The two domains have been typically treated in complete separation both in terms of the underlying computational mechanisms and the brain areas and processes implementing those computations. However, a number of recent findings in both domains call in question this strict separation. We interpret classical and more recent results in the general framework of probabilistic computation, provide a unifying view of how various aspects of the two domains are interlinked, and suggest how the probabilistic approach can also alleviate the problem of dealing with widely different types of neural correlates of learning. Finally, we outline several directions along which our proposed approach fosters new types of experiments that can promote investigations of natural learning in humans and other species.