Context-dependent changes in functional circuitry in visual area MT

Neuronal thresholds and choice-related activity of otolith afferent fibers during heading perception

How activity of sensory neurons leads to perceptual decisions remains a challenge to understand. Correlations between choices and single neuron firing rates have been found early in vestibular processing, in the brainstem and cerebellum. To investigate the origins of choice-related activity, we have recorded from otolith afferent fibers while animals performed a fine heading discrimination task. We find that afferent fibers have similar discrimination thresholds as central cells, and the most sensitive fibers have thresholds that are only twofold or threefold greater than perceptual thresholds. Unlike brainstem and cerebellar nuclei neurons, spike counts from afferent fibers do not exhibit trial-by-trial correlations with perceptual decisions. This finding may reflect the fact that otolith afferent responses are poorly suited for driving heading perception because they fail to discriminate self-motion from changes in orientation relative to gravity. Alternatively, if choice probabilities reflect top-down inference signals, they are not relayed to the vestibular periphery.

Choice-correlated activity fluctuations underlie learning of neuronal category representation

The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. To elucidate the neural mechanisms underlying categorization, we constructed a cortical circuit model that is capable of learning a motion categorization task through reward-dependent plasticity. Here we show that stable category representations develop in neurons intermediate to sensory and decision layers if they exhibit choice-correlated activity fluctuations (choice probability). In the model, choice probability and task-specific interneuronal correlations emerge from plasticity of top-down projections from decision neurons. Specific model predictions are confirmed by analysis of single-neuron activity from the monkey parietal cortex, which reveals a mixture of directional and categorical tuning, and a positive correlation between category selectivity and choice probability. Beyond demonstrating a circuit mechanism for categorization, the present work suggests a key role of plastic top-down feedback in simultaneously shaping both neural tuning and correlated neural variability.

The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. Here, the authors demonstrate a critical role for choice-correlated activity fluctuations in the emergence of stable cortical category representations.

Stochastic transitions into silence cause noise correlations in cortical circuits

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.

Improved estimation and interpretation of correlations in neural circuits

Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150-350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive 'excitatory' interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative 'inhibitory' interactions were less selective. Because of its superior performance, this 'sparse+latent' estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix.

Natural grouping of neural responses reveals spatially segregated clusters in prearcuate cortex

A fundamental challenge in studying the frontal lobe is to parcellate this cortex into "natural" functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated subnetworks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes at least two spatially segregated subnetworks that differentially represent behavioral choice and reaction time. Importantly, these subnetworks are detectable during different behavioral states and, surprisingly, are defined better by "common noise" than task-evoked responses. Our parcellation process works well on "spontaneous" neural activity, and thus bears strong resemblance to the identification of "resting-state" networks in fMRI data sets. Our results demonstrate a powerful new tool for identifying cortical subnetworks by objective classification of simultaneously recorded electrophysiological activity.

Global cognitive factors modulate correlated response variability between V4 neurons

Recent studies have shown that cognitive factors such as spatial and feature-based attention, learning, and task-switching can change the extent to which the trial-to-trial variability in the responses of neurons in sensory cortex is shared between pairs of neurons (for review, see Cohen and Kohn, 2011). Global cognitive factors related to concentration, motivation, effort, arousal, or alertness also affect performance on perceptual tasks and the responses of individual neurons in many cortical areas (Spitzer et al., 1988; Spitzer and Richmond, 1991; Motter, 1993; Bichot et al., 2001; Hasegawa et al., 2004; Boudreau et al., 2006; Niwa et al., 2012). The question of how global cognitive factors affect correlated response variability is important because these factors likely vary both across and within all psychophysical and physiological studies. Furthermore, global cognitive factors might provide a convenient platform for studying the neuronal mechanisms underlying how cognitive factors affect correlated variability because they can be manipulated easily without training complex perceptual tasks. We recorded simultaneously from groups of neurons in visual area V4 while rhesus monkeys performed a contrast discrimination task whose difficulty changed in blocks of trials. We found that correlated variability decreased when the task was more difficult, even when the visual stimuli were far outside the receptive fields of the recorded neurons. Our results suggest that studying global cognitive factors might provide a general framework for studying how cognitive factors affect the responses of neurons throughout sensory cortex.

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

Feature attention for binocular disparity in primate area MT depends on tuning strength

Attending to a stimulus modulates the responses of sensory neurons that represent features of that stimulus, a phenomenon named "feature attention." For example, attending to a stimulus containing upward motion enhances the responses of upward-preferring direction-selective neurons in the middle temporal area (MT) and suppresses the responses of downward-preferring neurons, even when the attended stimulus is outside of the spatial receptive fields of the recorded neurons (Treue S, Martinez-Trujillo JC. Nature 399: 575-579, 1999). This modulation renders the representation of sensory information across a neuronal population more selective for the features present in the attended stimulus (Martinez-Trujillo JC, Treue S. Curr Biol 14: 744-751, 2004). We hypothesized that if feature attention modulates neurons according to their tuning preferences, it should also be sensitive to their tuning strength, which is the magnitude of the difference in responses to preferred and null stimuli. We measured how the effects of feature attention on MT neurons in rhesus monkeys (Macaca mulatta) depended on the relationship between features-in our case, direction of motion and binocular disparity-of the attended stimulus and a neuron's tuning for those features. We found that, as for direction, attention to stimuli containing binocular disparity cues modulated the responses of MT neurons and that the magnitude of the modulation depended on both a neuron's tuning preferences and its tuning strength. Our results suggest that modulation by feature attention may depend not just on which features a neuron represents but also on how well the neuron represents those features.

High-fidelity coding with correlated neurons

Positive correlations in the activity of neurons are widely observed in the brain. Previous studies have shown these correlations to be detrimental to the fidelity of population codes, or at best marginally favorable compared to independent codes. Here, we show that positive correlations can enhance coding performance by astronomical factors. Specifically, the probability of discrimination error can be suppressed by many orders of magnitude. Likewise, the number of stimuli encoded—the capacity—can be enhanced more than tenfold. These effects do not necessitate unrealistic correlation values, and can occur for populations with a few tens of neurons. We further show that both effects benefit from heterogeneity commonly seen in population activity. Error suppression and capacity enhancement rest upon a pattern of correlation. Tuning of one or several effective parameters can yield a limit of perfect coding: the corresponding pattern of positive correlation leads to a ‘lock-in’ of response probabilities that eliminates variability in the subspace relevant for stimulus discrimination. We discuss the nature of this pattern and we suggest experimental tests to identify it.

Traditionally, sensory neuroscience has focused on correlating inputs from the physical world with the response of a single neuron. Two stimuli can be distinguished solely from the response of one neuron if one stimulus elicits a response and the other does not. But as soon as one departs from extremely simple stimuli, single-cell coding becomes less effective, because cells often respond weakly and unreliably. High fidelity coding then relies upon populations of cells, and correlation among those cells can greatly affect the neural code. While previous theoretical studies have demonstrated a potential coding advantage of correlation, they allowed only a marginal improvement in coding power. Here, we present a scenario in which a pattern of correlation among neurons in a population yields an improvement in coding performance by several orders of magnitude. By “improvement” we mean that a neural population is much better at both distinguishing stimuli and at encoding a large number of them. The scenario we propose does not invoke unrealistic values of correlation. What is more, it is even effective for small neural populations and in subtle cases in which single-cell coding fails utterly. These results demonstrate a previously unappreciated potential for correlated population coding.

Attention can either increase or decrease spike count correlations in visual cortex

Visual attention enhances the responses of visual neurons that encode the attended location. Several recent studies have shown that attention also decreases correlations between fluctuations in the responses of pairs of neurons (termed spike count correlation or r(SC)). These results are consistent with two hypotheses. First, attention-related changes in rate and r(SC) might be linked (perhaps through a common mechanism), with attention always decreasing r(SC). Second, attention might either increase or decrease r(SC), possibly depending on the role of the neurons in the behavioral task. We recorded simultaneously from dozens of neurons in area V4 while monkeys performed a discrimination task. We found strong evidence in favor of the second hypothesis, showing that attention can flexibly increase or decrease correlations depending on whether the neurons provide evidence for the same or opposite choices. These results place important constraints on models of the neuronal mechanisms underlying cognitive factors.

Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment

It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information.

The Markov Chain Monte Carlo (MCMC) approach to probabilistic inference for a distribution is to draw a sequence of samples from and to carry out computational operations via simple online computations on such a sequence. But such a sequential computational process takes time, and therefore this simple version of the MCMC approach runs into problems when one needs to carry out probabilistic inference for rapidly varying distributions. This difficulty also affects all currently existing models for emulating MCMC sampling by networks of stochastically firing neurons. We show here that by moving to a space-rate approach where salient probabilities are encoded through the spiking activity of ensembles of neurons, rather than by single neurons, this problem can be solved. In this way even theoretically optimal models for dealing with time varying distributions through sequential Monte Carlo sampling, so called particle filters, can be emulated by networks of spiking neurons. Each spike of a neuron in an ensemble represents in this approach a “particle” (or vote) for a particular value of a time-varying random variable. In other words, neural circuits can speed up computations based on Monte Carlo sampling through their inherent parallelism.

Temporal pattern processing in songbirds

Understanding how the brain perceives, organizes and uses patterned information is directly related to the neurobiology of language. Given the present limitations, such knowledge at the scale of neurons, neural circuits and neural populations can only come from non-human models, focusing on shared capacities that are relevant to language processing. Here we review recent advances in the behavioral and neural basis of temporal pattern processing of natural auditory communication signals in songbirds, focusing on European starlings. We suggest a general inhibitory circuit for contextual modulation that can act to control sensory representations based on patterning rules.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

State dependence of noise correlations in macaque primary visual cortex

Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1-2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations but can also be estimated and accounted for based on neuronal population activity.

ROC-based estimates of neural-behavioral covariations using matched filters

Correlations between responses in visual cortex and perceptual performance help draw a functional link between neural activity and visually guided behavior. These correlations are commonly derived with ROC-based neural-behavioral covariances (referred to as choice or detect probability) using boxcar analysis windows. Although boxcar windows capture the covariation between neural activity and behavior during steady-state stimulus presentations, they are not optimized to capture these correlations during short time-varying visual inputs. In this study, we implemented a matched-filter technique, combined with cross-validation, to improve the estimation of ROC-based neural-behavioral covariance under short and dynamic stimulus conditions. We show that this approach maximizes the area under the ROC curve and converges to the true neural-behavioral covariance using a Poisson spiking model. We also demonstrate that the matched filter, combined with cross-validation, reveals the dynamics of the neural-behavioral covariations of individual MT neurons during the detection of a brief motion stimulus.

Decision-related activity in sensory neurons may depend on the columnar architecture of cerebral cortex

Many studies have reported correlations between the activity of sensory neurons and animals' judgments in discrimination tasks. Here, we suggest that such neuron-behavior correlations may require a cortical map for the task relevant features. This would explain why studies using discrimination tasks based on disparity in area V1 have not found these correlations: V1 contains no map for disparity. This scheme predicts that activity of V1 neurons correlates with decisions in an orientation-discrimination task. To test this prediction, we trained two macaque monkeys in a coarse orientation discrimination task using band-pass-filtered dynamic noise. The two orientations were always 90° apart and task difficulty was controlled by varying the orientation bandwidth of the filter. While the trained animals performed this task, we recorded from orientation-selective V1 neurons (n = 82, n = 31 for Monkey 1, n = 51 for Monkey 2). For both monkeys, we observed significant correlation (quantified as "choice probabilities") of the V1 activity with the monkeys' perceptual judgments (mean choice probability 0.54, p = 10(-5)). In one of these animals, we had previously measured choice probabilities in a disparity discrimination task in V1, which had been at chance (0.49, not significantly different from 0.5). The choice probabilities in this monkey for the orientation discrimination task were significantly larger than those for the disparity discrimination task (p = 0.032). These results are predicted by our suggestion that choice probabilities are only observed for cortical sensory neurons that are organized in maps for the task-relevant feature.

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.

Gamma synchrony predicts neuron-neuron correlations and correlations with motor behavior in extrastriate visual area MT

Correlated variability of neuronal responses is an important factor in estimating sensory parameters from a population response. Large correlations among neurons reduce the effective size of a neural population and increase the variation of the estimates. They also allow the activity of one neuron to be informative about impending perceptual decisions or motor actions on single trials. In extrastriate visual area MT of the rhesus macaque, for example, some but not all neurons show nonzero "choice probabilities" for perceptual decisions or non-zero "MT-pursuit" correlations between the trial-by-trial variations in neural activity and smooth pursuit eye movements. To understand the functional implications of zero versus nonzero correlations between neural responses and impending perceptions or actions, we took advantage of prior observations that specific frequencies of local field potentials reflect the correlated activity of neurons. We found that the strength of the spike-field coherence of a neuron in the gamma-band frequency range is related to the size of its MT-pursuit correlations for eye direction, as well as to the size of the neuron-neuron correlations. Spike-field coherence predicts MT-pursuit correlations better for direction than for speed, perhaps because the topographic organization of direction preference in MT is more amenable to creating meaningful local field potentials. We suggest that the relationship between spiking and local-field potentials is stronger for neurons that have larger correlations with their neighbors; larger neuron-neuron correlations create stronger MT-pursuit correlations. Neurons that lack strong correlations with their neighbors also have weaker correlations with pursuit behavior, but still could drive pursuit strongly.

Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory

Prefrontal persistent activity during the delay of spatial working memory tasks is thought to maintain spatial location in memory. A 'bump attractor' computational model can account for this physiology and its relationship to behavior. However, direct experimental evidence linking parameters of prefrontal firing to the memory report in individual trials is lacking, and, to date, no demonstration exists that bump attractor dynamics underlies spatial working memory. We analyzed monkey data and found model-derived predictive relationships between the variability of prefrontal activity in the delay and the fine details of recalled spatial location, as evident in trial-to-trial imprecise oculomotor responses. Our results support a diffusing bump representation for spatial working memory instantiated in persistent prefrontal activity. These findings reinforce persistent activity as a basis for spatial working memory, provide evidence for a continuous prefrontal representation of memorized space and offer experimental support for bump attractor dynamics mediating cognitive tasks in the cortex.

Bottom-up and top-down attention: different processes and overlapping neural systems

The brain is limited in its capacity to process all sensory stimuli present in the physical world at any point in time and relies instead on the cognitive process of attention to focus neural resources according to the contingencies of the moment. Attention can be categorized into two distinct functions: bottom-up attention, referring to attentional guidance purely by externally driven factors to stimuli that are salient because of their inherent properties relative to the background; and top-down attention, referring to internal guidance of attention based on prior knowledge, willful plans, and current goals. Over the past few years, insights on the neural circuits and mechanisms of bottom-up and top-down attention have been gained through neurophysiological experiments. Attention affects the mean neuronal firing rate as well as its variability and correlation across neurons. Although distinct processes mediate the guidance of attention based on bottom-up and top-down factors, a common neural apparatus, the frontoparietal network, is essential in both types of attentional processes.

Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex

Correlations in the spiking activity of neurons have been found in many regions of the cortex under multiple experimental conditions and are postulated to have important consequences for neural population coding. While there is a large body of extracellular data reporting correlations of various strengths, the subthreshold events underlying the origin and magnitude of signal-independent correlations (called noise or spike count correlations) are unknown. Here we investigate, using intracellular recordings, how synaptic input correlations from shared presynaptic neurons translate into membrane potential and spike-output correlations. Using a pharmacologically activated thalamocortical slice preparation, we perform simultaneous recordings from pairs of layer IV neurons in the auditory cortex of mice and measure synaptic potentials/currents, membrane potentials, and spiking outputs. We calculate cross-correlations between excitatory and inhibitory inputs to investigate correlations emerging from the network. We furthermore evaluate membrane potential correlations near resting potential to study how excitation and inhibition combine and affect spike-output correlations. We demonstrate directly that excitation is correlated with inhibition thereby partially canceling each other and resulting in weak membrane potential and spiking correlations between neurons. Our data suggest that cortical networks are set up to partially cancel correlations emerging from the connections between neurons. This active decorrelation is achieved because excitation and inhibition closely track each other. Our results suggest that the numerous shared presynaptic inputs do not automatically lead to increased spiking correlations.

Elevated correlations in neuronal ensembles of mouse auditory cortex following parturition

The auditory cortex is malleable by experience. Previous studies of auditory plasticity have described experience-dependent changes in response profiles of single neurons or changes in global tonotopic organization. However, experience-dependent changes in the dynamics of local neural populations have remained unexplored. In this study, we examined the influence of a dramatic yet natural experience in the life of female mice, giving birth and becoming a mother on single neurons and neuronal ensembles in the primary auditory cortex (A1). Using in vivo two-photon calcium imaging and electrophysiological recordings from layer 2/3 in A1 of mothers and age-matched virgin mice, we monitored changes in the responses to a set of artificial and natural sounds. Population dynamics underwent large changes as measured by pairwise and higher-order correlations, with noise correlations increasing as much as twofold in lactating mothers. Concomitantly, changes in response properties of single neurons were modest and selective. Remarkably, despite the large changes in correlations, information about stimulus identity remained essentially the same in the two groups. Our results demonstrate changes in the correlation structure of neuronal activity as a result of a natural life event.

Origins of correlated spiking in the mammalian olfactory bulb

Mitral/tufted (M/T) cells of the main olfactory bulb transmit odorant information to higher brain structures. The relative timing of action potentials across M/T cells has been proposed to encode this information and to be critical for the activation of downstream neurons. Using ensemble recordings from the mouse olfactory bulb in vivo, we measured how correlations between cells are shaped by stimulus (odor) identity, common respiratory drive, and other cells' activity. The shared respiration cycle is the largest source of correlated firing, but even after accounting for all observable factors a residual positive noise correlation was observed. Noise correlation was maximal on a ∼100-ms timescale and was seen only in cells separated by <200 µm. This correlation is explained primarily by common activity in groups of nearby cells. Thus, M/T-cell correlation principally reflects respiratory modulation and sparse, local network connectivity, with odor identity accounting for a minor component.

Diversity, heterogeneity and orientation-dependent variation of spike count correlation in the cat visual cortex

Cortical neurons are known to be noisy encoders of information, showing large response variabilities with repeated presentations of identical stimuli. These spike count variabilities are correlated over the cell population and their neuronal mechanism and functional significance have not been well understood. Recently there has been much debate over the magnitude of the population mean of the correlation, ranging from 0.1 to 0.2 down to nearly zero. We performed multi-neuron recordings on the cat visual cortex and found that the population mean did not necessarily represent the nature of correlated variabilities because the spike count correlation showed significant diversity and heterogeneity. Although the population mean was relatively small (0.06), the correlations of individual unit pairs were distributed over a broad range, extending to both positive and negative values. In most of the recording sessions of local cell populations (83%), significantly positive correlations coexisted with significantly negative ones in different unit pairs. Furthermore, nearly 20% of the unit pairs showed significant variation in the spike count correlation for different stimulus orientations. Correlation analysis between the spike count correlation and the firing activity of the unit pair suggested that the orientation tuning properties of the two quantities were unlikely to have originated from a common neuronal mechanism. Diversity, heterogeneity and context-dependent variation suggests that the correlated spike count variabilities originate not from fixed anatomical connections but rather from the dynamic interaction of neuronal networks.

Monkey area MT latencies to speed changes depend on attention and correlate with behavioral reaction times

Selective visual attention is known to be associated with characteristic modulations of neuronal activity in early visual cortex, but there is only rare evidence showing that these neuronal modulations are directly related to attention-dependent behavioral improvements. Here, we describe a strong, transient increase in the response of neurons in the mediotemporal (MT) area to behaviorally relevant speed changes that is not only modulated by attention but also highly correlated with the animal's performance. In trials with fast reaction time (RT), this transient component occurs with short latency, whereas latency increases monotonically with slower RT. Importantly, RTs are related not to the firing rate modulation during sustained attentive tracking of the target prior to the speed change but to the variability of the neuronal response. Our findings suggest a direct link between attention-dependent response modulations in early visual cortex and improved behavioral performance.

Associative learning enhances population coding by inverting interneuronal correlation patterns

Learning-dependent cortical encoding has been well described in single neurons. But behaviorally relevant sensory signals drive the coordinated activity of millions of cortical neurons; whether learning produces stimulus-specific changes in population codes is unknown. Because the pattern of firing rate correlations between neurons--an emergent property of neural populations--can significantly impact encoding fidelity, we hypothesize that it is a target for learning. Using an associative learning procedure, we manipulated the behavioral relevance of natural acoustic signals and examined the evoked spiking activity in auditory cortical neurons in songbirds. We show that learning produces stimulus-specific changes in the pattern of interneuronal correlations that enhance the ability of neural populations to recognize signals relevant for behavior. This learning-dependent enhancement increases with population size. The results identify the pattern of interneuronal correlation in neural populations as a target of learning that can selectively enhance the representations of specific sensory signals.

Encoding by synchronization in the primate striatum

Information is encoded in the nervous system through the discharge and synchronization of single neurons. The striatum, the input stage of the basal ganglia, is divided into three territories: the putamen, the caudate, and the ventral striatum, all of which converge onto the same motor pathway. This parallel organization suggests that there are multiple and competing systems in the basal ganglia network controlling behavior. To explore which mechanism(s) enables the different striatal domains to encode behavioral events and to control behavior, we compared the neural activity of phasically active neurons [medium spiny neurons (MSNs), presumed projection neurons] and tonically active neurons (presumed cholinergic interneurons) across striatal territories from monkeys during the performance of a well practiced task. Although neurons in all striatal territories displayed similar spontaneous discharge properties and similar temporal modulations of their discharge rates to the behavioral events, their correlation structure was profoundly different. The distributions of signal and noise correlation of pairs of putamen MSNs were strongly shifted toward positive correlations and these two measures were correlated. In contrast, MSN pairs in the caudate and ventral striatum displayed symmetrical, near-zero signal and noise correlation distributions. Furthermore, only putamen MSN pairs displayed different noise correlation dynamics to rewarding versus neutral/aversive cues. Similarly, the noise correlation between tonically active neuron pairs was stronger in the putamen than in the caudate. We suggest that the level of synchronization of the neuronal activity and its temporal dynamics differentiate the striatal territories and may thus account for the different roles that striatal domains play in behavioral control.

Top-down influences on visual processing

Re-entrant or feedback pathways between cortical areas carry rich and varied information about behavioural context, including attention, expectation, perceptual tasks, working memory and motor commands. Neurons receiving such inputs effectively function as adaptive processors that are able to assume different functional states according to the task being executed. Recent data suggest that the selection of particular inputs, representing different components of an association field, enable neurons to take on different functional roles. In this Review, we discuss the various top-down influences exerted on the visual cortical pathways and highlight the dynamic nature of the receptive field, which allows neurons to carry information that is relevant to the current perceptual demands.

Neuronal mechanisms of visual perceptual learning

Numerous psychophysical studies have described perceptual learning as long-lasting improvements in perceptual discrimination and detection capabilities following practice. Where and how long-term plastic changes occur in the brain is central to understanding the neural basis of perceptual learning. Here, neurophysiological research using non-human primates is reviewed to address the neural mechanisms underlying visual perceptual learning. Previous studies have shown that training either has no effect on or only weakly alters the sensitivity of neurons in early visual areas, but more recent evidence indicates that training can cause long-term changes in how sensory signals are read out in the later stages of decision making. These results are discussed in the context of learning specificity, which has been crucial in interpreting the mechanisms underlying perceptual learning. The possible mechanisms that support learning-related plasticity are also discussed.

Structure of spike count correlations reveals functional interactions between neurons in dorsolateral prefrontal cortex area 8a of behaving primates

Neurons within the primate dorsolateral prefrontal cortex (dlPFC) are clustered in microcolumns according to their visuospatial tuning. One issue that remains poorly investigated is how this anatomical arrangement influences functional interactions between neurons during behavior. To investigate this question we implanted 4 mm×4 mm multielectrode arrays in two macaques' dlPFC area 8a and measured spike count correlations (r_sc) between responses of simultaneously recorded neurons when animals maintained stationary gaze. Positive and negative r_sc were significantly higher than predicted by chance across a wide range of inter-neuron distances (from 0.4 to 4 mm). Positive r_sc were stronger between neurons with receptive fields (RFs) separated by ≤90° of angular distance and progressively decreased as a function of inter-neuron physical distance. Negative r_sc were stronger between neurons with RFs separated by >90° and increased as a function of inter-neuron distance. Our results show that short- and long-range functional interactions between dlPFC neurons depend on the physical distance between them and the relationship between their visuospatial tuning preferences. Neurons with similar visuospatial tuning show positive r_sc that decay with inter-neuron distance, suggestive of excitatory interactions within and between adjacent microcolumns. Neurons with dissimilar tuning from spatially segregated microcolumns show negative r_sc that increase with inter-neuron distance, suggestive of inhibitory interactions. This pattern of results shows that functional interactions between prefrontal neurons closely follow the pattern of connectivity reported in anatomical studies. Such interactions may be important for the role of the prefrontal cortex in the allocation of attention to targets in the presence of competing distracters.

Decorrelation by recurrent inhibition in heterogeneous neural circuits

The activity of neurons is correlated, and this correlation affects how the brain processes information. We study the neural circuit mechanisms of correlations by analyzing a network model characterized by strong and heterogeneous interactions: excitatory input drives the fluctuations of neural activity, which are counterbalanced by inhibitory feedback. In particular, excitatory input tends to correlate neurons, while inhibitory feedback reduces correlations. We demonstrate that heterogeneity of synaptic connections is necessary for this inhibition of correlations. We calculate statistical averages over the disordered synaptic interactions and apply our findings to both a simple linear model and a more realistic spiking network model. We find that correlations at zero time lag are positive and of magnitude K(-1/2) where K is the number of connections to a neuron. Correlations at longer timescales are of smaller magnitude, of order K(-1), implying that inhibition of correlations occurs quickly, on a timescale of K(-1/2). The small magnitude of correlations agrees qualitatively with physiological measurements in the cerebral cortex and basal ganglia. The model could be used to study correlations in brain regions dominated by recurrent inhibition, such as the striatum and globus pallidus.

Functional specializations of the ventral intraparietal area for multisensory heading discrimination

The ventral intraparietal area (VIP) of the macaque brain is a multimodal cortical region with directionally selective responses to visual and vestibular stimuli. To explore how these signals contribute to self-motion perception, neural activity in VIP was monitored while macaques performed a fine heading discrimination task based on vestibular, visual, or multisensory cues. For neurons with congruent visual and vestibular heading tuning, discrimination thresholds improved during multisensory stimulation, suggesting that VIP (like the medial superior temporal area; MSTd) may contribute to the improved perceptual discrimination seen during cue combination. Unlike MSTd, however, few VIP neurons showed opposite visual/vestibular tuning over the range of headings relevant to behavior, and those few cells showed reduced sensitivity under cue combination. Our data suggest that the heading tuning of some VIP neurons may be locally remodeled to increase the proportion of cells with congruent tuning over the behaviorally relevant stimulus range. VIP neurons also showed much stronger trial-by-trial correlations with perceptual decisions (choice probabilities; CPs) than MSTd neurons. While this may suggest that VIP neurons are more strongly linked to heading perception, we also find that correlated noise is much stronger among pairs of VIP neurons, with noise correlations averaging 0.14 in VIP as compared with 0.04 in MSTd. Thus, the large CPs in VIP could be a consequence of strong interneuronal correlations. Together, our findings suggest that VIP neurons show specializations that may make them well equipped to play a role in multisensory integration for heading perception.

Inferring decoding strategies from choice probabilities in the presence of correlated variability

The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship that is often quantified by choice probabilities. Although choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. We derive the mathematical relationship between choice probabilities, read-out weights and correlated variability in the standard neural decision-making model. Our solution allowed us to prove and generalize earlier observations on the basis of numerical simulations and to derive new predictions. Notably, our results indicate how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we developed a test to decide whether the decoding weights of individual neurons are optimal for the task, even without knowing the underlying correlations. We confirmed the practicality of our approach using simulated data from a realistic population model. Thus, our findings provide a theoretical foundation for a growing body of experimental results on choice probabilities and correlations.

Correlated variability in laminar cortical circuits

Despite the fact that strong trial-to-trial correlated variability in responses has been reported in many cortical areas, recent evidence suggests that neuronal correlations are much lower than previously thought. Here, we used multicontact laminar probes to revisit the issue of correlated variability in primary visual (V1) cortical circuits. We found that correlations between neurons depend strongly on local network context--whereas neurons in the input (granular) layers showed virtually no correlated variability, neurons in the output layers (supragranular and infragranular) exhibited strong correlations. The laminar dependence of noise correlations is consistent with recurrent models in which neurons in the granular layer receive intracortical inputs from nearby cells, whereas supragranular and infragranular layer neurons receive inputs over larger distances. Contrary to expectation that the output cortical layers encode stimulus information most accurately, we found that the input network offers superior discrimination performance compared to the output networks.

Neural changes after training to perform cognitive tasks

Cognitive operations requiring working memory rely on the activity of neurons in areas of the association cortex, most prominently the lateral prefrontal cortex. Human imaging and animal neurophysiological studies indicate that this activity is shaped by learning, though much is unknown about how much training alters neural activity and cortical organization. Results from non-human primates demonstrate that prior to any training in cognitive tasks, prefrontal neurons respond to stimuli, exhibit persistent activity after their offset, and differentiate between matching and non-matching stimuli presented in sequence. A number of important changes also occur after training in a working memory task. More neurons are recruited by the stimuli and exhibit higher firing rates, particularly during the delay period. Operant stimuli that need to be recognized in order to perform the task elicit higher overall rates of responses, while the variability of individual discharges and correlation of discharges between neurons decrease after training. New information is incorporated in the activity of a small population of neurons highly specialized for the task and in a larger population of neurons that exhibit modest task related information, while information about other aspects of stimuli remains present in neuronal activity. Despite such changes, the relative selectivity of the dorsal and ventral aspect of the lateral prefrontal cortex is not radically altered with regard to spatial and non-spatial stimuli after training. Collectively, these results provide insights on the nature and limits of cortical plasticity mediating cognitive tasks.

Choice-related activity and correlated noise in subcortical vestibular neurons

Functional links between neuronal activity and perception are studied by examining trial-by-trial correlations (choice probabilities) between neural responses and perceptual decisions. We addressed fundamental issues regarding the nature and origin of choice probabilities by recording from subcortical (brainstem and cerebellar) neurons in rhesus monkeys during a vestibular heading discrimination task. Subcortical neurons showed robust choice probabilities that exceeded those seen in cortex (area MSTd) under identical conditions. The greater choice probabilities of subcortical neurons could be predicted by a stronger dependence of correlated noise on tuning similarity, as revealed by population decoding. Significant choice probabilities were observed almost exclusively for neurons that responded selectively to translation, whereas neurons that represent net gravito-inertial acceleration did not show choice probabilities. These findings suggest that the emergence of choice probabilities in the vestibular system depends on a critical signal transformation that occurs in subcortical pathways to distinguish translation from orientation relative to gravity.

Impact of correlated neural activity on decision-making performance

Stimulus from the environment that guides behavior and informs decisions is encoded in the firing rates of neural populations. Neurons in the populations, however, do not spike independently: spike events are correlated from cell to cell. To what degree does this apparent redundancy have an impact on the accuracy with which decisions can be made and the computations required to optimally decide? We explore these questions for two illustrative models of correlation among cells. Each model is statistically identical at the level of pairwise correlations but differs in higher-order statistics that describe the simultaneous activity of larger cell groups. We find that the presence of correlations can diminish the performance attained by an ideal decision maker to either a small or large extent, depending on the nature of the higher-order correlations. Moreover, although this optimal performance can in some cases be obtained using the standard integration-to-bound operation, in others it requires a nonlinear computation on incoming spikes. Overall, we conclude that a given level of pairwise correlations, even when restricted to identical neural populations, may not always indicate redundancies that diminish decision-making performance.

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP). In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1) empirical questions not yet answered by existing data; (2) implementation issues related to how neural circuits could actually implement the mechanisms suggested by both extracellular neurophysiology and psychophysics; and (3) ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general "encoding-decoding framework" that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities

Correlations between trial-to-trial fluctuations in the responses of individual sensory neurons and perceptual reports, commonly quantified with choice probability (CP), have been widely used as an important tool for assessing the contributions of neurons to behavior. These correlations are usually weak and often require a large number of trials for a reliable estimate. Therefore, working with measures such as CP warrants care in data analysis as well as rigorous controls during data collection. Here we identify potential confounds that can arise in data analysis and lead to biased estimates of CP, and suggest methods to avoid the bias. In particular, we show that the common practice of combining neuronal responses across different stimulus conditions with z-score normalization can result in an underestimation of CP when the ratio of the numbers of trials for the two behavioral response categories differs across the stimulus conditions. We also discuss the effects of using variable time intervals for quantifying neuronal response on CP measurements. Finally, we demonstrate that serious artifacts can arise in reaction time tasks that use varying measurement intervals if the mean neuronal response and mean behavioral performance vary over time within trials. To emphasize the importance of addressing these concerns in neurophysiological data, we present a set of data collected from V1 cells in macaque monkeys while the animals performed a detection task.

Response variability of frontal eye field neurons modulates with sensory input and saccade preparation but not visual search salience

Discharge rate modulation of frontal eye field (FEF) neurons has been identified with a representation of visual search salience (physical conspicuity and behavioral relevance) and saccade preparation. We tested whether salience or saccade preparation are evident in the trial-to-trial variability of discharge rate. We quantified response variability via the Fano factor in FEF neurons recorded in monkeys performing efficient and inefficient visual search tasks. Response variability declined following stimulus presentation in most neurons, but despite clear discharge rate modulation, variability did not change with target salience. Instead, we found that response variability was modulated by stimulus luminance and the number of items in the visual field independently of attentional demands. Response variability declined to a minimum before saccade initiation, and presaccadic response variability was directionally tuned. In addition, response variability was correlated with the response time of memory-guided saccades. These results indicate that the trial-by-trial response variability of FEF neurons reflects saccade preparation and the strength of sensory input, but not visual search salience or attentional allocation.

Correlated discharges in the primate prefrontal cortex before and after working memory training

The correlation of discharges between single neurons can provide information about the computations and network properties of neuronal populations during the performance of cognitive tasks. In recent years, dynamic modulation of neuronal correlations by attention has been revealed during the execution of behavioral tasks. Much less is known about the influence of learning and performing a task itself. We therefore sought to quantify the correlated firing of simultaneously recorded pairs of neurons in the prefrontal cortex of naïve monkeys that were only required to fixate, and to examine how this correlation was altered after they had learned to perform a working memory task. We found that the trial-to-trial correlation of discharge rates between pairs of neurons (noise correlation) differed across neurons depending on their responsiveness and selectivity for stimuli, even before training in a working memory task. After monkeys had learned to perform the task, correlated firing decreased overall, although the effects varied according to the functional properties of the neurons. The greatest decreases were observed on comparison of populations of neurons that exhibited elevated firing rates during the trial events and those that had more similar spatial and temporal tuning. Greater decreases in noise correlation were also observed for pairs comprising one fast spiking neuron (putative interneuron) and one regular spiking neuron (putative pyramidal neuron) than pairs comprising regular spiking neurons only. Our results demonstrate that learning and performance of a cognitive task alters the correlation structure of neuronal firing in the prefrontal cortex.

Feedforward origins of response variability underlying contrast invariant orientation tuning in cat visual cortex

Contrast invariant orientation tuning in simple cells of the visual cortex depends critically on contrast dependent trial-to-trial variability in their membrane potential responses. This observation raises the question of whether this variability originates from within the cortical circuit or the feedforward inputs from the lateral geniculate nucleus (LGN). To distinguish between these two sources of variability, we first measured membrane potential responses while inactivating the surrounding cortex, and found that response variability was nearly unaffected. We then studied variability in the LGN, including contrast dependence, and the trial-to-trial correlation in responses between nearby neurons. Variability decreased significantly with contrast, whereas correlation changed little. When these experimentally measured parameters of variability were applied to a feedforward model of simple cells that included realistic mechanisms of synaptic integration, contrast-dependent, orientation independent variability emerged in the membrane potential responses. Analogous mechanisms might contribute to the stimulus dependence and propagation of variability throughout the neocortex.

Variability of prefrontal neuronal discharges before and after training in a working memory task

Variability of neural discharges can be revealing about the computations and network properties of neuronal populations during the performance of cognitive tasks. We sought to quantify neuronal variability in the prefrontal cortex of naïve monkeys that were only required to fixate, and to examine how this measure was altered by learning and execution of a working memory task. We therefore performed analysis of a large database of recordings in the same animals, using the same stimuli, before and after training. Our results indicate that the Fano Factor, a measure of variability, differs across neurons depending on their functional properties both before and after learning. Fano Factor generally decreased after learning the task. Variability was modulated by task events and displayed lowest values during the stimulus presentation. Nonetheless, the decrease in variability after training was present even prior to the presentation of any stimuli, in the fixation period. The greatest decreases were observed comparing populations of neurons that exhibited elevated firing rate during the trial events. Our results offer insights on how properties of the prefrontal network are affected by performance of a cognitive task.

Decision-related activity in sensory neurons: correlations among neurons and with behavior

Neurons in early sensory cortex show weak but systematic correlations with perceptual decisions when trained animals perform at psychophysical threshold. These correlations are observed across repeated presentations of identical stimuli and cannot be explained by variation in external factors. The relationship between the activity of individual sensory neurons and the animal's behavioral choice means that even neurons in early sensory cortex carry information about an upcoming decision. This relationship, termed choice probability, may reflect the effect of fluctuations in neuronal firing rate on the animal's decision, but it can also reflect modulation of sensory responses by cognitive factors, or network properties such as variability that is shared among populations of neurons. Here, we review recent work clarifying the relationship among fluctuations in the responses of individual neurons, correlated variability, and behavior in a variety of tasks and cortical areas. We also discuss the possibility that choice probability may in part reflect the influence of cognitive factors on sensory neurons and explore the situations in which choice probability can be used to make inferences about the role of particular sensory neurons in the decision-making process.

The functional link between area MT neural fluctuations and detection of a brief motion stimulus

Fluctuations of neural firing rates in visual cortex are known to be correlated with variations in perceptual performance. It is important to know whether these fluctuations are functionally linked to perception in a causal manner or instead reflect non-causal processes that arise after the perceptual decision is made. We recorded from middle temporal (MT) neurons from monkey subjects while they detected the random occurrence of a brief 50 ms motion pulse that occurred in either of two (or simultaneously in both) random dot patches located in the same hemisphere. The receptive field parameters of the motion pulse were matched to that preferred by each MT neuron under study. This task contained uncertainty in both space and time because, on any given trial, the subjects did not know which patch would contain the motion pulse or when the motion pulse would occur. Covariations between MT activity and behavior began just before the motion pulse onset and peaked at the maximum neural response. These neural-behavioral covariations were strongest when only one patch contained the motion pulse and were still weakly present when a patch did not contain a motion pulse. A feedforward temporal integration model with two independent detector channels captured both the detection performance and evolution of the neural-behavior covariations over time and stimulus condition. The results suggest that, when detecting a brief visual stimulus, there is a causal relationship between fluctuations in neural activity and variations in behavior across trials.

The effect of noise correlations in populations of diversely tuned neurons

The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations.

Mapping spikes to sensations

Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research.

Perceptual learning reduces interneuronal correlations in macaque visual cortex

Responses of neurons in early visual cortex change little with training and appear insufficient to account for perceptual learning. Behavioral performance, however, relies on population activity, and the accuracy of a population code is constrained by correlated noise among neurons. We tested whether training changes interneuronal correlations in the dorsal medial superior temporal area, which is involved in multisensory heading perception. Pairs of single units were recorded simultaneously in two groups of subjects: animals trained extensively in a heading discrimination task, and "naive" animals that performed a passive fixation task. Correlated noise was significantly weaker in trained versus naive animals, which might be expected to improve coding efficiency. However, we show that the observed uniform reduction in noise correlations leads to little change in population coding efficiency when all neurons are decoded. Thus, global changes in correlated noise among sensory neurons may be insufficient to account for perceptual learning.