Attention improves performance primarily by reducing interneuronal correlations

Top-down modulation of lateral interactions in visual cortex

The primary visual cortex (V1) changes its computation according to the perceptual task being performed. We propose that this cognitive modulation results from gating of V1 intrinsic connections. To test this idea, using behavioral paradigms that engage top-down modulation of V1 contextual interactions, we recorded from chronically implanted electrode arrays in macaques. We observed task-dependent changes in interactions between V1 sites measured both by correlation between spike trains and by coherence between local field potentials (LFP-LFP coherence). The direction of the changes in aggregate activity, as measured by LFPs, depended on perceptual strategy: perceptual grouping increased LFP coherence between sites crucial for the task, whereas perceptual segregation lowered the LFP coherence. Using spiking activity as a measure, we found that the behaviorally driven changes in correlation structure between neurons dramatically increased the stimulus-related information that they convey; this additional increase in encoded information at the level of neuronal ensembles equals that obtained from task-driven reconfigurations of neural tuning curves. The improvements in information encoding were strongest for stimuli with greatest discrimination difficulty.

Population codes in the visual cortex

Every sensory event elicits activity in a broad population of cells that is distributed within and across cortical areas. How these neurons function together to represent the sensory environment is a major question in systems neuroscience. A number of proposals have been made, and recent advances in multi-neuronal recording have begun to allow researchers to test the predictions of these population-coding theories. In this review, I provide an introduction to some of the key concepts in population coding and describe several studies in the recent literature. The focus of this review is on sensory representation in the visual cortex and related perceptual decisions. The frameworks used to study population coding include population vectors, linear decoders, and Bayesian inference. Simple examples are provided to illustrate these concepts. Testing theories of population coding is an emerging subject in systems neuroscience, but advances in multi-neuronal recording and analysis suggest that an understanding is within reach.

Gating of sensory input by spontaneous cortical activity

The activity of neural populations is determined not only by sensory inputs but also by internally generated patterns. During quiet wakefulness, the brain produces spontaneous firing events that can spread over large areas of cortex and have been suggested to underlie processes such as memory recall and consolidation. Here we demonstrate a different role for spontaneous activity in sensory cortex: gating of sensory inputs. We show that population activity in rat auditory cortex is composed of transient 50-100 ms packets of spiking activity that occur irregularly during silence and sustained tone stimuli, but reliably at tone onset. Population activity within these packets had broadly consistent spatiotemporal structure, but the rate and also precise relative timing of action potentials varied between stimuli. Packet frequency varied with cortical state, with desynchronized state activity consistent with superposition of multiple overlapping packets. We suggest that such packets reflect the sporadic opening of a "gate" that allows auditory cortex to broadcast a representation of external sounds to other brain regions.

Neural variability, or lack thereof

We do not claim that the brain is completely deterministic, and we agree that noise may be beneficial in some cases. But we suggest that neuronal variability may be often overestimated, due to uncontrolled internal variables, and/or the use of inappropriate reference times. These ideas are not new, but should be re-examined in the light of recent experimental findings: trial-to-trial variability is often correlated across neurons, across trials, greater for higher-order neurons, and reduced by attention, suggesting that "intrinsic" sources of noise can only account for a minimal part of it. While it is obviously difficult to control for all internal variables, the problem of reference time can be largely avoided by recording multiple neurons at the same time, and looking at statistical structures in relative latencies. These relative latencies have another major advantage: they are insensitive to the variability that is shared across neurons, which is often a significant part of the total variability. Thus, we suggest that signal-to-noise ratios in the brain may be much higher than usually thought, leading to reactive systems, economic in terms of number of neurons, and energy efficient.

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.

Synchronous oscillatory neural ensembles for rules in the prefrontal cortex

Intelligent behavior requires acquiring and following rules. Rules define how our behavior should fit different situations. To understand its neural mechanisms, we simultaneously recorded from multiple electrodes in dorsolateral prefrontal cortex (PFC) while monkeys switched between two rules (respond to color versus orientation). We found evidence that oscillatory synchronization of local field potentials (LFPs) formed neural ensembles representing the rules: there were rule-specific increases in synchrony at "beta" (19-40 Hz) frequencies between electrodes. In addition, individual PFC neurons synchronized to the LFP ensemble corresponding to the current rule (color versus orientation). Furthermore, the ensemble encoding the behaviorally dominant orientation rule showed increased "alpha" (6-16 Hz) synchrony when preparing to apply the alternative (weaker) color rule. This suggests that beta-frequency synchrony selects the relevant rule ensemble, while alpha-frequency synchrony deselects a stronger, but currently irrelevant, ensemble. Synchrony may act to dynamically shape task-relevant neural ensembles out of larger, overlapping circuits.

Cortical circuits for the control of attention

How are some thoughts favored over others? A wealth of data at the level of single neurons has yielded candidate brain areas and mechanisms for our best-understood model: visual attention. Recent work has naturally evolved toward efforts at a more integrative, network, understanding. It suggests that focusing attention arises from interactions between widespread cortical and subcortical networks that may be regulated via their rhythmic synchronization.

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.

Effects of neural synchrony on surface EEG

It has long been assumed that the surface electroencephalography (EEG) signal depends on both the amplitude and spatial synchronization of underlying neural activity, though isolating their respective contribution remains elusive. To address this, we made simultaneous surface EEG measurements along with intracortical recordings of local field potentials (LFPs) in the primary visual cortex of behaving nonhuman primates. We found that trial-by-trial fluctuations in EEG power could be explained by a linear combination of LFP power and interelectrode temporal synchrony. This effect was observed in both stimulus and stimulus-free conditions and was particularly strong in the gamma range (30-100 Hz). Subsequently, we used pharmacological manipulations to show that neural synchrony can produce a positively modulated EEG signal even when the LFP signal is negatively modulated. Taken together, our results demonstrate that neural synchrony can modulate EEG signals independently of amplitude changes in neural activity. This finding has strong implications for the interpretation of EEG in basic and clinical research, and helps reconcile EEG response discrepancies observed in different modalities (e.g., EEG vs. functional magnetic resonance imaging) and different spatial scales (e.g., EEG vs. intracranial EEG).

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.

Long-term modification of cortical synapses improves sensory perception

Synapses and receptive fields of the cerebral cortex are plastic. However, changes to specific inputs must be coordinated within neural networks to ensure that excitability and feature selectivity are appropriately configured for perception of the sensory environment. Long-lasting enhancements and decrements to rat primary auditory cortical excitatory synaptic strength were induced by pairing acoustic stimuli with activation of the nucleus basalis neuromodulatory system. Here we report that these synaptic modifications were approximately balanced across individual receptive fields, conserving mean excitation while reducing overall response variability. Decreased response variability should increase detection and recognition of near-threshold or previously imperceptible stimuli, as we found in behaving animals. Thus, modification of cortical inputs leads to wide-scale synaptic changes, which are related to improved sensory perception and enhanced behavioral performance.

Contribution of cholinergic and GABAergic mechanisms to direction tuning, discriminability, response reliability, and neuronal rate correlations in macaque middle temporal area

Previous studies have investigated the effects of acetylcholine (ACh) on neuronal tuning, coding, and attention in primary visual cortex, but its contribution to coding in extrastriate cortex is unexplored. Here we investigate the effects of ACh on tuning properties of macaque middle temporal area MT neurons and contrast them with effects of gabazine, a GABA(A) receptor blocker. ACh increased neuronal activity, it had no effect on tuning width, but it significantly increased the direction discriminability of a neuron. Gabazine equally increased neuronal activity, but it widened tuning curves and decreased the direction discriminability of a neuron. Although gabazine significantly reduced response reliability, ACh application had little effect on response reliability. Finally, gabazine increased noise correlation of simultaneously recorded neurons, whereas ACh reduced it. Thus, both drugs increased firing rates, but only ACh application improved neuronal tuning and coding in line with effects seen in studies in which attention was selectively manipulated.

Circuit mechanisms revealed by spike-timing correlations in macaque area MT

We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative flanks were most prevalent and deepest when the neurons were driven to high rates by visual stimuli that moved in the neurons' preferred directions. The temporal development of the negative flanks in the CCG coincided with a parallel, modest reduction of the noise correlation between the spike counts of the neurons. Computational analysis of a model cortical circuit suggested that negative flanks in the CCG arise from the excitation-triggered mutual cross-inhibition between pairs of excitatory neurons. Intracortical recurrent inhibition and afterhyperpolarization caused by intrinsic outward currents, such as the calcium-activated potassium current of small conductance, can both contribute to the negative flanks in the ACG. In the model circuit, stronger intracortical inhibition helped to maintain the temporal precision between the spike trains of pairs of neurons and led to weaker noise correlations. Our results suggest a neural circuit architecture that can leverage activity-dependent intracortical inhibition to adaptively modulate both the synchrony of spike timing and the correlations in response variability.

Suppression of cortical neural variability is stimulus- and state-dependent

Internally generated, spontaneous activity is ubiquitous in the cortex, yet it does not appear to have a significant negative impact on sensory processing. Various studies have found that stimulus onset reduces the variability of cortical responses, but the characteristics of this suppression remained unexplored. By recording multiunit activity from awake and anesthetized rats, we investigated whether and how this noise suppression depends on properties of the stimulus and on the state of the cortex. In agreement with theoretical predictions, we found that the degree of noise suppression in awake rats has a nonmonotonic dependence on the temporal frequency of a flickering visual stimulus with an optimal frequency for noise suppression ~2 Hz. This effect cannot be explained by features of the power spectrum of the spontaneous neural activity. The nonmonotonic frequency dependence of the suppression of variability gradually disappears under increasing levels of anesthesia and shifts to a monotonic pattern of increasing suppression with decreasing frequency. Signal-to-noise ratios show a similar, although inverted, dependence on cortical state and frequency. These results suggest the existence of an active noise suppression mechanism in the awake cortical system that is tuned to support signal propagation and coding.

Increasing the accuracy of electromagnetic inverses using functional area source correlation constraints

Estimating cortical current distributions from electroencephalographic (EEG) or magnetoencephalographic data is a difficult inverse problem whose solution can be improved by the addition of priors on the associated neural responses. In the context of visual activation studies, we propose a new approach that uses a functional area constrained estimator (FACE) to increase the accuracy of the reconstructions. It derives the source correlation matrix from a segmentation of the cortex into areas defined by retinotopic maps of the visual field or by functional localizers obtained independently by fMRI. These areas are computed once for each individual subject and the associated estimators can therefore be reused for any new study on the same participant. The resulting FACE reconstructions emphasize the activity of sources within these areas or enforce their intercorrelations. We used realistic Monte-Carlo simulations to demonstrate that this approach improved our estimates of a diverse set of source configurations. Reconstructions obtained from a real EEG dataset demonstrate that our priors improve the localization of the cortical areas involved in horizontal disparity processing.

Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations

Correlated neuronal activity is an important feature in many neural codes, a neural correlate of a variety of cognitive states, as well as a signature of several disease states in the nervous system. The cellular and circuit mechanics of neural correlations is a vibrant area of research. Synapses throughout the cortex exhibit a form of short-term depression where increased presynaptic firing rates deplete neurotransmitter vesicles, which transiently reduces synaptic efficacy. The release and recovery of these vesicles are inherently stochastic, and this stochasticity introduces variability into the conductance elicited by depressing synapses. The impact of spiking and subthreshold membrane dynamics on the transfer of neuronal correlations has been studied intensively, but an investigation of the impact of short-term synaptic depression and stochastic vesicle dynamics on correlation transfer is lacking. We find that short-term synaptic depression and stochastic vesicle dynamics can substantially reduce correlations, shape the timescale over which these correlations occur, and alter the dependence of spiking correlations on firing rate. Our results show that short-term depression and stochastic vesicle dynamics need to be taken into account when modeling correlations in neuronal populations.

Neuromodulation of brain states

Switches between different behavioral states of the animal are associated with prominent changes in global brain activity, between sleep and wakefulness or from inattentive to vigilant states. What mechanisms control brain states, and what are the functions of the different states? Here we summarize current understanding of the key neural circuits involved in regulating brain states, with a particular emphasis on the subcortical neuromodulatory systems. At the functional level, arousal and attention can greatly enhance sensory processing, whereas sleep and quiet wakefulness may facilitate learning and memory. Several new techniques developed over the past decade promise great advances in our understanding of the neural control and function of different brain states.

Predicting errors from reconfiguration patterns in human brain networks

Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance. Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network's communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core-periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior.

Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity

VIDEO ABSTRACT

How information encoded in neuronal spike trains is used to guide sensory decisions is a fundamental question. In olfaction, a single sniff is sufficient for fine odor discrimination but the neural representations on which olfactory decisions are based are unclear. Here, we recorded neural ensemble activity in the anterior piriform cortex (aPC) of rats performing an odor mixture categorization task. We show that odors evoke transient bursts locked to sniff onset and that odor identity can be better decoded using burst spike counts than by spike latencies or temporal patterns. Surprisingly, aPC ensembles also exhibited near-zero noise correlations during odor stimulation. Consequently, fewer than 100 aPC neurons provided sufficient information to account for behavioral speed and accuracy, suggesting that behavioral performance limits arise downstream of aPC. These findings demonstrate profound transformations in the dynamics of odor representations from the olfactory bulb to cortex and reveal likely substrates for odor-guided decisions.

The spatial structure of stimuli shapes the timescale of correlations in population spiking activity

Throughout the central nervous system, the timescale over which pairs of neural spike trains are correlated is shaped by stimulus structure and behavioral context. Such shaping is thought to underlie important changes in the neural code, but the neural circuitry responsible is largely unknown. In this study, we investigate a stimulus-induced shaping of pairwise spike train correlations in the electrosensory system of weakly electric fish. Simultaneous single unit recordings of principal electrosensory cells show that an increase in the spatial extent of stimuli increases correlations at short () timescales while simultaneously reducing correlations at long () timescales. A spiking network model of the first two stages of electrosensory processing replicates this correlation shaping, under the assumptions that spatially broad stimuli both saturate feedforward afferent input and recruit an open-loop inhibitory feedback pathway. Our model predictions are experimentally verified using both the natural heterogeneity of the electrosensory system and pharmacological blockade of descending feedback projections. For weak stimuli, linear response analysis of the spiking network shows that the reduction of long timescale correlation for spatially broad stimuli is similar to correlation cancellation mechanisms previously suggested to be operative in mammalian cortex. The mechanism for correlation shaping supports population-level filtering of irrelevant distractor stimuli, thereby enhancing the population response to relevant prey and conspecific communication inputs.

The size of a stimulus that is sensed by the nervous system can control the activity of neurons in sensory areas. How neural wiring supports this dependence remains an open question. We explore this general phenomenon using weakly electric fish, which possess a sensory system that detects electric field modulations produced by the surrounding environment. In particular, these animals' nervous systems are tuned to detect the difference between spatially compact prey inputs and spatially broad communication calls from other fish. In experiment, we discover that these two classes of stimuli differentially control the synchrony between pairs of electrosensory neurons. Using a computational model, we predict that this modulation is related to feedforward and feedback neural pathways in the electrosensory system, and we verify this prediction with experiments. This architecture prevents low frequency distractor stimuli, such as the animal's own tail motion, from driving neural population responses. With our model, we demonstrate how a common neural architecture enables a population-level code for behaviorally relevant stimuli.

Attention deficits without cortical neuronal deficits

The ability to selectively process relevant stimuli is a fundamental function of the primate visual system. The best understood correlate of this function is the enhanced response of neurons in visual cortex to attended stimuli^1,2. However, recent results show that the superior colliculus (SC), a midbrain structure, also plays a crucial role in visual attention^3–5. It has been assumed that the SC acts through the same well-known mechanisms in visual cortex^3,5. Here we tested this hypothesis by transiently inactivating the SC during a motion-change detection task and measuring responses in two visual cortical areas. We found that despite large deficits in visual attention, the enhanced responses of neurons in visual cortex to attended stimuli were unchanged. These results show that the SC contributes to visual attention through mechanisms that are independent of the classic effects in visual cortex, demonstrating that other processes must play a major role in visual attention.

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.

Perceptual consequences of feature-based attentional enhancement and suppression

Feature-based attention has been shown to enhance the responses of neurons tuned to an attended feature while simultaneously suppressing responses of neurons tuned to unattended features. However, the influence of these suppressive neuronal-level modulations on perception is not well understood. Here, we investigated the perceptual consequences of feature-based attention by having subjects judge which of four random dot patterns (RDPs) contained a motion signal (Experiment 1) or which of four RDPs contained the most salient nonrandom motion signal (Experiment 2). Subjects viewed pre-cues which validly, invalidly, or neutrally cued the direction of the target RDP. Behavioral data were fit using the linear ballistic accumulator (LBA) model; the model design that best described the data revealed that the rate of sensory evidence accumulation (drift rate) was highest on valid trials and systematically decreased until the cued direction and the target direction were orthogonal. These results demonstrate behavioral correlates of both feature-based attentional enhancement and suppression.

Reduced variability of ongoing and evoked cortical activity leads to improved behavioral performance

Sensory responses of the brain are known to be highly variable, but the origin and functional relevance of this variability have long remained enigmatic. Using the variable foreperiod of a visual discrimination task to assess variability in the primate cerebral cortex, we report that visual evoked response variability is not only tied to variability in ongoing cortical activity, but also predicts mean response time. We used cortical local field potentials, simultaneously recorded from widespread cortical areas, to gauge both ongoing and visually evoked activity. Trial-to-trial variability of sensory evoked responses was strongly modulated by foreperiod duration and correlated both with the cortical variability before stimulus onset as well as with response times. In a separate set of experiments we probed the relation between small saccadic eye movements, foreperiod duration and manual response times. The rate of eye movements was modulated by foreperiod duration and eye position variability was positively correlated with response times. Our results indicate that when the time of a sensory stimulus is predictable, reduction in cortical variability before the stimulus can improve normal behavioral function that depends on the stimulus.

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.

Beyond establishing involvement: quantifying the contribution of anticipatory α- and β-band suppression to perceptual improvement with attention

Systems and cognitive neuroscience aim at understanding the neurophysiological mechanisms that underlie cognition and behavior. Many studies have revealed the involvement of many types of neural signals in diverse cognitive and behavioral phenomena. Here, we go beyond establishing such involvement and address two fundamental, yet largely unaddressed, questions: 1) exactly how much does a given neural signal contribute to a cognitive or behavioral phenomenon of interest; and 2) to what extent are distinct neural signals independently related to this phenomenon? We recorded brain activity using magnetoencephalography while human participants performed a cued somatosensory detection task. Using a novel method, we then quantified the contribution (in a predictive but not causal sense) of two well-established neural phenomena to the improvement in perception with attentional orienting. In our sample, the anticipatory suppression of extracranially recorded oscillatory α- and β-band amplitudes from contralateral primary somatosensory cortex could account for maximally 29% of the attention-induced improvement in tactile perception. In addition, although amplitude suppressions in the α- and β-frequency bands both contributed to this improvement, their contribution was largely shared. These data reveal the upper limit of the cognitive/behavioral relevance of this type of signal and show that at least 71% of the perceptual improvement with attention must be accounted for by other signals.

Exploring the relationship between perceptual learning and top-down attentional control

Here, we review the role of top-down attention in both the acquisition and the expression of perceptual learning, as well as the role of learning in more efficiently guiding attentional modulations. Although attention often mediates learning at the outset of training, many of the characteristic behavioral and neural changes associated with learning can be observed even when stimuli are task irrelevant and ignored. However, depending on task demands, attention can override the effects of perceptual learning, suggesting that even if top-down factors are not strictly necessary to observe learning, they play a critical role in determining how learning-related changes in behavior and neural activity are ultimately expressed. In turn, training may also act to optimize the effectiveness of top-down attentional control by improving the efficiency of sensory gain modulations, regulating intrinsic noise, and altering the read-out of sensory information.

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.

Stimulus-induced reduction of noise correlation in rat prefrontal cortex

We have shown previously that stimulus-induced modulation of noise correlation in rat somatosensory cortex conveys additional information about the delivery of tactile stimulation. Here we investigated whether noise correlation is also modulated by an external sensory stimulus in rat prefrontal cortex and, if so, whether such modulation conveys additional information on stimulus delivery. Noise correlation was significantly reduced after the onset of a conditional stimulus (auditory tone) that signaled an electric foot shock in the prefrontal cortex. However, noise correlation contributed little to the transmission of information on stimulus delivery. These results indicate that a meaningful sensory stimulus reduces noise correlation in rat prefrontal cortex, but such modulation does not play a significant role in conveying information on stimulus delivery.

Attentional modulations related to spatial gating but not to allocation of limited resources in primate V1

Attention can modulate neural responses in sensory cortical areas and improve behavioral performance in perceptual tasks. However, the nature and purpose of these modulations remain under debate. Here we used voltage-sensitive dye imaging (VSDI) to measure V1 population responses while monkeys performed a difficult detection task under focal or distributed attention. We found that V1 responses at attended locations are significantly elevated relative to actively ignored or irrelevant locations, consistent with the hypothesis that an important goal of attention in V1 is to highlight task-relevant information. Surprisingly, these modulations were indistinguishable under focal and distributed attention, suggesting a minor or no role for attention as a mechanism for allocating limited representational resources in V1. The response elevation at attended locations is additive, is widespread, and starts shortly before stimulus onset. This elevation could contribute to spatial gating by biasing competition in subsequent processing stages in favor of attended stimuli.

Prestimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception

Attention is thought to improve most aspects of perception. However, we recently showed that, somewhat surprisingly, endogenous attention can also lead to low subjective perceptual ratings (Rahnev et al., 2011). Here we investigated the neural basis of this effect and tested whether spontaneous fluctuations of the attentional state can lead to low confidence in one's perceptual decision. We measured prestimulus functional magnetic resonance imaging activity in the dorsal attention network and used that activity as an index of the level of attention involved in a motion direction discrimination task. Extending our previous findings, we showed that low prestimulus activity in the dorsal attention network, which presumably reflected low level of attention, was associated with higher confidence ratings. These results were explained by a signal detection theoretic model in which lack of attention increases the trial-by-trial variability of the internal perceptual response. In line with the model, we also found that low prestimulus activity in the dorsal attention network was associated with higher trial-by-trial variability of poststimulus peak activity in the motion-sensitive region MT+. These findings support the notion that lack of attention may lead to liberal subjective perceptual biases, a phenomenon we call "inattentional inflation of subjective perception."

Spatial attention enhances the selective integration of activity from area MT

Distinguishing which of the many proposed neural mechanisms of spatial attention actually underlies behavioral improvements in visually guided tasks has been difficult. One attractive hypothesis is that attention allows downstream neural circuits to selectively integrate responses from the most informative sensory neurons. This would allow behavioral performance to be based on the highest-quality signals available in visual cortex. We examined this hypothesis by asking how spatial attention affects both the stimulus sensitivity of middle temporal (MT) neurons and their corresponding correlation with behavior. Analyzing a data set pooled from two experiments involving four monkeys, we found that spatial attention did not appreciably affect either the stimulus sensitivity of the neurons or the correlation between their activity and behavior. However, for those sessions in which there was a robust behavioral effect of attention, focusing attention inside the neuron's receptive field significantly increased the correlation between these two metrics, an indication of selective integration. These results suggest that, similar to mechanisms proposed for the neural basis of perceptual learning, the behavioral benefits of focusing spatial attention are attributable to selective integration of neural activity from visual cortical areas by their downstream targets.

Decorrelation of sensory-evoked neuronal responses in rat barrel cortex during postnatal development

The ability to detect and discriminate sensory stimuli greatly improves with age. To better understand the neural basis of perceptual development, we studied the postnatal development of sensory responses in cortical neurons. Specifically, we analyzed neuronal responses to single-whisker deflections in the posteromedial barrel subfield (PMBSF) of the rat primary somatosensory cortex. Responses of PMBSF neurons showed a long onset latency and duration in the first postnatal week, but became fast and transient over the next few weeks. Trial-by-trial variations of single neuron responses did not change systematically with age, whereas the covariation of responses across trials between neurons (noise correlation) was high on postnatal day 5-6 (P5-6), and gradually decreased with age to near zero by P30-31. Computational analyses showed that pooled responses of multiple neurons became more reliable across stimulus trials with age. The period over which these changes occurred corresponds to the period when rats develop a full set of exploratory whisking behavior. We suggest that reduced noise correlation across a population of neurons, in addition to sharpening the temporal characteristics of single neuron responses, may help improve behavioral performance.

Complementary role of frontoparietal activity and cortical pattern similarity in successful episodic memory encoding

One central goal in cognitive neuroscience of learning and memory is to characterize the neural processes that lead to long-lasting episodic memory. In addition to the stronger frontoparietal activity, greater category- or item-specific cortical representation during encoding, as measured by pattern similarity (PS), is also associated with better subsequent episodic memory. Nevertheless, it is unknown whether frontoparietal activity and cortical PS reflect distinct mechanisms. To address this issue, we reanalyzed previous data (Xue G, Dong Q, Chen C, Lu ZL, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science. 330:97, Experiment 3) using a novel approach based on combined activation-based and information-based analyses. The results showed that across items, stronger frontoparietal activity was associated with greater PS in distributed brain regions, including those where the PS was predictive of better subsequent memory. Nevertheless, the item-specific PS was still associated with later episodic memory after controlling the effect of frontoparietal activity. Our results suggest that one possible mechanism of frontoparietal activity on episodic memory encoding is via enhancing PS, resulting in more unique and consistent input to the medial temporal lobe. In addition, they suggest that PS might index additional processes, such as pattern reinstatement as a result of study-phase retrieval, that contribute to episodic memory encoding.

Toward a unified theory of visual area V4

Visual area V4 is a midtier cortical area in the ventral visual pathway. It is crucial for visual object recognition and has been a focus of many studies on visual attention. However, there is no unifying view of V4's role in visual processing. Neither is there an understanding of how its role in feature processing interfaces with its role in visual attention. This review captures our current knowledge of V4, largely derived from electrophysiological and imaging studies in the macaque monkey. Based on recent discovery of functionally specific domains in V4, we propose that the unifying function of V4 circuitry is to enable selective extraction of specific functional domain-based networks, whether it be by bottom-up specification of object features or by top-down attentionally driven selection.

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.

Neural network mechanisms underlying stimulus driven variability reduction

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits.

To understand how neurons encode information, neuroscientists record their firing activity while the animal executes a given task for many trials. Surprisingly, it has been found that the neural response is highly variable, which a priori limits the encoding of information by these neurons. However, recent experiments have shown that this variability is reduced when the animal receives a stimulus or attends to a particular one, suggesting an enhancement of information encoding. It is known that a cause of neural variability resides in the fact that individual neurons receive an input which fluctuates around their firing threshold. We demonstrate here that all the experimental results can naturally arise from the dynamics of a neural network. Using a realistic model, we show that the neural variability during spontaneous activity is particularly high because input noise induces large fluctuations between multiple –but unstable- network states. With stimulation or attention, one particular network state is stabilized and fluctuations decrease, leading to a neural variability reduction. In conclusion, our results suggest that the observed variability reduction is a property of the neural circuits of the brain.

Dissociation of response variability from firing rate effects in frontal eye field neurons during visual stimulation, working memory, and attention

Recent studies suggest that trial-to-trial variability of neuronal spiking responses may provide important information about behavioral state. Observed changes in variability during sensory stimulation, attention, motor preparation, and visual discrimination suggest that variability may reflect the engagement of neurons in a behavioral task. We examined changes in spiking variability of frontal eye field (FEF) neurons in a change detection task requiring monkeys to remember a visually cued location and direct attention to that location while ignoring distracters elsewhere. In this task, the firing rates (FRs) of FEF neurons not only continuously reflect the location of the remembered cue and select targets, but also predict detection performance on a trial-by-trial basis. Changes in FEF response variability, as measured by the Fano factor (FF), showed clear dissociations from changes in FR. The FF declined in response to visual stimulation at all tested locations, even in the opposite hemifield, indicating much broader spatial tuning of the FF compared with the FR. Furthermore, despite robust spatial modulation of the FR throughout all epochs of the task, spatial tuning of the FF did not persist throughout the delay period, nor did it show attentional modulation. These results indicate that changes in variability, at least in the FEF, are most effectively driven by visual stimulation, while behavioral engagement is not sufficient. Instead, changes in variability may reflect shifts in the balance between feedforward and recurrent sources of excitatory drive.

Cellular and circuit mechanisms maintain low spike co-variability and enhance population coding in somatosensory cortex

The responses of cortical neurons are highly variable across repeated presentations of a stimulus. Understanding this variability is critical for theories of both sensory and motor processing, since response variance affects the accuracy of neural codes. Despite this influence, the cellular and circuit mechanisms that shape the trial-to-trial variability of population responses remain poorly understood. We used a combination of experimental and computational techniques to uncover the mechanisms underlying response variability of populations of pyramidal (E) cells in layer 2/3 of rat whisker barrel cortex. Spike trains recorded from pairs of E-cells during either spontaneous activity or whisker deflected responses show similarly low levels of spiking co-variability, despite large differences in network activation between the two states. We developed network models that show how spike threshold non-linearities dilute E-cell spiking co-variability during spontaneous activity and low velocity whisker deflections. In contrast, during high velocity whisker deflections, cancelation mechanisms mediated by feedforward inhibition maintain low E-cell pairwise co-variability. Thus, the combination of these two mechanisms ensure low E-cell population variability over a wide range of whisker deflection velocities. Finally, we show how this active decorrelation of population variability leads to a drastic increase in the population information about whisker velocity. The prevalence of spiking non-linearities and feedforward inhibition in the nervous system suggests that the mechanisms for low network variability presented in our study may generalize throughout the brain.

Neural correlation is stimulus modulated by feedforward inhibitory circuitry

Correlated variability of neural spiking activity has important consequences for signal processing. How incoming sensory signals shape correlations of population responses remains unclear. Cross-correlations between spiking of different neurons may be particularly consequential in sparsely firing neural populations such as those found in layer 2/3 of sensory cortex. In rat whisker barrel cortex, we found that pairs of excitatory layer 2/3 neurons exhibit similarly low levels of spike count correlation during both spontaneous and sensory-evoked states. The spontaneous activity of excitatory-inhibitory neuron pairs is positively correlated, while sensory stimuli actively decorrelate joint responses. Computational modeling shows how threshold nonlinearities and local inhibition form the basis of a general decorrelating mechanism. We show that inhibitory population activity maintains low correlations in excitatory populations, especially during periods of sensory-evoked coactivation. The role of feedforward inhibition has been previously described in the context of trial-averaged phenomena. Our findings reveal a novel role for inhibition to shape correlations of neural variability and thereby prevent excessive correlations in the face of feedforward sensory-evoked activation.

Computational advances towards linking BOLD and behavior

Traditionally, fMRI studies have focused on analyzing the mean response amplitude within a cortical area. However, the mean response is blind to many important patterns of cortical modulation, which severely limits the formulation and evaluation of linking hypotheses between neural activity, BOLD responses, and behavior. More recently, multivariate pattern classification analysis (MVPA) has been applied to fMRI data to evaluate the information content of spatially distributed activation patterns. This approach has been remarkably successful at detecting the presence of specific information in targeted brain regions, and provides an extremely flexible means of extracting that information without a precise generative model for the underlying neural activity. However, this flexibility comes at a cost: since MVPA relies on pooling information across voxels that are selective for many different stimulus attributes, it is difficult to infer how specific sub-sets of tuned neurons are modulated by an experimental manipulation. In contrast, recently developed encoding models can produce more precise estimates of feature-selective tuning functions, and can support the creation of explicit linking hypotheses between neural activity and behavior. Although these encoding models depend on strong - and often untested - assumptions about the response properties of underlying neural generators, they also provide a unique opportunity to evaluate population-level computational theories of perception and cognition that have previously been difficult to assess using either single-unit recording or conventional neuroimaging techniques.

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

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

Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study

Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity--as measured by the Fisher information--is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint.

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

According to signal detection theoretical analyses, visual signals occurring at a cued location are detected more accurately, whereas frequently occurring ones are reported more often but are not better distinguished from noise. However, conventional analyses that estimate sensitivity and bias by comparing true- and false-positive rates offer limited insights into the mechanisms responsible for these effects. Here, we reassessed the prior influences of signal probability and relevance on visual contrast detection using a reverse-correlation technique that quantifies how signal-like fluctuations in noise predict trial-to-trial variability in choice discarded by conventional analyses. This approach allowed us to estimate separately the sensitivity of true and false positives to parametric changes in signal energy. We found that signal probability and relevance both increased energy sensitivity, but in dissociable ways. Cues predicting the relevant location increased primarily the sensitivity of true positives by suppressing internal noise during signal processing, whereas cues predicting greater signal probability increased both the frequency and the sensitivity of false positives by biasing the baseline activity of signal-selective units. We interpret these findings in light of "predictive-coding" models of perception, which propose separable top-down influences of expectation (probability driven) and attention (relevance driven) on bottom-up sensory processing.

Disentangling the functional consequences of the connectivity between optic-flow processing neurons

Typically, neurons in sensory areas are highly interconnected. Coupling two neurons can synchronize their activity and affect a variety of single-cell properties, such as their stimulus tuning, firing rate or gain. All of these factors must be considered to understand how two neurons should be coupled to optimally process stimuli. We quantified the functional effect of an interaction between two optic-flow processing neurons (Vi and H1) in the fly (Lucilia sericata). Using a generative model, we estimated a uni-directional coupling from H1 to Vi. Especially at a low signal-to-noise ratio (SNR), the coupling strongly improved the information about optic-flow in Vi. We identified two constraints confining the strength of the interaction. First, for weak couplings, Vi benefited from inputs by H1 without a concomitant shift of its stimulus tuning. Second, at both low and high SNR, the coupling strength lay in a range in which the information carried by single spikes is optimal.

Mechanisms of selective attention: response enhancement, noise reduction, and efficient pooling of sensory responses

In this issue of Neuron, Pestilli and coworkers provide evidence that response gain and noise reduction are insufficient to account for attention-induced changes in perception. Instead, selection may critically depend on the biased pooling of sensory signals during decision making.