Adjacent visual cortical complex cells share about 20% of their stimulus-related information

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality

Scaling relationships characterize complex systems at criticality. In the brain, these relationships are evident in scale-invariant activity cascades, so-called neuronal avalanches, quantified by power laws in avalanche size and duration. At the cellular level, neuronal avalanches are identified in spatially distributed groups of neurons that participate in cascades of coincident action potential firing. Such spatiotemporal synchronization is central to theories on brain function, yet scaling relationships in avalanche synchronization have been challenging to study when only a fraction of neurons is observed, underestimating avalanche properties. Here, we study these biases from fractional sampling in an all-to-all, balanced network of excitatory and inhibitory neurons with critical branching process dynamics. We focus on the growth of mean avalanche size with avalanche duration. For parabolic avalanches, this growth is quadratic, quantified by the scaling exponent, χ = 2 , which signifies rapid spatial expansion of coincident firing within a relatively short period of time. In contrast, χ < < 2 for fractionally sampled networks. We show that temporal coarse-graining combined with a threshold for the minimally required coincident firing in the network recovers χ = 2 , even when sampling as few as 0.1% of the neurons. In contrast, a commonly proposed 'crackling noise' approach fails to recover χ under those conditions. Our approach robustly identifies χ = 2 for ongoing neuronal activity in frontal cortex of awake mice using cellular 2-photon imaging. Our findings demonstrate how to correct scaling bias from fractional sampling and identifies rapid, scale-invariant synchronization of cell assemblies in the brain.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Accounting for Biases in the Estimation of Neuronal Signal Correlation

Signal correlation (rs) is commonly defined as the correlation between the tuning curves of two neurons and is widely used as a metric of tuning similarity. It is fundamental to how populations of neurons represent stimuli and has been central to many studies of neural coding. Yet the classic estimate, Pearson's correlation coefficient, [Formula: see text], between the average responses of two neurons to a set of stimuli suffers from confounding biases. The estimate [Formula: see text] can be downwardly biased by trial-to-trial variability and also upwardly biased by trial-to-trial correlation between neurons, and these biases can hide important aspects of neural coding. Here we provide analytic results on the source of these biases and explore them for ranges of parameters that are relevant for electrophysiological experiments. We then provide corrections for these biases that we validate in simulation. Furthermore, we apply these corrected estimators to make the following novel experimental observation in cortical area MT: pairs of nearby neurons that are strongly tuned for motion direction tend to have high signal correlation, and pairs that are weakly tuned tend to have low signal correlation. We dismiss a trivial explanation for this and find that an analogous trend holds for orientation tuning in the primary visual cortex. We also consider the potential consequences for encoding whereby the association of signal correlation and tuning strength naturally regularizes the dimensionality of downstream computations.SIGNIFICANCE STATEMENT Fundamental to how cortical neurons encode information about the environment is their functional similarity, that is, the redundancy in what they encode and their shared noise. These properties have been extensively studied theoretically and experimentally throughout the nervous system, but here we show that a common estimator of functional similarity has confounding biases. We characterize these biases and provide estimators that do not suffer from them. Using our improved estimators, we demonstrate a novel result, that is, there is a positive relationship between tuning curve similarity and amplitude for nearby neurons in the visual cortical motion area MT. We provide a simple stochastic model explaining this relationship and discuss how it would naturally regularize the dimensionality of neural encoding.

Network dynamics of Broca's area during word selection

Current models of word-production in Broca’s area (i.e. left ventro-lateral prefrontal cortex, VLPFC) posit that sequential and staggered semantic, lexical, phonological and articulatory processes precede articulation. Using millisecond-resolution intra-cranial recordings, we evaluated spatiotemporal dynamics and high frequency functional interconnectivity between left VLPFC regions during single-word production. Through the systematic variation of retrieval, selection, and phonological loads, we identified specific activation profiles and functional coupling patterns between these regions that fit within current psycholinguistic theories of word production. However, network interactions underpinning these processes activate in parallel (not sequentially), while the processes themselves are indexed by specific changes in network state. We found evidence that suggests that pars orbitalis is coupled with pars triangularis during lexical retrieval, while lexical selection is terminated via coupled activity with M1 at articulation onset. Taken together, this work reveals that speech production relies on very specific inter-regional couplings in rapid sequence in the language dominant hemisphere.

Perirhinal circuits for memory processing

The perirhinal cortex (PRC) serves as the gateway to the hippocampus for episodic memory formation and plays a part in retrieval through its backward connectivity to various neocortical areas. First, I present the evidence suggesting that PRC neurons encode both experientially acquired object features and their associative relations. Recent studies have revealed circuit mechanisms in the PRC for the retrieval of cue-associated information, and have demonstrated that, in monkeys, PRC neuron-encoded information can be behaviourally read out. These studies, among others, support the theory that the PRC converts visual representations of an object into those of its associated features and initiates backward-propagating, interareal signalling for retrieval of nested associations of object features that, combined, extensionally represent the object meaning. I propose that the PRC works as the ventromedial hub of a 'two-hub model' at an apex of the hierarchy of a distributed memory network and integrates signals encoded in other downstream cortical areas that support diverse aspects of knowledge about an object.

Quantifying encoding redundancy induced by rate correlations in Poisson neurons

Temporal correlations in neuronal spike trains are known to introduce redundancy to stimulus encoding. However, exact methods to describe how these correlations impact neural information transmission quantitatively are lacking. Here, we provide a general measure for the information carried by correlated rate modulations only, neglecting other spike correlations, and use it to investigate the effect of rate correlations on encoding redundancy. We derive it analytically by calculating the mutual information between a time-correlated, rate modulating signal and the resulting spikes of Poisson neurons. Whereas this information is determined by spike autocorrelations only, the redundancy in information encoding due to rate correlations depends on both the distribution and the autocorrelation of the rate histogram. We further demonstrate that at very small signal strengths the information carried by rate correlated spikes becomes identical to that of independent spikes, in effect measuring the signal modulation depth. In contrast, a vanishing signal correlation time maximizes information but does not generally yield the information of independent spikes. Overall, our study sheds light on the role of signal-induced temporal correlations for neural coding, by providing insight into how signal features shape redundancy and by establishing mathematical links between existing methods.

Probing Sensory Readout via Combined Choice-Correlation Measures and Microstimulation Perturbation

It is controversial whether covariation between neuronal activity and perceptual choice (i.e., choice correlation) reflects the functional readout of sensory signals. Here, we combined choice-correlation measures and electrical microstimulation on a site-to-site basis in the medial superior temporal area (MST), middle temporal area (MT), and ventral intraparietal area (VIP) when macaques discriminated between motion directions in both fine and coarse tasks. Microstimulation generated comparable effects between tasks but heterogeneous effects across and within brain regions. Within the MST and MT, microstimulation significantly biased an animal's choice toward the sensory preference instead of choice-related signals of the stimulated units. This was particularly evident for sites with conflict preference of sensory and choice-related signals. In the VIP, microstimulation failed to produce significant effects in either task despite strong choice correlations presented in this area. Our results suggest that sensory readout may not be inferred from choice-related signals during perceptual decision-making tasks.

Translaminar circuits formed by the pyramidal cells in the superficial layers of cat visual cortex

Pyramidal cells in the superficial layers of the neocortex provide a major excitatory projection to layer 5, which contains the pyramidal cells that project to subcortical motor-related targets. Both structurally and functionally rather little is known about this interlaminar pathway, especially in higher mammals. Here, we made sparse ultrastructural reconstructions of the projection to layer 5 of three pyramidal neurons from layer 3 in cat V1 whose morphology, physiology, and synaptic connections with layers 2 and 3 were known. The dominant targets of the 74 identified synapses in layer 5 were the dendritic spines of pyramidal cells. The fractions of target spiny dendrites were 59, 61, and 84% for the three cells, with the remaining targets being dendrites of smooth neurons. These fractions were similar to the distribution of targets of unlabeled asymmetric synapses in the surrounding neuropil. Serial section reconstructions revealed that the target dendrites were heterogenous in morphology, indicating that different cell types are innervated. This new evidence indicates that the descending projection from the superficial layer pyramidal cells does not simply drive the output pyramidal cells that project to cortical and subcortical targets, but participates in the complex circuitry of the deep cortical layers.

The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections (“Mexican hat” connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range (“inverse Mexican hat” connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.

Effect of interacting second- and third-order stimulus-dependent correlations on population-coding asymmetries

Spike correlations among neurons are widely encountered in the brain. Although models accounting for pairwise interactions have proved able to capture some of the most important features of population activity at the level of the retina, the evidence shows that pairwise neuronal correlation analysis does not resolve cooperative population dynamics by itself. By means of a series expansion for short time scales of the mutual information conveyed by a population of neurons, the information transmission can be broken down into firing rate and correlational components. In a proposed extension of this framework, we investigate the information components considering both second- and higher-order correlations. We show that the existence of a mixed stimulus-dependent correlation term defines a new scenario for the interplay between pairwise and higher-than-pairwise interactions in noise and signal correlations that would lead either to redundancy or synergy in the information-theoretic sense.

High noise correlation between the functionally connected neurons in emergent V1 microcircuits

Neural correlations (noise correlations and cross-correlograms) are widely studied to infer functional connectivity between neurons. High noise correlations between neurons have been reported to increase the encoding accuracy of a neuronal population; however, low noise correlations have also been documented to play a critical role in cortical microcircuits. Therefore, the role of noise correlations in neural encoding is highly debated. To this aim, through multi-electrodes, we recorded neuronal ensembles in the primary visual cortex of anaesthetized cats. By computing cross-correlograms, we divulged the functional network (microcircuit) between neurons within an ensemble in relation to a specific orientation. We show that functionally connected neurons systematically exhibit higher noise correlations than functionally unconnected neurons in a microcircuit that is activated in response to a particular orientation. Furthermore, the mean strength of noise correlations for the connected neurons increases steeply than the unconnected neurons as a function of the resolution window used to calculate noise correlations. We suggest that neurons that display high noise correlations in emergent microcircuits feature functional connections which are inevitable for information encoding in the primary visual cortex.

Five key factors determining pairwise correlations in visual cortex

The responses of cortical neurons to repeated presentation of a stimulus are highly variable, yet correlated. These "noise correlations" reflect a low-dimensional structure of population dynamics. Here, we examine noise correlations in 22,705 pairs of neurons in primary visual cortex (V1) of anesthetized cats, during ongoing activity and in response to artificial and natural visual stimuli. We measured how noise correlations depend on 11 factors. Because these factors are themselves not independent, we distinguished their influences using a nonlinear additive model. The model revealed that five key factors play a predominant role in determining pairwise correlations. Two of these are distance in cortex and difference in sensory tuning: these are known to decrease correlation. A third factor is firing rate: confirming most earlier observations, it markedly increased pairwise correlations. A fourth factor is spike width: cells with a broad spike were more strongly correlated amongst each other. A fifth factor is spike isolation: neurons with worse isolation were more correlated, even if they were recorded on different electrodes. For pairs of neurons with poor isolation, this last factor was the main determinant of correlations. These results were generally independent of stimulus type and timescale of analysis, but there were exceptions. For instance, pairwise correlations depended on difference in orientation tuning more during responses to gratings than to natural stimuli. These results consolidate disjoint observations in a vast literature on pairwise correlations and point towards regularities of population coding in sensory cortex.

Neuronal variability in orbitofrontal cortex during economic decisions

Neuroeconomic models assume that economic decisions are based on the activity of offer value cells in the orbitofrontal cortex (OFC), but testing this assertion has proven difficult. In principle, the decision made on a given trial should correlate with the stochastic fluctuations of these cells. However, this correlation, measured as a choice probability (CP), is small. Importantly, a neuron's CP reflects not only its individual contribution to the decision (termed readout weight), but also the intensity and the structure of correlated variability across the neuronal population (termed noise correlation). A precise mathematical relation between CPs, noise correlations, and readout weights was recently derived by Haefner and colleagues (Haefner RM, Gerwinn S, Macke JH, Bethge M. Nat Neurosci 16: 235-242, 2013) for a linear decision model. In this framework, concurrent measurements of noise correlations and CPs can provide quantitative information on how a population of cells contributes to a decision. Here we examined neuronal variability in the OFC of rhesus monkeys during economic decisions. Noise correlations had similar structure but considerably lower strength compared with those typically measured in sensory areas during perceptual decisions. In contrast, variability in the activity of individual cells was high and comparable to that recorded in other cortical regions. Simulation analyses based on Haefner's equation showed that noise correlations measured in the OFC combined with a plausible readout of offer value cells reproduced the experimental measures of CPs. In other words, the results obtained for noise correlations and those obtained for CPs taken together support the hypothesis that economic decisions are primarily based on the activity of offer value cells.

Neurons in cat V1 show significant clustering by degree of tuning

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and, for drifting gratings, directions. Orientation tuning width, spatial frequency tuning width, and direction selectivity index (DSI) all showed significant clustering: pairs of neurons recorded at a single site were significantly more similar in each of these properties than pairs of neurons from different recording sites. The strength of the clustering was generally modest. The percent decrease in the median difference between pairs from the same site, relative to pairs from different sites, was as follows: for different measures of orientation tuning width, 29-35% (drifting gratings) or 15-25% (flashed gratings); for DSI, 24%; and for spatial frequency tuning width measured in octaves, 8% (drifting gratings). The clusterings of all of these measures were much weaker than for preferred orientation (68% decrease) but comparable to that seen for preferred spatial frequency in response to drifting gratings (26%). For the above properties, little difference in clustering was seen between simple and complex cells. In studies of spatial frequency tuning to flashed gratings, strong clustering was seen among simple-cell pairs for tuning width (70% decrease) and preferred frequency (71% decrease), whereas no clustering was seen for simple-complex or complex-complex cell pairs.

Negative Correlations in Visual Cortical Networks

The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function.

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.

Increasing diversity of neural responses to speech sounds across the central auditory pathway

Neurons at higher stations of each sensory system are responsive to feature combinations not present at lower levels. As a result, the activity of these neurons becomes less redundant than lower levels. We recorded responses to speech sounds from the inferior colliculus and the primary auditory cortex neurons of rats, and tested the hypothesis that primary auditory cortex neurons are more sensitive to combinations of multiple acoustic parameters compared to inferior colliculus neurons. We independently eliminated periodicity information, spectral information and temporal information in each consonant and vowel sound using a noise vocoder. This technique made it possible to test several key hypotheses about speech sound processing. Our results demonstrate that inferior colliculus responses are spatially arranged and primarily determined by the spectral energy and the fundamental frequency of speech, whereas primary auditory cortex neurons generate widely distributed responses to multiple acoustic parameters, and are not strongly influenced by the fundamental frequency of speech. We found no evidence that inferior colliculus or primary auditory cortex was specialized for speech features such as voice onset time or formants. The greater diversity of responses in primary auditory cortex compared to inferior colliculus may help explain how the auditory system can identify a wide range of speech sounds across a wide range of conditions without relying on any single acoustic cue.

Functional heterogeneity in neighboring neurons of cat primary visual cortex in response to both artificial and natural stimuli

Neurons in primary visual cortex of many mammals are clustered according to their preference to stimulus parameters such as orientation and spatial frequency. Nevertheless, responses to complex visual stimuli are highly heterogeneous between adjacent neurons. To investigate the relation between these observations, we recorded from pairs of neighboring neurons in area 17 of anesthetized cats in response to stimuli of differing complexity: sinusoidal drifting gratings, binary dense noise, and natural movies. Comparisons of the tuning curves revealed similar orientation and direction preferences for neighboring neurons, but large differences in preferred phase, direction selectivity, and tuning width of spatial frequency. No pair was similar across all tuning properties. The neurons' firing rates averaged across multiple stimulus repetitions (the "signal") were also compared. Binned between 10 and 200 ms, the correlation between these signals was close to zero in the median across all pairs for all stimulus classes. Signal correlations agreed poorly with differences in tuning properties, except for receptive field offset and relative modulation (i.e., the strength of phase modulation). Nonetheless, signal correlations for different stimulus classes were well correlated with each other, even for gratings and movies. Conversely, trial-to-trial fluctuations (termed "noise") were poorly correlated between neighboring neurons, suggesting low degrees of common input. In response to gratings and visual noise, signal and noise correlations were well correlated with each other, but less so for responses to movies. These findings have relevance for our understanding of the processing of natural stimuli in a functionally heterogeneous cortical network.

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.

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.

Adaptation improves neural coding efficiency despite increasing correlations in variability

Exposure of cortical cells to sustained sensory stimuli results in changes in the neuronal response function. This phenomenon, known as adaptation, is a common feature across sensory modalities. Here, we quantified the functional effect of adaptation on the ensemble activity of cortical neurons in the rat whisker-barrel system. A multishank array of electrodes was used to allow simultaneous sampling of neuronal activity. We characterized the response of neurons to sinusoidal whisker vibrations of varying amplitude in three states of adaptation. The adaptors produced a systematic rightward shift in the neuronal response function. Consistently, mutual information revealed that peak discrimination performance was not aligned to the adaptor but to test amplitudes 3-9 μm higher. Stimulus presentation reduced single neuron trial-to-trial response variability (captured by Fano factor) and correlations in the population response variability (noise correlation). We found that these two types of variability were inversely proportional to the average firing rate regardless of the adaptation state. Adaptation transferred the neuronal operating regime to lower rates with higher Fano factor and noise correlations. Noise correlations were positive and in the direction of signal, and thus detrimental to coding efficiency. Interestingly, across all population sizes, the net effect of adaptation was to increase the total information despite increasing the noise correlation between neurons.

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.

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.

Low error discrimination using a correlated population code

We explored the manner in which spatial information is encoded by retinal ganglion cell populations. We flashed a set of 36 shape stimuli onto the tiger salamander retina and used different decoding algorithms to read out information from a population of 162 ganglion cells. We compared the discrimination performance of linear decoders, which ignore correlation induced by common stimulation, with nonlinear decoders, which can accurately model these correlations. Similar to previous studies, decoders that ignored correlation suffered only a modest drop in discrimination performance for groups of up to ∼30 cells. However, for more realistic groups of 100+ cells, we found order-of-magnitude differences in the error rate. We also compared decoders that used only the presence of a single spike from each cell with more complex decoders that included information from multiple spike counts and multiple time bins. More complex decoders substantially outperformed simpler decoders, showing the importance of spike timing information. Particularly effective was the first spike latency representation, which allowed zero discrimination errors for the majority of shape stimuli. Furthermore, the performance of nonlinear decoders showed even greater enhancement compared with linear decoders for these complex representations. Finally, decoders that approximated the correlation structure in the population by matching all pairwise correlations with a maximum entropy model fit to all 162 neurons were quite successful, especially for the spike latency representation. Together, these results suggest a picture in which linear decoders allow a coarse categorization of shape stimuli, whereas nonlinear decoders, which take advantage of both correlation and spike timing, are needed to achieve high-fidelity discrimination.

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.

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.

The effect of neural adaptation on population coding accuracy

Most neurons in the primary visual cortex initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. The functional consequences of adaptation are unclear. Typically a reduction of firing rate would reduce single neuron accuracy as less spikes are available for decoding, but it has been suggested that on the population level, adaptation increases coding accuracy. This question requires careful analysis as adaptation not only changes the firing rates of neurons, but also the neural variability and correlations between neurons, which affect coding accuracy as well. We calculate the coding accuracy using a computational model that implements two forms of adaptation: spike frequency adaptation and synaptic adaptation in the form of short-term synaptic plasticity. We find that the net effect of adaptation is subtle and heterogeneous. Depending on adaptation mechanism and test stimulus, adaptation can either increase or decrease coding accuracy. We discuss the neurophysiological and psychophysical implications of the findings and relate it to published experimental data.

The Ising decoder: reading out the activity of large neural ensembles

The Ising model has recently received much attention for the statistical description of neural spike train data. In this paper, we propose and demonstrate its use for building decoders capable of predicting, on a millisecond timescale, the stimulus represented by a pattern of neural activity. After fitting to a training dataset, the Ising decoder can be applied "online" for instantaneous decoding of test data. While such models can be fit exactly using Boltzmann learning, this approach rapidly becomes computationally intractable as neural ensemble size increases. We show that several approaches, including the Thouless-Anderson-Palmer (TAP) mean field approach from statistical physics, and the recently developed Minimum Probability Flow Learning (MPFL) algorithm, can be used for rapid inference of model parameters in large-scale neural ensembles. Use of the Ising model for decoding, unlike other problems such as functional connectivity estimation, requires estimation of the partition function. As this involves summation over all possible responses, this step can be limiting. Mean field approaches avoid this problem by providing an analytical expression for the partition function. We demonstrate these decoding techniques by applying them to simulated neural ensemble responses from a mouse visual cortex model, finding an improvement in decoder performance for a model with heterogeneous as opposed to homogeneous neural tuning and response properties. Our results demonstrate the practicality of using the Ising model to read out, or decode, spatial patterns of activity comprised of many hundreds of neurons.

Correlations between the activity of sensory neurons and behavior: how much do they tell us about a neuron's causality?

How the activity of sensory neurons elicits perceptions and guides behavior is central to our understanding of the brain and is a subject of intense investigation in neuroscience. Correlations between the activity of sensory neurons and behavior have been widely observed and are sometimes used to infer how neurons are used to guide a certain behavior. This view is challenged firstly by theoretical considerations that these correlations rely on the existence of correlated noise and its structure, and secondly by recent empirical observations suggesting that such correlated noise is not a fixed network property but that it depends on various sources, and varies with a subject's mental state.

Internal curvature signal and noise in low- and high-level vision

How does internal processing contribute to visual pattern perception? By modeling visual search performance, we estimated internal signal and noise relevant to perception of curvature, a basic feature important for encoding of three-dimensional surfaces and objects. We used isolated, sparse, crowded, and face contexts to determine how internal curvature signal and noise depended on image crowding, lateral feature interactions, and level of pattern processing. Observers reported the curvature of a briefly flashed segment, which was presented alone (without lateral interaction) or among multiple straight segments (with lateral interaction). Each segment was presented with no context (engaging low-to-intermediate-level curvature processing), embedded within a face context as the mouth (engaging high-level face processing), or embedded within an inverted-scrambled-face context as a control for crowding. Using a simple, biologically plausible model of curvature perception, we estimated internal curvature signal and noise as the mean and standard deviation, respectively, of the Gaussian-distributed population activity of local curvature-tuned channels that best simulated behavioral curvature responses. Internal noise was increased by crowding but not by face context (irrespective of lateral interactions), suggesting prevention of noise accumulation in high-level pattern processing. In contrast, internal curvature signal was unaffected by crowding but modulated by lateral interactions. Lateral interactions (with straight segments) increased curvature signal when no contextual elements were added, but equivalent interactions reduced curvature signal when each segment was presented within a face. These opposing effects of lateral interactions are consistent with the phenomena of local-feature contrast in low-level processing and global-feature averaging in high-level processing.

A neuronal population measure of attention predicts behavioral performance on individual trials

Visual attention improves perception for an attended location or feature and also modulates the responses of sensory neurons. In laboratory studies, the sensory stimuli and task instructions are held constant within an attentional condition, but despite experimenters' best efforts, attention likely varies from moment to moment. Because most previous studies have focused on single neurons, it has been impossible to use neuronal responses to identify attentional fluctuations and determine whether these are associated with changes in behavior. We show that an instantaneous measure of attention based on the responses of a modest number of neurons in area V4 of the rhesus monkey (Macaca mulatta) can reliably predict large changes in an animal's ability to perform a difficult psychophysical task. Unexpectedly, this measure shows that the amount of attention allocated at any moment to locations in opposite hemifields is uncorrelated, suggesting that animals allocate attention to each stimulus independently rather than moving their attentional focus from one location to another.

Visual stimulation decorrelates neuronal activity

The accuracy of neuronal encoding depends on the response statistics of individual neurons and the correlation of the activity between different neurons. Here, the dynamics of the neuronal response statistics in the anterior superior temporal sulcus of the macaque monkey is described. A transient reduction in the normalized trial-by-trial variability and decorrelation of the responses with both the activity of other neurons and previous activity of the same neuron are found at response onset. The variability of neuronal activity and its correlation structure return to the levels observed in the resting state 50-100 ms after response onset, except for marked increases in the signal correlation between neurons. The transient changes in the response statistics are seen even if there is little or no stimulus-elicited activity, indicating the effect is due to network properties rather than to activity changes per se. Modeling also indicates that the observed variations in response variability and correlation structure of the neuronal activity over time cannot be attributed to changes in firing rate. However, a reset of the underlying spike-generating process, possibly due to the driving input changing from recurrent to feedforward inputs, captures most of the observed changes. The nonstationarity indicated by the changes in correlation structure around response onset increases coding efficiency: compared with the mutual information calculated without regard to the transitory changes, the decorrelation increases the information conveyed by the initial response of modeled neuronal pairs by ≤ 4% and suggests that an integration time of as little as 50 ms is sufficient to extract 95% the available information during the initial response period.

Forward masking estimated by signal detection theory analysis of neuronal responses in primary auditory cortex

Psychophysical forward masking is an increase in threshold of detection of a sound (probe) when it is preceded by another sound (masker). This is reminiscent of the reduction in neuronal responses to a sound following prior stimulation. Studies in the auditory nerve and cochlear nucleus using signal detection theory techniques to derive neuronal thresholds showed that in centrally projecting neurons, increases in masked thresholds were significantly smaller than the changes measured psychophysically. Larger threshold shifts have been reported in the inferior colliculus of awake marmoset. The present study investigated the magnitude of forward masking in primary auditory cortical neurons of anaesthetised guinea-pigs. Responses of cortical neurons to unmasked and forward masked tones were measured and probe detection thresholds estimated using signal detection theory methods. Threshold shifts were larger than in the auditory nerve, cochlear nucleus and inferior colliculus. The larger threshold shifts suggest that central, and probably cortical, processes contribute to forward masking. However, although methodological differences make comparisons difficult, the threshold shifts in cortical neurons were, in contrast to subcortical nuclei, actually larger than those observed psychophysically. Masking was largely attributable to a reduction in the responses to the probe, rather than either a persistence of the masker responses or an increase in the variability of probe responses.

Trial-to-trial variability of spike response of V1 and saccadic response time

Single neurons in the primary visual cortex (V1) show variability in spike activity in response to an identical visual stimulus. In the current study, we examined the behavioral significance of the variability in spike activity of V1 neurons for visually guided saccades. We recorded single-cell activity from V1 of monkeys trained to detect and make saccades toward visual targets of varying contrast and analyzed trial-to-trial covariation between the onset time or firing rate of neural response and saccadic response time (RT). Neural latency (NL, the time of the first spike of neural response) was correlated with RT, whereas firing rate (FR) was not. When FR was computed with respect to target onset ignoring NL, a "false" correlation between FR and RT emerged. Multiple regression and partial correlation analyses on NL and FR for predictability of RT variability, as well as a simulation with artificial Poisson spike trains, supported the conclusion that the correlation between FR with respect to target onset and RT was mediated by a correlation between NL and RT, emphasizing the role of trial-to-trial variability of NL for extracting RT-related signals. We attempted to examine laminar differences in RT-related activity. Neurons recorded in the superficial layers tended to show a higher sensitivity to stimulus contrast and a lower correlation with RT compared with those in the lower layers, suggesting a sensory-to-motor transformation within V1 that follows the order of known anatomical connections. These results demonstrate that the trial-to-trial variability of neural response in V1 propagates to the stage of saccade execution, resulting in trial-to-trial variability of RT of a visually guided saccade.

Sparse coding and high-order correlations in fine-scale cortical networks

Connectivity in the cortex is organized at multiple scales, suggesting that scale-dependent correlated activity is particularly important for understanding the behaviour of sensory cortices and their function in stimulus encoding. We analysed the scale-dependent structure of cortical interactions by using maximum entropy models to characterize multiple-tetrode recordings from primary visual cortex of anaesthetized macaque monkeys (Macaca mulatta). We compared the properties of firing patterns among local clusters of neurons (<300 microm apart) with those of neurons separated by larger distances (600-2,500 microm). Here we report that local firing patterns are distinctive: whereas multi-neuronal firing patterns at larger distances can be predicted by pairwise interactions, patterns within local clusters often show evidence of high-order correlations. Surprisingly, these local correlations are flexible and rapidly reorganized by visual input. Although they modestly reduce the amount of information that a cluster conveys, they also modify the format of this information, creating sparser codes by increasing the periods of total quiescence, and concentrating information into briefer periods of common activity. These results imply a hierarchical organization of neuronal correlations: simple pairwise correlations link neurons over scales of tens to hundreds of minicolumns, but on the scale of a few minicolumns, ensembles of neurons form complex subnetworks whose moment-to-moment effective connectivity is dynamically reorganized by the stimulus.

Six principles of visual cortical dynamics

A fundamental goal in vision science is to determine how many neurons in how many areas are required to compute a coherent interpretation of the visual scene. Here I propose six principles of cortical dynamics of visual processing in the first 150 ms following the appearance of a visual stimulus. Fast synaptic communication between neurons depends on the driving neurons and the biophysical history and driving forces of the target neurons. Under these constraints, the retina communicates changes in the field of view driving large populations of neurons in visual areas into a dynamic sequence of feed-forward communication and integration of the inward current of the change signal into the dendrites of higher order area neurons (30-70 ms). Simultaneously an even larger number of neurons within each area receiving feed-forward input are pre-excited to sub-threshold levels. The higher order area neurons communicate the results of their computations as feedback adding inward current to the excited and pre-excited neurons in lower areas. This feedback reconciles computational differences between higher and lower areas (75-120 ms). This brings the lower area neurons into a new dynamic regime characterized by reduced driving forces and sparse firing reflecting the visual areas interpretation of the current scene (140 ms). The population membrane potentials and net-inward/outward currents and firing are well behaved at the mesoscopic scale, such that the decoding in retinotopic cortical space shows the visual areas' interpretation of the current scene. These dynamics have plausible biophysical explanations. The principles are theoretical, predictive, supported by recent experiments and easily lend themselves to experimental tests or computational modeling.

Decorrelated neuronal firing in cortical microcircuits

Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and to share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multitetrode arrays offering unprecedented recording quality to reexamine this question in the primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. Our findings suggest a refinement of current models of cortical microcircuit architecture and function: Either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated.

Long-term stability of visual pattern selective responses of monkey temporal lobe neurons

Many neurons in primate inferotemporal (IT) cortex respond selectively to complex, often meaningful, stimuli such as faces and objects. An important unanswered question is whether such response selectivity, which is thought to arise from experience-dependent plasticity, is maintained from day to day, or whether the roles of individual cells are continually reassigned based on the diet of natural vision. We addressed this question using microwire electrodes that were chronically implanted in the temporal lobe of two monkeys, often allowing us to monitor activity of individual neurons across days. We found that neurons maintained their selectivity in both response magnitude and patterns of spike timing across a large set of visual images throughout periods of stable signal isolation from the same cell that sometimes exceeded two weeks. These results indicate that stimulus-selectivity of responses in IT is stable across days and weeks of visual experience.

Spatial pattern coding of sensory information by climbing fiber-evoked calcium signals in networks of neighboring cerebellar Purkinje cells

Climbing fiber input produces complex spike synchrony across populations of cerebellar Purkinje cells oriented in the parasagittal axis. Elucidating the fine spatial structure of this synchrony is crucial for understanding its role in the encoding and processing of sensory information within the olivocerebellar cortical circuit. We investigated these issues using in vivo multineuron two-photon calcium imaging in combination with information theoretic analysis. Spontaneous dendritic calcium transients linked to climbing fiber input were observed in multiple neighboring Purkinje cells. Spontaneous synchrony of calcium transients between individual Purkinje cells falls off over approximately 200 microm mediolaterally, consistent with the presence of cerebellar microzones organized by climbing fiber input. Synchrony was increased after administration of harmaline, consistent with an olivary origin. Periodic sensory stimulation also resulted in a transient increase of synchrony after stimulus onset. To examine how synchrony affects the neural population code provided by the spatial pattern of complex spikes, we analyzed its information content. We found that spatial patterns of calcium events from small ensembles of cells provided substantially more stimulus information (59% more for seven-cell ensembles) than available by counting events across the pool without taking into account spatial origin. Information theoretic analysis indicated that, rather than contributing significantly to sensory coding via stimulus dependence, correlational effects on sensory coding are dominated by redundancy attributable to the prevalent spontaneous synchrony. The olivocerebellar circuit thus uses a labeled line code to report sensory signals, leaving open a role for synchrony in flexible selection of signals for output to deep cerebellar nuclei.

Stochasticity, spikes and decoding: sufficiency and utility of order statistics

For over 75 years it has been clear that the number of spikes in a neural response is an important part of the neuronal code. Starting as early as the 1950's with MacKay and McCullough, there has been speculation over whether each spike and its exact time of occurrence carry information. Although it is obvious that the firing rate carries information it has been less clear as to whether there is information in exactly timed patterns, when they arise from the dynamics of the neurons and networks, as opposed to when they represent some strong external drive that entrains them. One strong null hypothesis that can be applied is that spike trains arise from stochastic sampling of an underlying deterministic temporally modulated rate function, that is, there is a time-varying rate function. In this view, order statistics seem to provide a sufficient theoretical construct to both generate simulated spike trains that are indistinguishable from those observed experimentally, and to evaluate (decode) the data recovered from experiments. It remains to learn whether there are physiologically important signals that are not described by such a null hypothesis.

Spatial and temporal jitter distort estimated functional properties of visual sensory neurons

The functional properties of neural sensory cells or small neural ensembles are often characterized by analyzing response-conditioned stimulus ensembles. Many widely used analytical methods, like receptive fields (RF), Wiener kernels or spatio-temporal receptive fields (STRF), rely on simple statistics of those ensembles. They also tend to rely on simple noise models for the residuals of the conditional ensembles. However, in many cases the response-conditioned stimulus set has more complex structure. If not taken explicitly into account, it can bias the estimates of many simple statistics, and lead to erroneous conclusions about the functionality of a neural sensory system. In this article, we consider sensory noise in the visual system generated by small stimulus shifts in two dimensions (2 spatial or 1-space 1-time jitter). We model this noise as the action of a set of translations onto the stimulus that leave the response invariant. The analysis demonstrates that the spike-triggered average is a biased estimator of the model mean, and provides a de-biasing method. We apply this approach to observations from the stimulus/response characteristics of cells in the cat visual cortex and provide improved estimates of the structure of visual receptive fields. In several cases the new estimates differ substantially from the classic receptive fields, to a degree that may require re-evaluation of the functional description of the associated cells.

Spatial and temporal scales of neuronal correlation in primary visual cortex

The spiking activity of cortical neurons is correlated. For instance, trial-to-trial fluctuations in response strength are shared between neurons, and spikes often occur synchronously. Understanding the properties and mechanisms that generate these forms of correlation is critical for determining their role in cortical processing. We therefore investigated the spatial extent and functional specificity of correlated spontaneous and evoked activity. Because feedforward, recurrent, and feedback pathways have distinct extents and specificity, we reasoned that these measurements could elucidate the contribution of each type of input. We recorded single unit activity with microelectrode arrays which allowed us to measure correlation in many hundreds of pairings, across a large range of spatial scales. Our data show that correlated evoked activity is generated by two mechanisms that link neurons with similar orientation preferences on different spatial scales: one with high temporal precision and a limited spatial extent (approximately 3 mm), and a second that gives rise to correlation on a slow time scale and extends as far as we were able to measure (10 mm). The former is consistent with common input provided by horizontal connections; the latter likely involves feedback from extrastriate cortex. Spontaneous activity was correlated over a similar spatial extent, but approximately twice as strongly as evoked activity. Visual stimuli thus caused a substantial decrease in correlation, particularly at response onset. These properties and the circuit mechanism they imply provide new constraints on the functional role that correlation may play in visual processing.

Context-dependent changes in functional circuitry in visual area MT

Animals can flexibly change their behavior in response to a particular sensory stimulus; the mapping between sensory and motor representations in the brain must therefore be flexible as well. Changes in the correlated firing of pairs of neurons may provide a metric of changes in functional circuitry during behavior. We studied dynamic changes in functional circuitry by analyzing the noise correlations of simultaneously recorded MT neurons in two behavioral contexts: one that promotes cooperative interactions between the two neurons and another that promotes competitive interactions. We found that identical visual stimuli give rise to differences in noise correlation in the two contexts, suggesting that MT neurons receive inputs of central origin whose strength changes with the task structure. The data are consistent with a mixed feature-based attentional strategy model in which the animal sometimes alternates attention between opposite directions of motion and sometimes attends to the two directions simultaneously.

The role of feedback in visual masking and visual processing

This paper reviews the potential role of feedback in visual masking, for and against. Our analysis reveals constraints for feedback mecha- nisms that limit their potential role in visual masking, and in all other general brain functions. We propose a feedforward model of visual masking, and provide a hypothesis to explain the role of feedback in visual masking and visual processing in general. We review the anato-my and physiology of feedback mechanisms, and propose that the massive ratio of feedback versus feedforward connections in the visual system may be explained solely by the critical need for top-down attentional modulation. We discuss the merits of visual masking as a tool to discover the neural correlates of consciousness, especially as compared to other popular illusions, such as binocular rivalry. Finally, we propose a new set of neurophysiological standards needed to establish whether any given neuron or brain circuit may be the neural substrate of awareness.

Noise correlations have little influence on the coding of selective attention in area V1

Neurons in the visual primary cortex (area V1) do not only code simple features but also whether image elements are attended or not. These attentional signals are weaker than the feature-selective responses, and their reliability may therefore be limited by the noisiness of neuronal responses. Here we show that it is possible to decode the locus of attention on a single trial from the activity of a small population of neurons in area V1. Previous studies suggested that correlations between the activities of neurons that are part of a population limit the information gain, but here we report that the impact of these noise correlations depends on the relative position of the neurons' receptive fields. Correlations reduce the benefit of pooling neuronal responses evoked by the same object but actually enhance the advantage of pooling responses evoked by different objects. These opposing effects cancelled each other at the population level, so that the net effect of the noise correlations was negligible and attention could be decoded reliably. Our results suggest that noise correlations are caused by large-scale fluctuations in cortical excitability, which can be removed by a comparison of the response strengths evoked by different objects.

Information transmission by stimulus-dependent modulation of noise correlation

Cortical neurons that are near one another show correlated response variability (noise correlation), which can contribute to synergistic information transmission. In this study, we investigated the relationship between the level of external stimulation and noise correlation and its effect on population coding. Six levels of electrical stimulation were delivered to a rat's hind paw and responses of several neighboring neurons were simultaneously recorded in the primary somatosensory cortex. As the intensity of stimulation increased, noise correlation decreased down to near zero and then increased again to a relatively small value. The degree of synergistic information transmission depended on the amount by which noise correlation was modulated. Our results show that noise correlation among somatosensory cortical neurons is dynamically modulated by external stimulation, which allows transmission of additional information.