Relationship between spontaneous and evoked spike-time correlations in primate visual cortex

Basic Properties of Coordinated Neuronal Ensembles in the Auditory Thalamus

Coordinated neuronal activity has been identified to play an important role in information processing and transmission in the brain. However, current research predominantly focuses on understanding the properties and functions of neuronal coordination in hippocampal and cortical areas, leaving subcortical regions relatively unexplored. In this study, we use single-unit recordings in female Sprague Dawley rats to investigate the properties and functions of groups of neurons exhibiting coordinated activity in the auditory thalamus-the medial geniculate body (MGB). We reliably identify coordinated neuronal ensembles (cNEs), which are groups of neurons that fire synchronously, in the MGB. cNEs are shown not to be the result of false-positive detections or by-products of slow-state oscillations in anesthetized animals. We demonstrate that cNEs in the MGB have enhanced information-encoding properties over individual neurons. Their neuronal composition is stable between spontaneous and evoked activity, suggesting limited stimulus-induced ensemble dynamics. These MGB cNE properties are similar to what is observed in cNEs in the primary auditory cortex (A1), suggesting that ensembles serve as a ubiquitous mechanism for organizing local networks and play a fundamental role in sensory processing within the brain.

Repeated passive visual experience modulates spontaneous and non-familiar stimuli-evoked neural activity

Familiarity creates subjective memory of repeated innocuous experiences, reduces neural and behavioral responsiveness to those experiences, and enhances novelty detection. The neural correlates of the internal model of familiarity and the cellular mechanisms of enhanced novelty detection following multi-day repeated passive experience remain elusive. Using the mouse visual cortex as a model system, we test how the repeated passive experience of a 45° orientation-grating stimulus for multiple days alters spontaneous and non-familiar stimuli evoked neural activity in neurons tuned to familiar or non-familiar stimuli. We found that familiarity elicits stimulus competition such that stimulus selectivity reduces in neurons tuned to the familiar 45° stimulus; it increases in those tuned to the 90° stimulus but does not affect neurons tuned to the orthogonal 135° stimulus. Furthermore, neurons tuned to orientations 45° apart from the familiar stimulus dominate local functional connectivity. Interestingly, responsiveness to natural images, which consists of familiar and non-familiar orientations, increases subtly in neurons that exhibit stimulus competition. We also show the similarity between familiar grating stimulus-evoked and spontaneous activity increases, indicative of an internal model of altered experience.

Repeated passive visual experience modulates spontaneous and non-familiar stimulievoked neural activity

Familiarity creates subjective memory of repeated innocuous experiences, reduces neural and behavioral responsiveness to those experiences, and enhances novelty detection. The neural correlates of the internal model of familiarity and the cellular mechanisms of enhanced novelty detection following multi-day repeated passive experience remain elusive. Using the mouse visual cortex as a model system, we test how the repeated passive experience of a 45° orientation-grating stimulus for multiple days alters spontaneous and non-familiar stimuli evoked neural activity in neurons tuned to familiar or non-familiar stimuli. We found that familiarity elicits stimulus competition such that stimulus selectivity reduces in neurons tuned to the familiar 45° stimulus; it increases in those tuned to the 90° stimulus but does not affect neurons tuned to the orthogonal 135° stimulus. Furthermore, neurons tuned to orientations 45° apart from the familiar stimulus dominate local functional connectivity. Interestingly, responsiveness to natural images, which consists of familiar and non-familiar orientations, increases subtly in neurons that exhibit stimulus competition. We also show the similarity between familiar grating stimulus-evoked and spontaneous activity increases, indicative of an internal model of altered experience.

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.

State-Dependent Synchrony and Functional Connectivity in the Primary and Secondary Whisker Somatosensory Cortices

Synchronized activity plays an important role in sensory coding and memory and is a hallmark of functional network connectivity. However, the effect of sensory activation on synchronization and cortical functional connectivity is largely unknown. In this study, we investigated the effect of whisker activation on synchronization and functional connectivity of the primary (wS1) and secondary (wS2) whisker somatosensory cortices at the single-cell level. The results showed that during the spontaneous pre-stimulus state, neurons tended to be functionally connected with nearby neurons which shared similar tuning characteristics. Whisker activation using either ramp-and-hold stimulation or artificial whisking against sandpaper has significantly reduced the average overall pairwise synchronization and functional connectivity within the wS1 barrel and wS2 cortices. Whisker stimulation disconnected approximately a third of neuronal pairs that were functionally connected during the unstimulated state. Nearby neurons with congruent tuning properties were more likely to remain functionally connected during whisker activation. The findings of this study indicated that cortical somatosensory networks are organized in non-random small world networks composed of neurons sharing relatively similar tuning properties. Sensory whisker activation intensifies these properties and further subdivides the cortical network into smaller more functionally uniform subnetworks, which possibly serve to increase the computational capacity of the network.

The Spatiotemporal Patterns of Climate Asymmetric Warming and Vegetation Activities in an Arid and Semiarid Region

Asymmetric warming was bound to have a major impact on terrestrial ecosystems in arid regions during global warming. Further study was necessary to reveal the spatiotemporal patterns of asymmetric warming in Xinjiang; this study analyzed the climate and normalized difference vegetation index (NDVI) data (2000–2020). The change trends of the day and nighttime warming (DNW), seasonal warming, and the diurnal temperature range in northern Xinjiang (S1) and southern Xinjiang (S2) were determined. The findings indicated that the DNW rate showed a significant (p < 0.05) upward trend, especially in winter. The nighttime warming rate (0.65 °C (decade)−1) was faster than the daytime warming rate (0.4 °C (decade)−1), and the diurnal temperature range between daytime and nighttime exhibited a decreasing trend. The diurnal temperature range was the highest in spring and the lowest in winter. Extreme values of the diurnal temperature range appeared in autumn (48.6 °C) and winter (12.3 °C) and both in S1. The Tmin in S1 had an abruption trend in 2006–2017, the Tmax in S2 had an abruption trend in 2005–2011, and the probability of spatial abruption in S1 was higher than that in S2. The partial correlation between the NDVI and Tmin was significantly higher than that between the NDVI and Tmax in the area where the significance test passed; therefore, asymmetric nighttime warming had a greater impact on the NDVI than the asymmetric daytime warming.

Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

Previous studies in neurophysiology have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We show that this form of voxelwise NCs can improve MVPA performance if NCs are sufficiently strong. We also confirm these results using standard information-theoretic analyses in computational neuroscience. In the same theoretical framework, we further demonstrate that the effects of noise correlations at both the neuronal level and the voxel level may manifest differently in typical fMRI data, and their effects are modulated by tuning heterogeneity. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

Noise correlation (NC) is the key component of multivariate response distributions and thus characterizing its effects on population codes is the cornerstone for understanding probabilistic computation in the brain. Despite extensive studies of NCs in neurophysiology, little is known with respect to their role in functional magnetic resonance imaging (fMRI). We characterize the effect of voxelwise NC by building voxel-encoding models and directly quantifying the amount of information in simulated multivariate fMRI data. In contrast to the detrimental effects of NC implied in neurophysiological studies, we find that voxelwise NCs can enhance information codes if NC is sufficiently strong. Our work highlights the important role of noise correlations in decipher population codes using fMRI.

Re-learning to be different: Increased neural differentiation supports post-stroke language recovery

Identifying the neural changes that support recovery of cognitive functions after a brain lesion is important to advance our understanding of human neuroplasticity, which, in turn, forms the basis for the development of effective treatments. To date, the preponderance of neuroimaging studies has focused on localizing changes in average brain activity associated with functional recovery. Here, we took a novel approach by evaluating whether cognitive recovery in chronic stroke is related to increases in the differentiation of local neural response patterns. This approach is supported by research indicating that, in the intact brain, local neural representations become more differentiated (dissimilar) with learning (Glezer et al., 2015). We acquired fMRI data before and after 21 individuals received approximately 12 weeks of behavioral treatment for written language impairment due to a left-hemisphere stroke. We used Local-Heterogeneity Regression Analysis (Purcell and Rapp, 2018) to measure local neural response differentiation associated with written language processing, assuming that greater heterogeneity in the pattern of activity across adjacent neural areas indicates more well-differentiated neural representations. First, we observed pre to post-treatment increases in local neural differentiation (Local-Hreg) in the ventral occipital-temporal cortex of the left hemisphere. Second, we found that, in this region, higher local neural response differentiation prior to treatment was associated with less severe written language impairment, and that it also predicted greater future responsiveness to treatment. Third, we observed that changes in neural differentiation were systematically related to performance changes for trained and untrained items. Fourth, we did not observe these brain-behavior relationships for mean BOLD responses, only for Local-Hreg. Thus, this is the first investigation to quantify changes in local neural differentiation in the recovery of a cognitive function and the first to demonstrate the clear behavioral relevance of these changes. We conclude that the findings provide strong support for the novel hypothesis that the local re-differentiation of neural representations can play a significant role in functional recovery after brain lesion.

Stimulus-Tuned Structure of Correlated fMRI Activity in Human Visual Cortex

Processing units are interconnected in the visual system, where a sensory organ and downstream cortical regions communicate through hierarchical connections, and local sites within the regions communicate through horizontal connections. In such networks, neural activities at local sites are likely to influence one another in complex ways and thus are intricately correlated. Recognizing the functional importance of correlated activity in sensory representation, spontaneous activities have been studied via diverse local or global measures in various time scales. Here, measuring functional magnetic resonance imaging (fMRI) signals in human early visual cortex, we explored systematic patterns that govern the correlated activities arising spontaneously. Specifically, guided by previously identified biases in anatomical connection patterns, we characterized all possible pairs of gray matter sites in 3 relational factors: "retinotopic distance," "cortical distance," and "stimulus tuning similarity." By evaluating and comparing the unique contributions of these factors to the correlated activity, we found that tuning similarity factors overrode distance factors in accounting for the structure of correlated fMRI activity both within and between V1, V2, and V3, irrespective of the presence or degree of visual stimulation. Our findings indicate that the early human visual cortex is intrinsically organized as a network tuned to the stimulus features.

Correlation of Synaptic Inputs in the Visual Cortex of Awake, Behaving Mice

The subthreshold mechanisms that underlie neuronal correlations in awake animals are poorly understood. Here, we perform dual whole-cell recordings in the visual cortex (V1) of awake mice to investigate membrane potential (Vm) correlations between upper-layer sensory neurons. We find that the membrane potentials of neighboring neurons display large, correlated fluctuations during quiet wakefulness, including pairs of cells with disparate tuning properties. These fluctuations are driven by correlated barrages of excitation followed closely by inhibition (∼5-ms lag). During visual stimulation, low-frequency activity is diminished, and coherent high-frequency oscillations appear, even for non-preferred stimuli. These oscillations are generated by alternating excitatory and inhibitory inputs at a similar lag. The temporal sequence of depolarization for pairs of neurons is conserved during both spontaneous- and visually-evoked activity, suggesting a stereotyped flow of activation that may function to produce temporally precise "windows of opportunity" for additional synaptic inputs.

Local response heterogeneity indexes experience-based neural differentiation in reading

The ability to read requires learning letter-string representations whose neural codes would be expected to vary depending on the amount of experience that an individual has with reading them. Motivated by sparse coding theories (e.g., Rolls and Tovee, 1995; Olshausen and Field, 1996), recent work has demonstrated that better-learned relative to less well-learned neural representations are associated with more strongly differentiated, locally heterogeneous blood oxygenation level dependent (BOLD) responses (e.g., Jiang et al., 2013). Here we report a novel analysis method we call local heterogeneity regression (Local-Hreg) that quantifies the cross-voxel heterogeneity of BOLD responses, thereby providing a sensitive and methodologically flexible method for quantifying the local neural differentiation of neural representations. In a study of literate adults, we applied Local-Hreg to fMRI data obtained when participants read letter strings that varied in their frequency of occurrence in the written language. Consistent with previous research identifying the left ventral occipitotemporal cortex (vOTC) as a key site for orthographic representation in reading and spelling, we found that the cross-voxel heterogeneity of neural responses in this region varies according to the frequency with which the written letter strings have been experienced. This work provides a novel approach for examining the local differentiation of neural representations, and demonstrates that well-learned words have greater representational differentiation than less well-learned or unfamiliar words.

Coordinated neuronal ensembles in primary auditory cortical columns

The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI.

Individual Differences in Cognitive Function in Older Adults Predicted by Neuronal Selectivity at Corresponding Brain Regions

Relating individual differences in cognitive abilities to neural substrates in older adults is of significant scientific and clinical interest, but remains a major challenge. Previous functional magnetic resonance imaging (fMRI) studies of cognitive aging have mainly focused on the amplitude of fMRI response, which does not measure neuronal selectivity and has led to some conflicting findings. Here, using local regional heterogeneity analysis, or Hcorr , a novel fMRI analysis technique developed to probe the sparseness of neuronal activations as an indirect measure of neuronal selectivity, we found that individual differences in two different cognitive functions, episodic memory and letter verbal fluency, are selectively related to Hcorr -estimated neuronal selectivity at their corresponding brain regions (hippocampus and visual-word form area, respectively). This suggests a direct relationship between cognitive function and neuronal selectivity at the corresponding brain regions in healthy older adults, which in turn suggests that age-related neural dedifferentiation might contribute to rather than compensate for cognitive decline in healthy older adults. Additionally, the capability to estimate neuronal selectivity across brain regions with a single data set and link them to cognitive performance suggests that, compared to fMRI-adaptation-the established fMRI technique to assess neuronal selectivity, Hcorr might be a better alternative in studying normal aging and neurodegenerative diseases, both of which are associated with widespread changes across the brain.

The Nature of Shared Cortical Variability

Neuronal responses of sensory cortex are highly variable, and this variability is correlated across neurons. To assess how variability reflects factors shared across a neuronal population, we analyzed the activity of many simultaneously recorded neurons in visual cortex. We developed a simple model that comprises two sources of shared variability: a multiplicative gain, which uniformly scales each neuron’s sensory drive, and an additive offset, which affects different neurons to different degrees. This model captured the variability of spike counts and reproduced the dependence of pairwise correlations on neuronal tuning and stimulus orientation. The relative contributions of the additive and multiplicative fluctuations could vary over time and had marked impact on population coding. These observations indicate that shared variability of neuronal populations in sensory cortex can be largely explained by two factors that modulate the whole population.

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Response variability in V1 neuronal populations is largely shared across neurons

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Shared variability involves two factors: a multiplicative gain and an additive offset

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These two factors predict sensory responses of large populations on single trials

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They determine pairwise correlations and constrain information coding

Correlates of a single cortical action potential in the epidural EEG

To identify the correlates of a single cortical action potential in surface EEG, we recorded simultaneously epidural EEG and single-unit activity in the primary somatosensory cortex of awake macaque monkeys. By averaging over EEG segments coincident with more than hundred thousand single spikes, we found short-lived (≈ 0.5 ms) triphasic EEG deflections dominated by high-frequency components > 800 Hz. The peak-to-peak amplitude of the grand-averaged spike correlate was 80 nV, which matched theoretical predictions, while single-neuron amplitudes ranged from 12 to 966 nV. Combining these estimates with post-stimulus-time histograms of single-unit responses to median-nerve stimulation allowed us to predict the shape of the evoked epidural EEG response and to estimate the number of contributing neurons. These findings establish spiking activity of cortical neurons as a primary building block of high-frequency epidural EEG, which thus can serve as a quantitative macroscopic marker of neuronal spikes.

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Cortical spikes are coincident with short-lived (~ 0.5 ms) EEG deflections.

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Cortical spikes produce ~  80 nV epidural EEG deflections at a distance of ~ 5 mm.

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EEG potentials due to spikes are dominated by high-frequency (> 800 Hz) components.

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High-frequency (> 800 Hz) EEG is a genuine macroscopic marker of spiking activity.

Reduced neural specificity in middle-aged HIV+ women in the absence of behavioral deficits

In the post combination antiretroviral therapy (cART) era, the prevalence of mild forms of HIV-associated neurocognitive disorders (HAND) in individuals with HIV-infection remains high. There is a pressing need to find biomarkers that can aid clinical assessment of HAND, especially in those with mild or no neurocognitive symptoms. Here we hypothesized that a reduction in neural specificity, or the specificity of neuronal tuning, could serve as a potential biomarker of asymptomatic HAND. To directly test this hypothesis, we applied two advanced fMRI techniques to examine the difference in neural specificity between middle-aged HIV+ women and age-matched negative controls, with a focus on the fusiform face area (FFA), a critical region in face processing. Face discrimination performance was assessed outside of the scanner. While the behavioral performance of face discrimination was comparable between the two groups, a reduced neural specificity in the FFA of HIV-positive women was revealed by a novel fMRI analysis technique, local regional heterogeneity analysis, or H_corr, as well as an established technique, fMRI-rapid adaptation. In contrast, conventional fMRI techniques were insensitive to these early changes. These results suggest that, prior to the onset of detectable behavioral deficits, significant neuronal dysfunctions are already present in HIV+ individuals, and these early neuronal dysfunctions can be detected and assessed via neural specificity, which, in combining with the novel H_corr technique, has a strong potential to serve as a biomarker of asymptomatic HAND and other neurodegenerative diseases.

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We investigate early neuronal dysfunctions in cognitively normal HIV+ women.

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Conventional fMRI technique reveals normal neural activity in the FFA of HIV+ women.

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fMRI-adaptation reveals a decrease in neural specificity in the FFA of HIV+ women.

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H_corr, a novel fMRI technique, confirms the fMRI-adaptation results

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H_corr-estimated neural specificity might serve as a biomarker of asymptomatic HAND

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

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

Tuning dissimilarity explains short distance decline of spontaneous spike correlation in macaque V1

Fast spike correlation is a signature of neural ensemble activity thought to underlie perception, cognition, and action. To relate spike correlation to tuning and other factors, we focused on spontaneous activity because it is the common 'baseline' across studies that test different stimuli, and because variations in correlation strength are much larger across cell pairs than across stimuli. Is the probability of spike correlation between two neurons a graded function of lateral cortical separation, independent of functional tuning (e.g. orientation preferences)? Although previous studies found a steep decline in fast spike correlation with horizontal cortical distance, we hypothesized that, at short distances, this decline is better explained by a decline in receptive field tuning similarity. Here we measured macaque V1 tuning via parametric stimuli and spike-triggered analysis, and we developed a generalized linear model (GLM) to examine how different combinations of factors predict spontaneous spike correlation. Spike correlation was predicted by multiple factors including color, spatiotemporal receptive field, spatial frequency, phase and orientation but not ocular dominance beyond layer 4. Including these factors in the model mostly eliminated the contribution of cortical distance to fast spike correlation (up to our recording limit of 1.4mm), in terms of both 'correlation probability' (the incidence of pairs that have significant fast spike correlation) and 'correlation strength' (each pair's likelihood of fast spike correlation). We suggest that, at short distances and non-input layers, V1 fast spike correlation is determined more by tuning similarity than by cortical distance or ocular dominance.

Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates

A fundamental goal of neuroscience is to understand how cognitive processes, such as operant conditioning, are performed by the brain. Typical and well studied examples of operant conditioning, in which the firing rates of individual cortical neurons in monkeys are increased using rewards, provide an opportunity for insight into this. Studies of reward-modulated spike-timing-dependent plasticity (RSTDP), and of other models such as R-max, have reproduced this learning behavior, but they have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards). We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To fix this issue, we propose a new RSTDP model of synaptic plasticity based upon the observed effects that dopamine has on long-term potentiation and depression (LTP and LTD). We show, both analytically and through simulations, that our new model can exhibit unsupervised learning and lead to firing rate reinforcement. This requires that the strengthening of LTP by the reward signal is greater than the strengthening of LTD and that the reinforced neuron exhibits irregular firing. We show the robustness of our findings to spike-timing correlations, to the synaptic weight dependence that is assumed, and to changes in the mean reward. We also consider our model in the differential reinforcement of two nearby neurons. Our model aligns more strongly with experimental studies than previous models and makes testable predictions for future experiments.

Spike synchronization in cat primary visual cortex depends on similarity of surround-suppression magnitude

In the primary visual cortex (V1), the spike synchronization seen in neuron pairs with non-overlapping receptive fields can be explained by similarities in their preferred orientation (PO). However, this is not true for pairs with overlapping receptive fields, as they can still exhibit spike synchronization even if their POs are only weakly correlated. Here, we investigated the relationship between spike synchronization and suppressive modulation derived from classical receptive-field surround (surround suppression). We found that layer 4 and layer 2/3 pairs exhibited mainly asymmetric spike synchronization that had non-zero time-lags and was dependent on both the similarity of the PO and the strength of surround suppression. In contrast, layer 2/3 and layer 2/3 pairs showed mainly symmetric spike synchronization that had zero time-lag and was dependent on the similarity of the strength of surround suppression but not on the similarity in POs. From these results, we propose that in cat V1 there exists a functional network that mainly depends on the similarity in surround suppression, and that in layer 2/3 neurons the network maintains surround suppression that is primarily inherited from layer 4 neurons.

Increased neural correlations in primate auditory cortex during slow-wave sleep

During sleep, changes in brain rhythms and neuromodulator levels in cortex modify the properties of individual neurons and the network as a whole. In principle, network-level interactions during sleep can be studied by observing covariation in spontaneous activity between neurons. Spontaneous activity, however, reflects only a portion of the effective functional connectivity that is activated by external and internal inputs (e.g., sensory stimulation, motor behavior, and mental activity), and it has been shown that neural responses are less correlated during external sensory stimulation than during spontaneous activity. Here, we took advantage of the unique property that the auditory cortex continues to respond to sounds during sleep and used external acoustic stimuli to activate cortical networks for studying neural interactions during sleep. We found that during slow-wave sleep (SWS), local (neuron-neuron) correlations are not reduced by acoustic stimulation remaining higher than in wakefulness and rapid eye movement sleep and remaining similar to spontaneous activity correlations. This high level of correlations during SWS complements previous work finding elevated global (local field potential-local field potential) correlations during sleep. Contrary to the prediction that slow oscillations in SWS would increase neural correlations during spontaneous activity, we found little change in neural correlations outside of periods of acoustic stimulation. Rather, these findings suggest that functional connections recruited in sound processing are modified during SWS and that slow rhythms, which in general are suppressed by sensory stimulation, are not the sole mechanism leading to elevated network correlations during sleep.

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.

A quantitative link between face discrimination deficits and neuronal selectivity for faces in autism

Individuals with Autism Spectrum Disorder (ASD) appear to show a general face discrimination deficit across a range of tasks including social–emotional judgments as well as identification and discrimination. However, functional magnetic resonance imaging (fMRI) studies probing the neural bases of these behavioral differences have produced conflicting results: while some studies have reported reduced or no activity to faces in ASD in the Fusiform Face Area (FFA), a key region in human face processing, others have suggested more typical activation levels, possibly reflecting limitations of conventional fMRI techniques to characterize neuron-level processing. Here, we test the hypotheses that face discrimination abilities are highly heterogeneous in ASD and are mediated by FFA neurons, with differences in face discrimination abilities being quantitatively linked to variations in the estimated selectivity of face neurons in the FFA. Behavioral results revealed a wide distribution of face discrimination performance in ASD, ranging from typical performance to chance level performance. Despite this heterogeneity in perceptual abilities, individual face discrimination performance was well predicted by neural selectivity to faces in the FFA, estimated via both a novel analysis of local voxel-wise correlations, and the more commonly used fMRI rapid adaptation technique. Thus, face processing in ASD appears to rely on the FFA as in typical individuals, differing quantitatively but not qualitatively. These results for the first time mechanistically link variations in the ASD phenotype to specific differences in the typical face processing circuit, identifying promising targets for interventions.

Discharge-rate persistence of baseline activity during fixation reflects maintenance of memory-period activity in the macaque posterior parietal cortex

Recent evidence has demonstrated that spatiotemporal patterns of spontaneous activity reflect the patterns of activity evoked by sensory stimuli. However, few studies have examined whether response profiles of task-evoked activity, which is not related to external sensory stimuli but rather to internal processes, are also reflected in those of spontaneous activity. To address this, we recorded activity of neurons in the lateral intraparietal area (LIP) when monkeys performed reaction-time and delayed-response visual-search tasks. We particularly focused on the target location-dependent modulation of delay-period activity (delay-period modulation) in the delayed-response task, and the discharge-rate persistency in fixation-period activity (baseline-activity maintenance) in the reaction-time task. Baseline-activity maintenance was assessed by the correlation between the spike counts of 2 separate bins. We found that baseline-activity maintenance, calculated from bins separated by a long interval (200-500 ms), was correlated with delay-period modulation, whereas that calculated from bins separated by a short interval (~100 ms) was correlated with trial-to-trial fluctuations in baseline activity, suggesting a link between the capability to hold task-related information in delay-period activity and the degree of baseline-activity maintenance in a timescale-dependent manner.

Intrinsic signal optical imaging evidence for dorsal V3 in the prosimian galago (Otolemur garnettii)

Currently, we lack consensus regarding the organization along the anterior border of dorsomedial V2 in primates. Previous studies suggest that this region could be either the dorsomedial area, characterized by both an upper and a lower visual field representation, or the dorsal aspect of area V3, which only contains a lower visual field representation. We examined these proposals by using optical imaging of intrinsic signals to investigate this region in the prosimian galago (Otolemur garnettii). Galagos represent the prosimian radiation of surviving primates; cortical areas that bear strong resemblances across members of primates provide a strong argument for their early origin and conserved existence. Based on our mapping of horizontal and vertical meridian representations, visuotopy, and orientation preference, we find a clear lower field representation anterior to dorsal V2 but no evidence of any upper field representation. We also show statistical differences in orientation preference patches between V2 and V3. We additionally supplement our imaging results with electrode array data that reveal differences in the average spatial frequency preference, average temporal frequency preference, and sizes of the receptive fields between V1, V2, and V3. The lack of upper visual field representation along with the differences between the neighboring visual areas clearly distinguish the region anterior to dorsal V2 from earlier visual areas and argue against a DM that lies along the dorsomedial border of V2. We submit that the region of the cortex in question is the dorsal aspect of V3, thus strengthening the possibility that V3 is conserved among primates.

Dynamics of spiking neurons: between homogeneity and synchrony

Randomly connected networks of neurons driven by Poisson inputs are often assumed to produce "homogeneous" dynamics, characterized by largely independent firing and approximable by diffusion processes. At the same time, it is well known that such networks can fire synchronously. Between these two much studied scenarios lies a vastly complex dynamical landscape that is relatively unexplored. In this paper, we discuss a phenomenon which commonly manifests in these intermediate regimes, namely brief spurts of spiking activity which we call multiple firing events (MFE). These events do not depend on structured network architecture nor on structured input; they are an emergent property of the system. We came upon them in an earlier modeling paper, in which we discovered, through a careful benchmarking process, that MFEs are the single most important dynamical mechanism behind many of the V1 phenomena we were able to replicate. In this paper we explain in a simpler setting how MFEs come about, as well as their potential dynamic consequences. Although the mechanism underlying MFEs cannot easily be captured by current population dynamics models, this phenomena should not be ignored during analysis; there is a growing body of evidence that such collaborative activity may be a key towards unlocking the possible functional properties of many neuronal networks.

Effects of spatiotemporal stimulus properties on spike timing correlations in owl monkey primary somatosensory cortex

The correlated discharges of cortical neurons in primary somatosensory cortex are a potential source of information about somatosensory stimuli. One aspect of neuronal correlations that has not been well studied is how the spatiotemporal properties of tactile stimuli affect the presence and magnitude of correlations. We presented single- and dual-point stimuli with varying spatiotemporal relationships to the hands of three anesthetized owl monkeys and recorded neuronal activity from 100-electrode arrays implanted in primary somatosensory cortex. Correlation magnitudes derived from joint peristimulus time histogram (JPSTH) analysis of single neuron pairs were used to determine the level of spike timing correlations under selected spatiotemporal stimulus conditions. Correlated activities between neuron pairs were commonly observed, and the proportions of correlated pairs tended to decrease with distance between the recorded neurons. Distance between stimulus sites also affected correlations. When stimuli were presented simultaneously at two sites, ∼37% of the recorded neuron pairs showed significant correlations when adjacent phalanges were stimulated, and ∼21% of the pairs were significantly correlated when nonadjacent digits were stimulated. Spatial proximity of paired stimuli also increased the average correlation magnitude. Stimulus onset asynchronies in the paired stimuli had small effects on the correlation magnitude. These results show that correlated discharges between neurons at the first level of cortical processing provide information about the relative locations of two stimuli on the hand.

Beyond traditional approaches to understanding the functional role of neuromodulators in sensory cortices

Over the last two decades, a vast literature has described the influence of neuromodulatory systems on the responses of sensory cortex neurons (review in Gu, 2002; Edeline, 2003; Weinberger, 2003; Metherate, 2004, 2011). At the single cell level, facilitation of evoked responses, increases in signal-to-noise ratio, and improved functional properties of sensory cortex neurons have been reported in the visual, auditory, and somatosensory modality. At the map level, massive cortical reorganizations have been described when repeated activation of a neuromodulatory system are associated with a particular sensory stimulus. In reviewing our knowledge concerning the way the noradrenergic and cholinergic system control sensory cortices, I will point out that the differences between the protocols used to reveal these effects most likely reflect different assumptions concerning the role of the neuromodulators. More importantly, a gap still exists between the descriptions of neuromodulatory effects and the concepts that are currently applied to decipher the neural code operating in sensory cortices. Key examples that bring this gap into focus are the concept of cell assemblies and the role played by the spike timing precision (i.e., by the temporal organization of spike trains at the millisecond time-scale) which are now recognized as essential in sensory physiology but are rarely considered in experiments describing the role of neuromodulators in sensory cortices. Thus, I will suggest that several lines of research, particularly in the field of computational neurosciences, should help us to go beyond traditional approaches and, ultimately, to understand how neuromodulators impact on the cortical mechanisms underlying our perceptual abilities.

Default activity patterns at the neocortical microcircuit level

Even in absence of sensory stimuli cortical networks exhibit complex, self-organized activity patterns. While the function of those spontaneous patterns of activation remains poorly understood, recent studies both in vivo and in vitro have demonstrated that neocortical neurons activate in a surprisingly similar sequential order both spontaneously and following input into cortex. For example, neurons that tend to fire earlier within spontaneous bursts of activity also fire earlier than other neurons in response to sensory stimuli. These “default patterns” can last hundreds of milliseconds and are strongly conserved under a variety of conditions. In this paper, we will review recent evidence for these default patterns at the local cortical level. We speculate that cortical architecture imposes common constraints on spontaneous and evoked activity flow, which result in the similarity of the patterns.

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.

Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex

Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.

Measuring and interpreting neuronal correlations

Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the effect of correlations on cortical processing.

Membrane potential synchrony in primary visual cortex during sensory stimulation

When the primary visual cortex (V1) is activated by sensory stimulation, what is the temporal correlation between the synaptic inputs to nearby neurons? This question underlies the origin of correlated activity, the mechanism of how visually evoked activity emerges and propagates in cortical circuits, and the relationship between spontaneous and evoked activity. Here, we have recorded membrane potential from pairs of V1 neurons in anesthetized cats and found that visual stimulation suppressed low-frequency membrane potential synchrony (0-10 Hz), and often increased synchrony at high frequencies (20-80 Hz). The increase in high-frequency synchrony occurred for neurons with similar orientation preferences and for neurons with different orientation preferences and occurred for a wide range of stimulus orientations. Thus, while only a subset of neurons spike in response to visual stimulation, a far larger proportion of the circuit is correlated with spiking activity through subthreshold, high-frequency synchronous activity that crosses functional domains.

How do neurons work together? Lessons from auditory cortex

Recordings of single neurons have yielded great insights into the way acoustic stimuli are represented in auditory cortex. However, any one neuron functions as part of a population whose combined activity underlies cortical information processing. Here we review some results obtained by recording simultaneously from auditory cortical populations and individual morphologically identified neurons, in urethane-anesthetized and unanesthetized passively listening rats. Auditory cortical populations produced structured activity patterns both in response to acoustic stimuli, and spontaneously without sensory input. Population spike time patterns were broadly conserved across multiple sensory stimuli and spontaneous events, exhibiting a generally conserved sequential organization lasting approximately 100 ms. Both spontaneous and evoked events exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, and densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. Laminar propagation differed however, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. In both unanesthetized and urethanized rats, global activity fluctuated between "desynchronized" state characterized by low amplitude, high-frequency local field potentials and a "synchronized" state of larger, lower-frequency waves. Computational studies suggested that responses could be predicted by a simple dynamical system model fitted to the spontaneous activity immediately preceding stimulus presentation. Fitting this model to the data yielded a nonlinear self-exciting system model in synchronized states and an approximately linear system in desynchronized states. We comment on the significance of these results for auditory cortical processing of acoustic and non-acoustic information.

Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4

Attention typically amplifies neuronal responses evoked by task-relevant stimuli while attenuating responses to task-irrelevant distracters. In this context, visual distracters constitute an external source of noise that is diminished to improve attended signal quality. Activity that is internal to the cortex itself, stimulus-independent ongoing correlated fluctuations in firing, might also act as task-irrelevant noise. To examine this, we recorded from area V4 of macaques performing an attention-demanding task. The firing of neurons to identically repeated stimuli was highly variable. Much of this variability originates from ongoing low-frequency (<5 Hz) fluctuations in rate correlated across the neuronal population. When attention is directed to a stimulus inside a neuron's receptive field, these correlated fluctuations in rate are reduced. This attention-dependent reduction of ongoing cortical activity improves the signal-to-noise ratio of pooled neural signals substantially more than attention-dependent increases in firing rate.

Correlations and brain states: from electrophysiology to functional imaging

Neural activity in cortex is correlated, an observation that has traditionally been attributed to neurons receiving input from a shared and limited presynaptic pool. Recent studies have shown that correlations are also strongly influenced by network fluctuations that operate over a range of spatial and temporal scales, extending in some cases across cortical areas. These fluctuations are sensitive to internal states and external drive, so that correlations themselves depend strongly on cognitive state and stimulus properties. Given the potential impact on population coding, this modulation of correlations may play an important role in sensory processing.