Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy

Network mechanisms of ongoing brain activity's influence on conscious visual perception

Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity's influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.

The retina-brain axis and diabetic retinopathy

Diabetic retinopathy (DR) is a major contributor to permanent vision loss and blindness. Changes in retinal neurons, glia, and microvasculature have been the focus of intensive study in the quest to better understand DR. However, the impact of diabetes on the rest of the visual system has received less attention. There are reports of associations of changes in the visual system with preclinical and clinical manifestations of diabetes. Simultaneous investigation of the retina and the brain may shed light on the mechanisms underlying neurodegeneration in diabetics. Additionally, investigating the links between DR and other neurodegenerative disorders of the brain including Alzheimer's and Parkinson's disease may reveal shared mechanisms for neurodegeneration and potential therapy options.

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.

Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI

The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.

On the Role of LGN/V1 Spontaneous Activity as an Innate Learning Pattern for Visual Development

Correlated, spontaneous neural activity is known to play a necessary role in visual development, but the higher-order statistical structure of these coherent, amorphous patterns has only begun to emerge in the past decade. Several computational studies have demonstrated how this endogenous activity can be used to train a developing visual system. Models that generate spontaneous activity analogous to retinal waves have shown that these waves can serve as stimuli for efficient coding models of V1. This general strategy in development has one clear advantage: The same learning algorithm can be used both before and after eye-opening. This same insight can be applied to understanding LGN/V1 spontaneous activity. Although lateral geniculate nucleus (LGN) activity has been less discussed in the literature than retinal waves, here we argue that the waves found in the LGN have a number of properties that fill the role of a training pattern. We make the case that the role of “innate learning” with spontaneous activity is not only possible, but likely in later stages of visual development, and worth pursuing further using an efficient coding paradigm.

Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states

Studies of sensory-evoked neuronal responses often focus on mean spike rates, with fluctuations treated as internally-generated noise. However, fluctuations of spontaneous activity, often organized as traveling waves, shape stimulus-evoked responses and perceptual sensitivity. The mechanisms underlying these waves are unknown. Further, it is unclear whether waves are consistent with the low rate and weakly correlated “asynchronous-irregular” dynamics observed in cortical recordings. Here, we describe a large-scale computational model with topographically-organized connectivity and conduction delays relevant to biological scales. We find that spontaneous traveling waves are a general property of these networks. The traveling waves that occur in the model are sparse, with only a small fraction of neurons participating in any individual wave. Consequently, they do not induce measurable spike correlations and remain consistent with locally asynchronous irregular states. Further, by modulating local network state, they can shape responses to incoming inputs as observed in vivo.

Spontaneous traveling cortical waves shape neural responses. Using a large-scale computational model, the authors show that transmission delays shape locally asynchronous spiking dynamics into traveling waves without inducing correlations and boost responses to external input, as observed in vivo.

Multiple states in ongoing neural activity in the rat visual cortex

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.

Modulation in cortical excitability disrupts information transfer in perceptual-level stimulus processing

Despite significant interest in the neural underpinnings of behavioral variability, little light has been shed on the cortical mechanism underlying the failure to respond to perceptual-level stimuli. We hypothesized that cortical activity resulting from perceptual-level stimuli is sensitive to the moment-to-moment fluctuations in cortical excitability, and thus may not suffice to produce a behavioral response. We tested this hypothesis using electrocorticographic recordings to follow the propagation of cortical activity in six human subjects that responded to perceptual-level auditory stimuli. Here we show that for presentations that did not result in a behavioral response, the likelihood of cortical activity decreased from auditory cortex to motor cortex, and was related to reduced local cortical excitability. Cortical excitability was quantified using instantaneous voltage during a short window prior to cortical activity onset. Therefore, when humans are presented with an auditory stimulus close to perceptual-level threshold, moment-by-moment fluctuations in cortical excitability determine whether cortical responses to sensory stimulation successfully connect auditory input to a resultant behavioral response.

Top-down coordination of local cortical state during selective attention

Spontaneous fluctuations in cortical excitability influence sensory processing and behavior. These fluctuations, long thought to reflect global changes in cortical state, were recently found to be modulated locally within a retinotopic map during spatially selective attention. We report that periods of vigorous (On) and faint (Off) spiking activity, the signature of cortical state fluctuations, are coordinated across brain areas with retinotopic precision. Top-down attention enhanced interareal local state coordination, traversing along the reverse cortical hierarchy. The extent of local state coordination between areas was predictive of behavioral performance. Our results show that cortical state dynamics are shared across brain regions, modulated by cognitive demands and relevant for behavior.

Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex

An animal's decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This state embodies multiple cognitive factors, such as arousal and fatigue, but it is unclear how these factors influence the neural processes that encode sensory stimuli and form a decision. We discovered that, unprompted by task conditions, animals slowly shifted their likelihood of detecting stimulus changes over the timescale of tens of minutes. Neural population activity from visual area V4, as well as from prefrontal cortex, slowly drifted together with these behavioral fluctuations. We found that this slow drift, rather than altering the encoding of the sensory stimulus, acted as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in population activity across multiple brain areas and sheds further light on how internal states contribute to the decision-making process.

Frequency-separated principal component analysis of cortical population activity

A hallmark of neocortical activity is the presence of low-dimensional fluctuations in firing rate that are coordinated across neurons. However, the impact of these fluctuations on sensory processing remains unclear. Here, we examined fluctuations in populations of orientation-selective neurons from anesthetized macaque primary visual cortex (V1) during stimulus viewing as well as spontaneous activity. We introduce a novel approach termed frequency-separated principal component analysis (FS-PCA) to characterize these fluctuations. This method unveiled a distribution of components with a broad range of frequencies whose eigenvalues and variance followed an approximate power law. During stimulus viewing, subpopulations of V1 neurons correlated either positively or negatively with low-dimensional fluctuations. These two subpopulations displayed distinct activation properties and noise correlations in response to sensory input. Together, results suggest that slow, low-dimensional fluctuations in V1 population activity shape the response of individual neurons to oriented stimuli and may impact the transmission of sensory information to downstream regions of the primary visual system.NEW & NOTEWORTHY A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior

Perception reflects not only sensory inputs, but also the endogenous state when these inputs enter the brain. Prior studies show that endogenous neural states influence stimulus processing through non-specific, global mechanisms, such as spontaneous fluctuations of arousal. It is unclear if endogenous activity influences circuit and stimulus-specific processing and behavior as well. Here we use intracranial recordings from 30 pre-surgical epilepsy patients to show that patterns of endogenous activity are related to the strength of trial-by-trial neural tuning in different visual category-selective neural circuits. The same aspects of the endogenous activity that relate to tuning in a particular neural circuit also correlate to behavioral reaction times only for stimuli from the category that circuit is selective for. These results suggest that endogenous activity can modulate neural tuning and influence behavior in a circuit- and stimulus-specific manner, reflecting a potential mechanism by which endogenous neural states facilitate and bias perception.

Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different

Ongoing internal cortical activity plays a major role in perception and behavior both in animals and humans. Previously we have shown that spontaneous patterns resembling orientation-maps appear over large cortical areas in the primary visual-cortex of anesthetized cats. However, it remains unknown 1) whether spontaneous-activity in the primate also displays similar patterns and 2) whether a significant difference exists between cortical ongoing-activity in the anesthetized and awake primate. We explored these questions by combining voltage-sensitive-dye imaging with multiunit and local-field-potential recordings. Spontaneously emerging orientation and ocular-dominance maps, spanning up to 6 × 6 mm2, were readily observed in anesthetized but not in awake monkeys. Nevertheless, spontaneous correlated-activity involving orientation-domains was observed in awake monkeys. Under both anesthetized and awake conditions, spontaneous correlated-activity coincided with traveling waves. We found that spontaneous activity resembling orientation-maps in awake animals spans smaller cortical areas in each instance, but over time it appears across all of V1. Furthermore, in the awake monkey, our results suggest that the synaptic strength had been completely reorganized including connections between dissimilar elements of the functional architecture. These findings lend support to the notion that ongoing-activity has many more fast switching representations playing an important role in cortical function and behavior.

Representational untangling by the firing rate nonlinearity in V1 simple cells

An important computational goal of the visual system is ‘representational untangling’ (RU): representing increasingly complex features of visual scenes in an easily decodable format. RU is typically assumed to be achieved in high-level visual cortices via several stages of cortical processing. Here we show, using a canonical population coding model, that RU of low-level orientation information is already performed at the first cortical stage of visual processing, but not before that, by a fundamental cellular-level property: the thresholded firing rate nonlinearity of simple cells in the primary visual cortex (V1). We identified specific, experimentally measurable parameters that determined the optimal firing threshold for RU and found that the thresholds of V1 simple cells extracted from in vivo recordings in awake behaving mice were near optimal. These results suggest that information re-formatting, rather than maximisation, may already be a relevant computational goal for the early visual system.

State-aware detection of sensory stimuli in the cortex of the awake mouse

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.

Establishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task, in which an animal is trained to report the presence of a barely perceptible sensory stimulus. In such tasks, both sensory responses in the brain and behavioral responses are highly variable. A simple hypothesis, originating in signal detection theory, is that perceived inputs generate neural activity that cross some threshold for detection. According to this hypothesis, sensory response variability would predict behavioral variability, but previous studies have not born out this prediction. Further complicating the picture, sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability. Here, we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs. This observer has higher overall accuracy than the standard ideal observer. Moreover, because of the adaptation, the observer breaks the direct link between neural and behavioral variability, which could resolve discrepancies arising in past studies. We suggest new experiments to test our theory.

Cortical response states for enhanced sensory discrimination

Brain activity during wakefulness is characterized by rapid fluctuations in neuronal responses. Whether these fluctuations play any role in modulating the accuracy of behavioral responses is poorly understood. Here, we investigated whether and how trial changes in the population response impact sensory coding in monkey V1 and perceptual performance. Although the responses of individual neurons varied widely across trials, many cells tended to covary with the local population. When population activity was in a ‘low’ state, neurons had lower evoked responses and correlated variability, yet higher probability to predict perceptual accuracy. The impact of firing rate fluctuations on network and perceptual accuracy was strongest 200 ms before stimulus presentation, and it greatly diminished when the number of cells used to measure the state of the population was decreased. These findings indicate that enhanced perceptual discrimination occurs when population activity is in a ‘silent’ response mode in which neurons increase information extraction.

Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state

Cortical activity changes continuously during the course of the day. At a global scale, population activity varies between the ‘synchronized’ state during sleep and ‘desynchronized’ state during waking. However, whether local fluctuations in population synchrony during wakefulness modulate the accuracy of sensory encoding and behavioral performance is poorly understood. Here, we show that populations of cells in monkey visual cortex exhibit rapid fluctuations in synchrony ranging from desynchronized responses, indicative of high alertness, to highly synchronized responses. These fluctuations are local and control the trial variability in population coding accuracy and behavioral performance in a discrimination task. When local population activity is desynchronized, the correlated variability between neurons is reduced, and network and behavioral performance are enhanced. These findings demonstrate that the structure of variability in local cortical populations is not noise but rather controls how sensory information is optimally integrated with ongoing processes to guide network coding and behavior.

Changes in synchrony of cortical populations are observed across the sleep-wake cycle, however the effect of fluctuations in synchrony during wakefulness is not understood. Here the authors show that visual cortical neurons have improved sensory encoding accuracy as well as improved perceptual performance during periods of local population desynchrony.

Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in the three-layer turtle cortex

A primary goal of systems neuroscience is to understand cortical function, typically by studying spontaneous and stimulus-modulated cortical activity. Mounting evidence suggests a strong and complex relationship exists between the ongoing and stimulus-modulated cortical state. To date, most work in this area has been based on spiking in populations of neurons. While advantageous in many respects, this approach is limited in scope: it records the activity of a minority of neurons and gives no direct indication of the underlying subthreshold dynamics. Membrane potential recordings can fill these gaps in our understanding, but stable recordings are difficult to obtain in vivo. Here, we recorded subthreshold cortical visual responses in the ex vivo turtle eye-attached whole brain preparation, which is ideally suited for such a study. We found that, in the absence of visual stimulation, the network was "synchronous"; neurons displayed network-mediated transitions between hyperpolarized (Down) and depolarized (Up) membrane potential states. The prevalence of these slow-wave transitions varied across turtles and recording sessions. Visual stimulation evoked similar Up states, which were on average larger and less reliable when the ongoing state was more synchronous. Responses were muted when immediately preceded by large, spontaneous Up states. Evoked spiking was sparse, highly variable across trials, and mediated by concerted synaptic inputs that were, in general, only very weakly correlated with inputs to nearby neurons. Together, these results highlight the multiplexed influence of the cortical network on the spontaneous and sensory-evoked activity of individual cortical neurons.NEW & NOTEWORTHY Most studies of cortical activity focus on spikes. Subthreshold membrane potential recordings can provide complementary insight, but stable recordings are difficult to obtain in vivo. Here, we recorded the membrane potentials of cortical neurons during ongoing and visually evoked activity. We observed a strong relationship between network and single-neuron evoked activity spanning multiple temporal scales. The membrane potential perspective of cortical dynamics thus highlights the influence of intrinsic network properties on visual processing.

Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection

Fundamental to the function of nervous systems is the ability to reorganize to cope with changing sensory input. Although well-studied in single neurons, how such adaptive versatility manifests in the collective population dynamics and function of cerebral cortex remains unknown. Here we measured population neural activity with microelectrode arrays in turtle visual cortex while visually stimulating the retina. First, we found that, following the onset of stimulation, adaptation tunes the collective population dynamics towards a special regime with scale-free spatiotemporal activity, after an initial large-scale transient response. Concurrently, we observed an adaptive tradeoff between two important aspects of population coding-sensory detection and discrimination. As adaptation tuned the cortex toward scale-free dynamics, stimulus discrimination was enhanced, while stimulus detection was reduced. Finally, we used a network-level computational model to show that short-term synaptic depression was sufficient to mechanistically explain our experimental results. In the model, scale-free dynamics emerge only when the model operates near a special regime called criticality. Together our model and experimental results suggest unanticipated functional benefits and costs of adaptation near criticality in visual cortex.