Spatial and temporal scales of neuronal correlation in primary visual cortex

Feature selectivity of corticocortical feedback along the primate dorsal visual pathway

Anatomical studies have revealed a prominent role for feedback projections in the primate visual cortex. Theoretical models suggest that these projections support important brain functions such as attention, prediction, and learning. However, these models make different predictions about the relationship between feedback connectivity and neuronal stimulus selectivity. We have therefore performed simultaneous recordings in different regions of the primate dorsal visual pathway. Specifically, we recorded neural activity from the medial superior temporal (MST) area, and one of its main feedback targets, the middle temporal (MT) area. We estimated functional connectivity from correlations in the single-neuron spike trains and performed electrical microstimulation in MST to determine its causal influence on MT. Both methods revealed that inhibitory feedback occurred more commonly when the source and target neurons had very different stimulus preferences. At the same time, the strength of feedback suppression was greater for neurons with similar preferences. Excitatory feedback projections, in contrast, showed no consistent relationship with stimulus preferences. These results suggest that corticocortical feedback could play a role in shaping sensory responses according to behavioral or environmental context.NEW & NOTEWORTHY Here, we show that corticocortical feedback influences are often determined by the selectivity of the individual neurons. A common motif is the occurrence of inhibitory feedback among neurons with very different stimulus preferences. This results in strong suppression of responses in area MT when MST is electrically stimulated. Interestingly, this feedback shows a complex interaction with ongoing visual stimulation, being powerfully suppressive when visual inputs are strong, yet excitatory when visual inputs are weak.

An extensive dataset of spiking activity to reveal the syntax of the ventral stream

Visual neuroscience benefits from high-quality datasets with neuronal responses to many images. Several neuroimaging datasets have been published in recent years, but no comparable dataset with spiking activity exists. Here, we introduce the THINGS ventral stream spiking dataset (TVSD). We extensively sampled neuronal activity in response to >25,000 natural images from the THINGS database in macaques, using high-channel-count implants in three key cortical regions: primary visual cortex (V1), V4, and the inferotemporal cortex. We showcase the utility of TVSD by using an artificial neural network to visualize the tuning of neurons. We also characterize the correlated fluctuations in activity within and between areas and demonstrate that these noise correlations are strongest between neurons with similar tuning. The TVSD allows researchers to answer many questions about neuronal tuning, analyze the interactions within and between cortical regions, and compare spiking activity in monkeys to human neuroimaging data.

Contributions of action potentials to scalp EEG: Theory and biophysical simulations

Differences in the apparent 1/f component of neural power spectra require correction depending on the underlying neural mechanisms, which remain incompletely understood. Past studies suggest that neuronal spiking produces broadband signals and shapes the spectral trend of invasive macroscopic recordings, but it is unclear to what extent action potentials (APs) influence scalp EEG. Here, we combined biophysical simulations with statistical modelling to examine the amplitude and spectral content of scalp potentials generated by the electric fields from spiking activity. In physiological parameter regimes, we found that APs contribute negligibly to the EEG spectral trend. Consistent with this, comparing our biophysical simulations with previously published data from pharmacologically paralyzed subjects suggested that the EEG spectral trend can be explained by a combination of synaptic timescales and electromyogram contamination. We also modelled rhythmic EEG generation, finding that APs can generate detectable narrowband power between approximately 60 and 1000 Hz, reaching frequencies much faster than would be possible from synaptic currents. Finally, we show that different spectral detrending strategies are required for AP generated oscillations compared to synaptically generated oscillations, suggesting that existing detrending methods for EEG spectra need to be modified for high frequency signals.

Luminance invariant encoding in mouse primary visual cortex

The visual system adapts to maintain sensitivity and selectivity over a large range of luminance intensities. One way that the retina maintains sensitivity across night and day is by switching between rod and cone photoreceptors, which alters the receptive fields and interneuronal correlations of retinal ganglion cells (RGCs). While these adaptations allow the retina to transmit visual information to the brain across environmental conditions, the code used for that transmission varies. To determine how downstream targets encode visual scenes across light levels, we measured the effects of luminance adaptation on thalamic and cortical population activity. While changes in the retinal output are evident in the lateral geniculate nucleus (LGN), selectivity in the primary visual cortex (V1) is largely invariant to the changes in luminance. We show that the visual system could maintain sensitivity across environmental conditions without altering cortical selectivity through the convergence of parallel functional pathways from the thalamus to the cortex.

The geometry of correlated variability leads to highly suboptimal discriminative sensory coding

The brain represents the world through the activity of neural populations; however, whether the computational goal of sensory coding is to support discrimination of sensory stimuli or to generate an internal model of the sensory world is unclear. Correlated variability across a neural population (noise correlations) is commonly observed experimentally, and many studies demonstrate that correlated variability improves discriminative sensory coding compared to a null model with no correlations. However, such results do not address whether correlated variability is optimal for discriminative sensory coding. If the computational goal of sensory coding is discriminative, than correlated variability should be optimized to support that goal. We assessed optimality of noise correlations for discriminative sensory coding in diverse datasets by developing two novel null models, each with a biological interpretation. Across datasets, we found that correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Furthermore, biological constraints prevent many subsets of the neural populations from achieving optimality, and subselecting based on biological criteria leaves red discriminative coding performance suboptimal. Finally, we show that optimal subpopulations are exponentially small as the population size grows. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.NEW & NOTEWORTHY The brain represents the world through the activity of neural populations that exhibit correlated variability. We assessed optimality of correlated variability for discriminative sensory coding in diverse datasets by developing two novel null models. Across datasets, correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Biological constraints prevent the neural populations from achieving optimality. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

Auditory Competition and Stimulus Selection across Spatial Locations from Midbrain to Forebrain in Barn Owls

Barn owls enable investigation of neural mechanisms underlying stimulus selection of concurrent stimuli. The audiovisual space map in the optic tectum (OT), avian homolog of the superior colliculus, encodes relative strength of concurrent auditory stimuli through spike response rate and interneuronal spike train synchrony (STS). Open questions remain regarding stimulus selection in downstream forebrain regions lacking topographic coding of auditory space, including the functional consequences of interneuronal STS on interregional signaling. To this end, we presented concurrent stimuli at different locations and manipulated relative strength while simultaneously recording neural responses from OT and its downstream thalamic target, nucleus rotundus (nRt), in awake barn owls of both sexes. Results demonstrated that broadly spatially tuned nRt units exhibit different spike response patterns to competition depending on spatial tuning preferences. Modeling suggests nRt units integrate convergent inputs from distant locations across midbrain map regions. Additionally, STS within nRt reflects the temporal properties of the strongest stimulus. Furthermore, interregional STS between OT and nRt was strongest when spatial tuning overlap between units across regions was large and when the strongest stimulus location during competition was favorable for units in both regions. Additionally, though gamma oscillations synthesized within OT are weakly propagated within nRt, average gamma power across regions correlates with strength of interregional STS. Overall, we demonstrate that nRt integrates inputs across distant areas of OT, retains spatial information through differences in strength of inputs from various locations of the midbrain map across neurons, and prioritizes coding of identity features to the strongest sound.

Intermittent rate coding and cue-specific ensembles support working memory

Persistent, memorandum-specific neuronal spiking activity has long been hypothesized to underlie working memory1,2. However, emerging evidence suggests a potential role for 'activity-silent' synaptic mechanisms3-5. This issue remains controversial because evidence for either view has largely relied either on datasets that fail to capture single-trial population dynamics or on indirect measures of neuronal spiking. We addressed this controversy by examining the dynamics of mnemonic information on single trials obtained from large, local populations of lateral prefrontal neurons recorded simultaneously in monkeys performing a working memory task. Here we show that mnemonic information does not persist in the spiking activity of neuronal populations during memory delays, but instead alternates between coordinated 'On' and 'Off' states. At the level of single neurons, Off states are driven by both a loss of selectivity for memoranda and a return of firing rates to spontaneous levels. Further exploiting the large-scale recordings used here, we show that mnemonic information is available in the patterns of functional connections among neuronal ensembles during Off states. Our results suggest that intermittent periods of memorandum-specific spiking coexist with synaptic mechanisms to support working memory.

Context-dependent orientation discontinuity encoding by gamma rhythms in mouse primary visual cortex

Through the modulation of its surround, an identical visual stimulus can be perceived as more or less salient, allowing it to either stand out or seamlessly integrate with the rest of the visual scene. Gamma rhythms are associated with processing stimulus features across extensive areas of the visual field. Consistent with this concept, the magnitude of visually induced gamma rhythm depends on how well stimulus features aligned both within and outside the classical receptive field (CRF) at the recording site. However, there still exists some uncertainty regarding the encoding of context-modulated orientation discontinuity by gamma rhythms. To address this concern, we conducted extracellular recordings in layers II/III and IV of area V1 using lightly anaesthetized mice to investigate the gamma tuning for stimuli with orientation discontinuity. Our study revealed that gamma rhythms exhibit a preference for stimuli with orientation discontinuity similar to the spiking responses observed in V1, which contradicts the findings of previous studies. Furthermore, the gamma tuning of discontinuous orientations exhibits a moderate correlation with spike tuning and a positive correlation with the strength of surround suppression. Therefore, our study suggests a close association between gamma tuning and nearby spiking tuning; additionally, it highlights the connection between the encoding of visual features by gamma rhythms and functional architecture, as well as neural signal integration. KEY POINTS: Visual context modulates the gamma rhythms in the primary visual cortex. Discontinuous orientation elicits significantly enhanced gamma rhythms compared to the iso-orientation stimulus. The gamma tuning of discontinuous orientations exhibits a moderate correlation with spike tuning. Gamma tuning of orientation discontinuity exhibits a positive correlation with the strength of surround suppression.

Dissociation of Attentional State and Behavioral Outcome Using Local Field Potentials

Successful behavior depends on the attentional state and other factors related to decision-making, which may modulate neuronal activity differently. Here, we investigated whether attentional state and behavioral outcome (i.e., whether a target is detected or missed) are distinguishable using the power and phase of local field potential recorded bilaterally from area V4 of two male rhesus monkeys performing a cued visual attention task. To link each trial's outcome to pairwise measures of attention that are typically averaged across trials, we used several methods to obtain single-trial estimates of spike count correlation and phase consistency. Surprisingly, while attentional location was best discriminated using gamma and high-gamma power, behavioral outcome was best discriminated by alpha power and steady-state visually evoked potential. Power outperformed absolute phase in attentional/behavioral discriminability, although single-trial gamma phase consistency provided reasonably high attentional discriminability. Our results suggest a dissociation between the neuronal mechanisms that regulate attentional focus and behavioral outcome.

Border-ownership tuning determines the connectivity between V4 and V1 in the macaque visual system

Cortical feedback connections are extremely numerous but the logic of connectivity between higher and lower areas remains poorly understood. Feedback from higher visual areas to primary visual cortex (V1) has been shown to enhance responses on perceptual figures compared to backgrounds, an effect known as figure-background modulation (FBM). A likely source of this feedback are border-ownership (BO) selective cells in mid-tier visual areas (e.g. V4) which represent the location of figures. We examined the connectivity between V4 cells and V1 cells using noise-correlations and micro-stimulation to estimate connectivity strength. We show that connectivity is consistent with a model in which BO-tuned V4 cells send positive feedback in the direction of their preferred figure and negative feedback in the opposite direction. This connectivity scheme can recreate patterns of FBM observed in previous studies. These results provide insights into the cortical connectivity underlying figure-background perception and establish a link between FBM and BO-tuning.

The representational geometry for naturalistic textures in macaque V1 and V2

Our understanding of visual cortical processing has relied primarily on studying the selectivity of individual neurons in different areas. A complementary approach is to study how the representational geometry of neuronal populations differs across areas. Though the geometry is derived from individual neuronal selectivity, it can reveal encoding strategies difficult to infer from single neuron responses. In addition, recent theoretical work has begun to relate distinct functional objectives to different representational geometries. To understand how the representational geometry changes across stages of processing, we measured neuronal population responses in primary visual cortex (V1) and area V2 of macaque monkeys to an ensemble of synthetic, naturalistic textures. Responses were lower dimensional in V2 than V1, and there was a better alignment of V2 population responses to different textures. The representational geometry in V2 afforded better discriminability between out-of-sample textures. We performed complementary analyses of standard convolutional network models, which did not replicate the representational geometry of cortex. We conclude that there is a shift in the representational geometry between V1 and V2, with the V2 representation exhibiting features of a low-dimensional, systematic encoding of different textures and of different instantiations of each texture. Our results suggest that comparisons of representational geometry can reveal important transformations that occur across successive stages of visual processing.

Synchrony dynamics underlie irregular neocortical spiking

Cortical neurons are characterized by their variable spiking patterns. We challenge prevalent theories for the origin of spiking variability. We examine the specific hypothesis that cortical synchrony drives spiking variability in vivo . Using dynamic clamp, we demonstrate that intrinsic neuronal properties do not contribute substantially to spiking variability, but rather spiking variability emerges from weakly synchronous network drive. With large-scale electrophysiology we quantify the degree of synchrony and its time scale in cortical networks in vivo . We demonstrate that physiological levels of synchrony are sufficient to generate irregular responses found in vivo . Further, this synchrony shifts over timescales ranging from 25 to 200 ms, depending on the presence of external sensory input. Such shifts occur when the network moves from spontaneous to driven modes, leading naturally to a decline in response variability as observed across cortical areas. Finally, while individual neurons exhibit reliable responses to physiological drive, different neurons respond in a distinct fashion according to their intrinsic properties, contributing to stable synchrony across the neural network.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow

In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. Using Neuropixels probes, we measured border ownership modulation within large, local populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that cross-correlations between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, independent of the comparison with CRF stimulation, we observed that the magnitude of border ownership modulation increased with the proportion of information flow from feedback/horizontal layers to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

Horizontal cortical connections shape intrinsic traveling waves into feature-selective motifs that regulate perceptual sensitivity

Intrinsic cortical activity forms traveling waves that modulate sensory-evoked responses and perceptual sensitivity. These intrinsic traveling waves (iTWs) may arise from the coordination of synaptic activity through long-range feature-dependent horizontal connectivity within cortical areas. In a spiking network model that incorporates feature-selective patchy connections, we observe iTW motifs that result from shifts in excitatory/inhibitory balance as action potentials traverse these patchy connections. To test whether feature-selective motifs occur in vivo, we examined data recorded in the middle temporal visual area (Area MT) of marmosets performing a visual detection task. We find that some iTWs form motifs that are feature selective, exhibiting direction-selective modulations in spiking activity. Further, motifs modulate the gain of target-evoked responses and perceptual sensitivity if the target matches the preference of the motif. These results suggest that iTWs are shaped by the patchy horizontal fiber projections in the cortex and can regulate neural and perceptual sensitivity in a feature-selective manner.

Neurons of Macaque Frontal Eye Field Signal Reward-Related Surprise

The frontal eye field (FEF) plays a well-established role in the control of visual attention. The strength of an FEF neuron's response to a visual stimulus presented in its receptive field is enhanced if the stimulus captures spatial attention by virtue of its salience. A stimulus can be rendered salient by cognitive factors as well as by physical attributes. These include surprise. The aim of the present experiment was to determine whether surprise-induced salience would result in enhanced visual-response strength in the FEF. Toward this end, we monitored neuronal activity in two male monkeys while presenting first a visual cue predicting with high probability that the reward delivered at the end of the trial would be good or bad (large or small) and then a visual cue announcing the size of the impending reward with certainty. The second cue usually confirmed but occasionally violated the expectation set up by the first cue. Neurons responded more strongly to the second cue when it violated than when it confirmed expectation. The increase in the firing rate was accompanied by a decrease in spike-count correlation as expected from capture of attention. Although both good surprise and bad surprise induced enhanced firing, the effects appeared to arise from distinct mechanisms as indicated by the fact that the bad-surprise signal appeared at a longer latency than the good-surprise signal and by the fact that the strength of the two signals varied independently across neurons.

The tilt illusion arises from an efficient reallocation of neural coding resources at the contextual boundary

The tilt illusion - a bias in the perceived orientation of a center stimulus induced by an oriented surround - illustrates how context shapes visual perception. While the tilt illusion has been the subject of quantitative study for over 85 years, we still lack a comprehensive account of the phenomenon that connects its neural and behavioral characteristics. Here, we demonstrate that the tilt illusion originates from a dynamic change in neural coding precision induced by the surround context. We simultaneously obtained psychophysical and fMRI responses from human subjects while they viewed gratings in the absence and presence of an oriented surround, and extracted sensory encoding precision from their behavioral and neural data. Both measures show that in the absence of a surround, encoding reflects the natural scene statistics of orientation. However, in the presence of an oriented surround, encoding precision is significantly increased for stimuli similar to the surround orientation. This local change in encoding is sufficient to accurately predict the behavioral characteristics of the tilt illusion using a Bayesian observer model. The effect of surround modulation increases along the ventral stream, and is localized to the portion of the visual cortex with receptive fields at the center-surround boundary. The pattern of change in coding accuracy reflects the surround-conditioned orientation statistics in natural scenes, but cannot be explained by local stimulus configuration. Our results suggest that the tilt illusion naturally emerges from a dynamic coding strategy that efficiently reallocates neural coding resources based on the current stimulus context.

Automated customization of large-scale spiking network models to neuronal population activity

Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity's dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function.

Correlated variability and its attentional modulation depend on anatomical connectivity

Visual cortical neurons show variability in their responses to repeated presentations of a stimulus and a portion of this variability is shared across neurons. Attention may enhance visual perception by reducing shared spiking variability. However, shared variability and its attentional modulation are not consistent within or across cortical areas, and depend on additional factors such as neuronal type. A critical factor that has not been tested is actual anatomical connectivity. We measured spike count correlations among pairs of simultaneously recorded neurons in the primary visual cortex (V1) for which anatomical connectivity was inferred from spiking cross-correlations. Neurons were recorded in monkeys performing a contrast-change discrimination task requiring covert shifts in visual spatial attention. Accordingly, spike count correlations were compared across trials in which attention was directed toward or away from the visual stimulus overlapping recorded neuronal receptive fields. Consistent with prior findings, attention did not significantly alter spike count correlations among random pairings of unconnected V1 neurons. However, V1 neurons connected via excitatory synapses showed a significant reduction in spike count correlations with attention. Interestingly, V1 neurons connected via inhibitory synapses demonstrated high spike count correlations overall that were not modulated by attention. Correlated variability in excitatory circuits also depended upon neuronal tuning for contrast, the task-relevant stimulus feature. These results indicate that shared variability depends on the type of connectivity in neuronal circuits. Also, attention significantly reduces shared variability in excitatory circuits, even when attention effects on randomly sampled neurons within the same area are weak.

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

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability

Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models and provide a parsimonious explanation for the stimulus dependence of correlated variability within and between visual areas.

Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex

Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.

Dynamic gating of perceptual flexibility by non-classically responsive cortical neurons

The ability to flexibly respond to sensory cues in dynamic environments is essential to adaptive auditory-guided behaviors. Cortical spiking responses during behavior are highly diverse, ranging from reliable trial-averaged responses to seemingly random firing patterns. While the reliable responses of 'classically responsive' cells have been extensively studied for decades, the contribution of irregular spiking 'non-classically responsive' cells to behavior has remained underexplored despite their prevalence. Here, we show that flexible auditory behavior results from interactions between local auditory cortical circuits comprised of heterogeneous responses and inputs from secondary motor cortex. Strikingly, non-classically responsive neurons in auditory cortex were preferentially recruited during learning, specifically during rapid learning phases when the greatest gains in behavioral performance occur. Population-level decoding revealed that during rapid learning mixed ensembles comprised of both classically and non-classically responsive cells encode significantly more task information than homogenous ensembles of either type and emerge as a functional unit critical for learning. Optogenetically silencing inputs from secondary motor cortex selectively modulated non-classically responsive cells in the auditory cortex and impaired reversal learning by preventing the remapping of a previously learned stimulus-reward association. Top-down inputs orchestrated highly correlated non-classically responsive ensembles in sensory cortex providing a unique task-relevant manifold for learning. Thus, non-classically responsive cells in sensory cortex are preferentially recruited by top-down inputs to enable neural and behavioral flexibility.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

EEG-estimated functional connectivity, and not behavior, differentiates Parkinson's patients from health controls during the Simon conflict task

Neural biomarkers that can classify or predict disease are of broad interest to the neurological and psychiatric communities. Such biomarkers can be informative of disease state or treatment efficacy, even before there are changes in symptoms and/or behavior. This work investigates EEG-estimated functional connectivity (FC) as a Parkinson's Disease (PD) biomarker. Specifically, we investigate FC mediated via neural oscillations and consider such activity during the Simons conflict task. This task yields sensory-motor conflict, and one might expect differences in behavior between PD patients and healthy controls (HCs). In addition to considering spatially focused approaches, such as FC, as a biomarker, we also consider temporal biomarkers, which are more sensitive to ongoing changes in neural activity. We find that FC, estimated from delta (1-4Hz) and theta (4-7Hz) oscillations, yields spatial FC patterns significantly better at distinguishing PD from HC than temporal features or behavior. This study reinforces that FC in spectral bands is informative of differences in brain-wide processes and can serve as a biomarker distinguishing normal brain function from that seen in disease.

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

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2 , reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2 , even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Eigenvalue spectra of finely structured random matrices

Random matrix theory allows for the deduction of stability criteria for complex systems using only a summary knowledge of the statistics of the interactions between components. As such, results like the well-known elliptical law are applicable in a myriad of different contexts. However, it is often assumed that all components of the complex system in question are statistically equivalent, which is unrealistic in many applications. Here we introduce the concept of a finely structured random matrix. These are random matrices with element-specific statistics, which can be used to model systems in which the individual components are statistically distinct. By supposing that the degree of "fine structure" in the matrix is small, we arrive at a succinct "modified" elliptical law. We demonstrate the direct applicability of our results to the niche and cascade models in theoretical ecology, as well as a model of a neural network, and a directed network with arbitrary degree distribution. The simple closed form of our central results allow us to draw broad qualitative conclusions about the effect of fine structure on stability.

Auditory Competition and Coding of Relative Stimulus Strength across Midbrain Space Maps of Barn Owls

The natural environment challenges the brain to prioritize the processing of salient stimuli. The barn owl, a sound localization specialist, exhibits a circuit called the midbrain stimulus selection network, dedicated to representing locations of the most salient stimulus in circumstances of concurrent stimuli. Previous competition studies using unimodal (visual) and bimodal (visual and auditory) stimuli have shown that relative strength is encoded in spike response rates. However, open questions remain concerning auditory-auditory competition on coding. To this end, we present diverse auditory competitors (concurrent flat noise and amplitude-modulated noise) and record neural responses of awake barn owls of both sexes in subsequent midbrain space maps, the external nucleus of the inferior colliculus (ICx) and optic tectum (OT). While both ICx and OT exhibit a topographic map of auditory space, OT also integrates visual input and is part of the global-inhibitory midbrain stimulus selection network. Through comparative investigation of these regions, we show that while increasing strength of a competitor sound decreases spike response rates of spatially distant neurons in both regions, relative strength determines spike train synchrony of nearby units only in the OT. Furthermore, changes in synchrony by sound competition in the OT are correlated to gamma range oscillations of local field potentials associated with input from the midbrain stimulus selection network. The results of this investigation suggest that modulations in spiking synchrony between units by gamma oscillations are an emergent coding scheme representing relative strength of concurrent stimuli, which may have relevant implications for downstream readout.

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Calibrating Bayesian Decoders of Neural Spiking Activity

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.

Nonlinear manifolds underlie neural population activity during behaviour

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.

Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks

Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, such as those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural covariance in neural networks with dropout has not been studied, and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phases. We found that trial-by-trial correlation between neurons (i.e., noise correlation) is positive and low dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. This evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked aspect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.

tRNS boosts visual perceptual learning in participants with bilateral macular degeneration

Perceptual learning (PL) has shown promise in enhancing residual visual functions in patients with age-related macular degeneration (MD), however it requires prolonged training and evidence of generalization to untrained visual functions is limited. Recent studies suggest that combining transcranial random noise stimulation (tRNS) with perceptual learning produces faster and larger visual improvements in participants with normal vision. Thus, this approach might hold the key to improve PL effects in MD. To test this, we trained two groups of MD participants on a contrast detection task with (n = 5) or without (n = 7) concomitant occipital tRNS. The training consisted of a lateral masking paradigm in which the participant had to detect a central low contrast Gabor target. Transfer tasks, including contrast sensitivity, near and far visual acuity, and visual crowding, were measured at pre-, mid and post-tests. Combining tRNS and perceptual learning led to greater improvements in the trained task, evidenced by a larger increment in contrast sensitivity and reduced inhibition at the shortest target to flankers' distance. The overall amount of transfer was similar between the two groups. These results suggest that coupling tRNS and perceptual learning has promising potential applications as a clinical rehabilitation strategy to improve vision in MD patients.

Horizontal cortical connections shape intrinsic traveling waves into feature-selective motifs that regulate perceptual sensitivity

Intrinsic, ongoing fluctuations of cortical activity form traveling waves that modulate the gain of sensory-evoked responses and perceptual sensitivity. Several lines of evidence suggest that intrinsic traveling waves (iTWs) may arise, in part, from the coordination of synaptic activity through the recurrent horizontal connectivity within cortical areas, which include long range patchy connections that link neurons with shared feature preferences. In a spiking network model with anatomical topology that incorporates feature-selective patchy connections, which we call the Balanced Patchy Network (BPN), we observe repeated iTWs, which we refer to as motifs. In the model, motifs stem from fluctuations in the excitability of like-tuned neurons that result from shifts in E/I balance as action potentials traverse these patchy connections. To test if feature-selective motifs occur in vivo, we examined data previously recorded using multielectrode arrays in Area MT of marmosets trained to perform a threshold visual detection task. Using a newly developed method for comparing the similarity of wave patterns we found that some iTWs can be grouped into motifs. As predicted by the BPN, many of these motifs are feature selective, exhibiting direction-selective modulations in ongoing spiking activity. Further, motifs modulate the gain of the response evoked by a target and perceptual sensitivity to the target if the target matches the preference of the motif. These results provide evidence that iTWs are shaped by the patterns of horizontal fiber projections in the cortex and that patchy connections enable iTWs to regulate neural and perceptual sensitivity in a feature selective manner.

Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.

Compact deep neural network models of visual cortex

A powerful approach to understanding the computations carried out in visual cortex is to develop models that predict neural responses to arbitrary images. Deep neural network (DNN) models have worked remarkably well at predicting neural responses [1, 2, 3], yet their underlying computations remain buried in millions of parameters. Have we simply replaced one complicated system in vivo with another in silico? Here, we train a data-driven deep ensemble model that predicts macaque V4 responses ~50% more accurately than currently-used task-driven DNN models. We then compress this deep ensemble to identify compact models that have 5,000x fewer parameters yet equivalent accuracy as the deep ensemble. We verified that the stimulus preferences of the compact models matched those of the real V4 neurons by measuring V4 responses to both 'maximizing' and adversarial images generated using compact models. We then analyzed the inner workings of the compact models and discovered a common circuit motif: Compact models share a similar set of filters in early stages of processing but then specialize by heavily consolidating this shared representation with a precise readout. This suggests that a V4 neuron's stimulus preference is determined entirely by its consolidation step. To demonstrate this, we investigated the compression step of a dot-detecting compact model and found a set of simple computations that may be carried out by dot-selective V4 neurons. Overall, our work demonstrates that the DNN models currently used in computational neuroscience are needlessly large; our approach provides a new way forward for obtaining explainable, high-accuracy models of visual cortical neurons.

A thalamocortical substrate for integrated information via critical synchronous bursting

Understanding the neurobiological mechanisms underlying consciousness remains a significant challenge. Recent evidence suggests that the coupling between distal-apical and basal-somatic dendrites in thick-tufted layer 5 pyramidal neurons (L5PN), regulated by the nonspecific-projecting thalamus, is crucial for consciousness. Yet, it is uncertain whether this thalamocortical mechanism can support emergent signatures of consciousness, such as integrated information. To address this question, we constructed a biophysical network of dual-compartment thick-tufted L5PN, with dendrosomatic coupling controlled by thalamic inputs. Our findings demonstrate that integrated information is maximized when nonspecific thalamic inputs drive the system into a regime of time-varying synchronous bursting. Here, the system exhibits variable spiking dynamics with broad pairwise correlations, supporting the enhanced integrated information. Further, the observed peak in integrated information aligns with criticality signatures and empirically observed layer 5 pyramidal bursting rates. These results suggest that the thalamocortical core of the mammalian brain may be evolutionarily configured to optimize effective information processing, providing a potential neuronal mechanism that integrates microscale theories with macroscale signatures of consciousness.

Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo

To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.

An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain

Single-unit (SU) recording in nonhuman primates (NHPs) is indispensible in the quest of how the brain works, yet electrodes currently used for the NHP brain are limited in signal longevity, stability, and spatial coverage. Using new structural materials, microfabrication, and penetration techniques, we develop a mechanically robust ultraflexible, 1 µm thin electrode array (MERF) that enables pial penetration and high-density, large-scale, and chronic recording of neurons along both vertical and horizontal cortical axes in the nonhuman primate brain. Recording from three monkeys yields 2,913 SUs from 1,065 functional recording channels (up to 240 days), with some SUs tracked for up to 2 months. Recording from the primary visual cortex (V1) reveals that neurons with similar orientation preferences for visual stimuli exhibited higher spike correlation. Furthermore, simultaneously recorded neurons in different cortical layers of the primary motor cortex (M1) show preferential firing for hand movements of different directions. Finally, it is shown that a linear decoder trained with neuronal spiking activity across M1 layers during monkey's hand movements can be used to achieve on-line control of cursor movement. Thus, the MERF electrode array offers a new tool for basic neuroscience studies and brain-machine interface (BMI) applications in the primate brain.

Routing states transition during oscillatory bursts and attentional selection

Brain-wide information routing relies on the spatio-temporal dynamics of neural activity, but it remains unclear how routing states emerge at fast spiking timescales and relate to slower activity dynamics during cognitive processes. Here, we show that localized spiking events participate in directional routing states with spiking activity in distant brain areas that dynamically switch or amplify states during oscillatory bursts, attentional selection, and decision-making. Modeling and neural recordings from lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), and striatum of nonhuman primates revealed that cross-regional routing states arise within 20 ms following individual neuron spikes, with LPFC spikes leading the activity in ACC and striatum. The baseline routing state amplified during LPFC beta bursts between LPFC and striatum and switched direction during ACC theta/alpha bursts between ACC and LPFC. Selective attention amplified theta-/alpha-band-specific lead ensembles in ACC, while decision-making increased the lead of ACC and LPFC spikes to the striatum.

Retinal input integration in excitatory and inhibitory neurons in the mouse superior colliculus in vivo

The superior colliculus (SC) is a midbrain structure that receives inputs from retinal ganglion cells (RGCs). The SC contains one of the highest densities of inhibitory neurons in the brain but whether excitatory and inhibitory SC neurons differentially integrate retinal activity in vivo is still largely unknown. We recently established a recording approach to measure the activity of RGCs simultaneously with their postsynaptic SC targets in vivo, to study how SC neurons integrate RGC activity. Here, we employ this method to investigate the functional properties that govern retinocollicular signaling in a cell type-specific manner by identifying GABAergic SC neurons using optotagging in VGAT-ChR2 mice. Our results demonstrate that both excitatory and inhibitory SC neurons receive comparably strong RGC inputs and similar wiring rules apply for RGCs innervation of both SC cell types, unlike the cell type-specific connectivity in the thalamocortical system. Moreover, retinal activity contributed more to the spiking activity of postsynaptic excitatory compared to inhibitory SC neurons. This study deepens our understanding of cell type-specific retinocollicular functional connectivity and emphasizes that the two major brain areas for visual processing, the visual cortex and the SC, differently integrate sensory afferent inputs.

Identification of interacting neural populations: methods and statistical considerations

As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.

Propagation of activity through the cortical hierarchy and perception are determined by neural variability

Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions-the primary and secondary somatosensory cortex (S1 and S2)-in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.

Rate versus synchrony codes for cerebellar control of motor behavior

Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the code for information transmission from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellum of rhesus macaques reveal how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and insufficient to impact the firing of downstream vestibular nucleus neurons. Analysis of previous metrics that purported to reveal Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate rather than a synchrony code to drive the activity of downstream neurons and thus control motor behavior.

Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling

In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.

Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Correlated variability in primate superior colliculus depends on functional class

Correlated variability in neuronal activity (spike count correlations, r_SC) can constrain how information is read out from populations of neurons. Traditionally, r_SC is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of r_SC that are not captured by the population r_SC. We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of r_SC. "Delay class" neurons displayed the highest r_SC, especially during saccades that relied on working memory. Such dependence of r_SC on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.

Unsupervised approach to decomposing neural tuning variability

Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture the moment-to-moment tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientifically essential- we discovered a simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of unexpected structure of the neural code, capturing the influence of the external stimulus drive and internal states simultaneously.

Recurrent network interactions explain tectal response variability and experience-dependent behavior

Response variability is an essential and universal feature of sensory processing and behavior. It arises from fluctuations in the internal state of the brain, which modulate how sensory information is represented and transformed to guide behavioral actions. In part, brain state is shaped by recent network activity, fed back through recurrent connections to modulate neuronal excitability. However, the degree to which these interactions influence response variability and the spatial and temporal scales across which they operate, are poorly understood. Here, we combined population recordings and modeling to gain insights into how neuronal activity modulates network state and thereby impacts visually evoked activity and behavior. First, we performed cellular-resolution calcium imaging of the optic tectum to monitor ongoing activity, the pattern of which is both a cause and consequence of changes in network state. We developed a minimal network model incorporating fast, short range, recurrent excitation and long-lasting, activity-dependent suppression that reproduced a hallmark property of tectal activity - intermittent bursting. We next used the model to estimate the excitability state of tectal neurons based on recent activity history and found that this explained a portion of the trial-to-trial variability in visually evoked responses, as well as spatially selective response adaptation. Moreover, these dynamics also predicted behavioral trends such as selective habituation of visually evoked prey-catching. Overall, we demonstrate that a simple recurrent interaction motif can be used to estimate the effect of activity upon the incidental state of a neural network and account for experience-dependent effects on sensory encoding and visually guided behavior.

Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling

The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.