Paolo Papale

Cortical ripples are brief high-frequency (80–150 Hz) oscillatory bursts in the extracellular potential, associated with memory and attention. So far, it is unclear whether they are brain-state specific and which neuron types participate in their generation. We report cortical ripples in macaque V1 at rest, present with both eyes open and closed. Using massively parallel Utah array recordings, we identify six single-unit classes and describe their relation to ripple-band oscillations. The classes differ in firing-rate modulation, phase-locking strength, and preferred ripple-band phase. One class of putative deep-layer neurons exhibits non-sinusoidal phase locking, explained by a simple thresholding mechanism in a computational model. During ripples, most units fire irregularly with transient regular bursts. The ripple-associated spiking organisation is largely preserved during natural-image viewing and confirmed in V1 of a blind human. Together, these findings suggest cortical ripples arise from a shared circuit conserved across species and conditions. Graphical

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science^1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses⁴. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1–3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, where for one claim per study, at least five re-analysts independently re-analysed the original data. The statistical appropriateness of the re-analyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent re-analyses yielded the same result (within a tolerance region of +/- 0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator rose to 57%. Regarding the conclusions drawn, 74% of analyses were reported to arrive at the same conclusion as in the original investigation; 24% to no effects/inconclusive result, and 2% to the opposite effect as in the original investigation. This exploratory study suggests that the common single-path analyses in social and behavioural research should not simply be assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

The perception of the external environment relies on a cascade of neural computations in the visual cortex, where stimulus features are represented hierarchically. These features can be extracted through convolutional neural networks (CNNs) from naturalistic stimuli. Neuroimaging studies showed that these are processed along the dorsal and ventral visual streams and linked a gradient of increasing complexity to sequential activations and reorganization of cortical interactions. Spectrally, in the alpha and beta bands, magnetoencephalography revealed that natural viewing induced a reduction in the amplitude of functional connectivity and a maintenance of topography at rest. However, the impact of specific stimulus features on functional connectivity remains unclear. To address this gap, we acquired MEG data on 12 subjects (5 female) during movie watching and used CNN to track the dynamics of movie features. Then, we compared the temporal dynamics of CNN layers with changes in functional connectivity in the alpha and beta bands. Our findings show that feature encoding in functional connectivity relates to a complex temporal, spectral, and topological organization. In both bands, increasing stimulus complexity involved a higher number of connections and longer temporal delays between stimulus and the connectivity dynamics. Moreover, as complexity increased, we observed a shift in the involvement of connector hubs from low-level nodes to high-level ones in the visual network. Finally, a spectral dissociation emerged: the alpha band primarily encoded midlevel features, while the beta band encoded higher-level ones.

Studies in neuroscience inspired progress in the design of artificial neural networks (ANNs), and, vice versa, ANNs provide new insights into the functioning of brain circuits. So far, the focus has been on how ANNs can help to explain the tuning of neurons at various stages of the visual cortical hierarchy. However, the role of modulatory feedback connections, which play a role in attention and perceptual organization, has not yet been resolved. The present study presents a biologically plausible neural network that performs scene segmentation and can shift attention using modulatory feedback connections from higher to lower cortical brain areas. The model replicates several neurophysiological signatures of recurrent processing. Specifically, figural regions elicit more activity in model units than background regions. The modulation of activity by figure and ground occurs at a delay after the first feedforward response, because it depends on a loop through the higher model areas. Importantly, the figural response enhancement is amplified by object-based attention, which stays focused on the figural regions and does not spill over to the adjacent background, just as is observed in the visual cortex. Our results indicate how progress in artificial intelligence can be used to garner insight into the recurrent cortical processing for scene segmentation and object-based attention.

The visual system needs to identify perceptually relevant borders to segment complex natural scenes. The primary visual cortex (V1) is thought to extract local borders, and higher visual areas are thought to identify the perceptually relevant borders between objects and the background. To test this conjecture, we used natural images that had been annotated by human observers who marked the perceptually relevant borders. We assessed the effect of perceptual relevance on V1 responses using human neuroimaging, macaque electrophysiology, and computational modeling. We report that perceptually relevant borders elicit stronger responses in the early visual cortex than irrelevant ones, even if simple features, such as contrast and the energy of oriented filters, are matched. Moreover, V1 neurons discriminate perceptually relevant borders surprisingly fast, during the early feedforward-driven activity at a latency of ~50 ms, indicating that they are tuned to the features that characterize them. We also revealed a delayed, contextual effect that enhances the V1 responses that are elicited by perceptually relevant borders at a longer latency. Our results reveal multiple mechanisms that allow V1 neurons to infer the layout of objects in natural images.

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.

Finding the tuning of visual neurons has kept neuroscientists busy for decades. One approach to this problem has been to test specific hypotheses on the relevance of a visual property (e.g. orientation or color), build a set of “artificial” stimuli that vary along that property and then record neural responses to those stimuli. Here, we present a complementary, data-driven method to retrieve the tuning properties of visual neurons. Exploiting deep generative networks and electrophysiology in monkeys, we first used a method to reconstruct any stimulus from its evoked neuronal activity in the inferotemporal cortex (IT). Then, by arbitrarily perturbing the response of individual cortical sites in the model, we generated naturalistic and interpretable sequences of images that strongly influence neural activity of that site. This method enables the discovery of previously unknown tuning properties of high-level visual neurons that are easily interpretable, which we tested with carefully controlled stimuli. When we knew which images drove the neurons, we activated the cells with electrical microstimulation and observed a predicable shift of the monkey perception in the direction of the preferred image. By allowing the brain to tell us what it cares about, we are no longer limited by our experimental imagination.

SUMMARY: To navigate complex environments, the mammalian visual system prioritizes informative inputs by comparing incoming sensory signals with learned contextual information. While this process is often conceptualized as a difference computation, cortical circuitry poses fundamental challenges: visual contextual feedback is predominantly amplifying, and whether inhibitory circuits support selective subtraction is unknown. Here, we show how layer 2/3 pyramidal neurons in mouse primary visual cortex can implement this comparison through plasticity that acts in opposite directions on responses to direct visual stimulation and contextual signals. To dissociate these components, we presented full images to mice or images where one quadrant was occluded, removing direct visual stimulation while preserving surrounding contextual information, and measured pyramidal cell responses to these stimuli before and after visual familiarization. After familiarization to non-occluded images, responses to direct visual inputs were selectively reduced, whereas contextual responses to occluded images strengthened. This strengthening generalized across natural scenes, allowing contextual inputs to also drive responses to novel occluded images and amplify responses to novel non-occluded images. A computational model reproduced these changes by weakening feedforward inputs and strengthening both feedforward-driven inhibition and contextual feedback in response to repeated pyramidal cell activity. This context-contrasting mechanism produced a conserved structure of responses to occluded and non-occluded images across mice and previously acquired monkey and human data. Together, these results suggest a mechanism for saliency assignment that exploits the compartmental organization of cortical pyramidal neurons: they learn the generalized statistical structure of familiar contexts, allowing selective amplification of improbable inputs or omissions.