Contextual drive of neuronal responses in mouse V1 in the absence of feedforward input

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.

Layer 1 neocortex: Gating and integrating multidimensional signals

Layer 1 (L1) of the neocortex acts as a nexus for the collection and processing of widespread information. By integrating ascending inputs with extensive top-down activity, this layer likely provides critical information regulating how the perception of sensory inputs is reconciled with expectation. This is accomplished by sorting, directing, and integrating the complex network of excitatory inputs that converge onto L1. These signals are combined with neuromodulatory afferents and gated by the wealth of inhibitory interneurons that either are embedded within L1 or send axons from other cortical layers. Together, these interactions dynamically calibrate information flow throughout the neocortex. This review will primarily focus on L1 within the primary sensory cortex and will use these insights to understand L1 in other cortical areas.

Experience shapes chandelier cell function and structure in the visual cortex

Detailed characterization of interneuron types in primary visual cortex (V1) has greatly contributed to understanding visual perception, yet the role of chandelier cells (ChCs) in visual processing remains poorly characterized. Using viral tracing we found that V1 ChCs predominantly receive monosynaptic input from local layer 5 pyramidal cells and higher-order cortical regions. Two-photon calcium imaging and convolutional neural network modeling revealed that ChCs are visually responsive but weakly selective for stimulus content. In mice running in a virtual tunnel, ChCs respond strongly to events known to elicit arousal, including locomotion and visuomotor mismatch. Repeated exposure of the mice to the virtual tunnel was accompanied by reduced visual responses of ChCs and structural plasticity of ChC boutons and axon initial segment length. Finally, ChCs only weakly inhibited pyramidal cells. These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of their axon initial segments during behaviorally relevant events.

Alpha-frequency feedback to early visual cortex orchestrates coherent naturalistic vision

During naturalistic vision, the brain generates coherent percepts by integrating sensory inputs scattered across the visual field. Here, we asked whether this integration process is mediated by rhythmic cortical feedback. In electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments, we experimentally manipulated integrative processing by changing the spatiotemporal coherence of naturalistic videos presented across visual hemifields. Our EEG data revealed that information about incoherent videos is coded in feedforward-related gamma activity while information about coherent videos is coded in feedback-related alpha activity, indicating that integration is indeed mediated by rhythmic activity. Our fMRI data identified scene-selective cortex and human middle temporal complex (hMT) as likely sources of this feedback. Analytically combining our EEG and fMRI data further revealed that feedback-related representations in the alpha band shape the earliest stages of visual processing in cortex. Together, our findings indicate that the construction of coherent visual experiences relies on cortical feedback rhythms that fully traverse the visual hierarchy.

The representation of occluded image regions in area V1 of monkeys and humans

Neuronal activity in the primary visual cortex (V1) is driven by feedforward input from within the neurons' receptive fields (RFs) and modulated by contextual information in regions surrounding the RF. The effect of contextual information on spiking activity occurs rapidly and is therefore challenging to dissociate from feedforward input. To address this challenge, we recorded the spiking activity of V1 neurons in monkeys viewing either natural scenes or scenes where the information in the RF was occluded, effectively removing the feedforward input. We found that V1 neurons responded rapidly and selectively to occluded scenes. V1 responses elicited by occluded stimuli could be used to decode individual scenes and could be predicted from those elicited by non-occluded images, indicating that there is an overlap between visually driven and contextual responses. We used representational similarity analysis to show that the structure of V1 representations of occluded scenes measured with electrophysiology in monkeys correlates strongly with the representations of the same scenes in humans measured with functional magnetic resonance imaging (fMRI). Our results reveal that contextual influences rapidly alter V1 spiking activity in monkeys over distances of several degrees in the visual field, carry information about individual scenes, and resemble those in human V1. VIDEO ABSTRACT.

What multiplexing means for the interpretation of functional MRI data

Despite technology advances that have enabled routine acquisition of functional MRI data with sub-millimeter resolution, the inferences that cognitive neuroscientists must make to link fMRI data to behavior are complicated. Thus, a single dataset subjected to different analyses can be interpreted in different ways. This article presents two optical analogies that can be useful for framing fMRI analyses in a way that allows for multiple interpretations of fMRI data to be valid simultaneously without undermining each other. The first is reflection: when an object is reflected in a mirrored surface, it appears as if the reflected object is sharing space with the mirrored object, but of course it is not. This analogy can be a good guide for interpreting the fMRI signal, since even at sub-millimeter resolutions the signal is determined by a mixture of local and long-range neural computations. The second is refraction. If we view an object through a multi-faceted prism or gemstone, our view will change-sometimes dramatically-depending on our viewing angle. In the same way, interpretation of fMRI data (inference of underlying neuronal activity) can and should be different depending on the analysis approach. Rather than representing a weakness of the methodology, or the superiority of one approach over the other (for example, simple regression analysis versus multi-voxel pattern analysis), this is an expected consequence of how information is multiplexed in the neural networks of the brain: multiple streams of information are simultaneously present in each location. The fact that any one analysis typically shows only one view of the data also puts some parentheses around fMRI practitioners' constant search for ground truth against which to compare their data. By holding our interpretations lightly and understanding that many interpretations of the data can all be true at the same time, we do a better job of preparing ourselves to appreciate, and eventually understand, the complexity of the brain and the behavior it produces.