Search

A new study leveraging advances in high-field fMRI provides evidence that superficial cortical layers in humans play a crucial role in signaling prediction errors, a finding that is consistent with the predictive processing framework. [ABSTRACT FROM AUTHOR]

This perspective describes predictive processing as a computational framework for understanding cortical function in the context of emerging evidence, with a focus on sensory processing. We discuss how the predictive processing framework may be implemented at the level of cortical circuits and how its implementation could be falsified experimentally. Lastly, we summarize the general implications of predictive processing on cortical function in healthy and diseased states. In this perspective, Keller and Mrsic-Flogel describe the advantages of predictive processing as a computational framework for understanding cortical function in the context of emerging evidence with a focus on sensory processing. [ABSTRACT FROM AUTHOR]

Summary In primary visual cortex, a subset of neurons responds when a particular stimulus is encountered in a certain location in visual space. This activity can be modeled using a visual receptive field. In addition to visually driven activity, there are neurons in visual cortex that integrate visual and motor-related input to signal a mismatch between actual and predicted visual flow. Here we show that these mismatch neurons have receptive fields and signal a local mismatch between actual and predicted visual flow in restricted regions of visual space. These mismatch receptive fields are aligned to the retinotopic map of visual cortex and are similar in size to visual receptive fields. Thus, neurons with mismatch receptive fields signal local deviations of actual visual flow from visual flow predicted based on self-motion and could therefore underlie the detection of objects moving relative to the visual flow caused by self-motion. Video Abstract [ABSTRACT FROM AUTHOR]

Summary Neurodegenerative processes in Alzheimer’s disease (AD) affect the structure and function of neurons [ 1–4 ], resulting in altered neuronal activity patterns comprising neuronal hypo- and hyperactivity [ 5, 6 ] and causing the disruption of long-range projections [ 7, 8 ]. Impaired information processing between functionally connected brain areas is evident in defective visuomotor integration, an early sign of the disease [ 9–11 ]. The cellular and neuronal circuit mechanisms underlying this disruption of information processing in AD, however, remain elusive. Recent studies in mice suggest that visuomotor integration already occurs in primary visual cortex (V1), as it not only processes sensory input but also exhibits strong motor-related activity, likely driven by neuromodulatory or excitatory inputs [ 12–17 ]. Here, we probed the integration of visual—and motor-related—inputs in V1 of behaving APP/PS1 [ 18 ] mice, a well-characterized mouse model of AD, using two-photon calcium imaging. We find that sensorimotor signals in APP/PS1 mice are differentially affected: while visually driven and motor-related signals are strongly reduced, neuronal responses signaling a mismatch between expected and actual visual flow are selectively spared. We furthermore observe an increase in aberrant activity during quiescent states in APP/PS1 mice. Jointly, the reduction in running-correlated activity and the enhanced aberrant activity degrade the coding accuracy of the network, indicating that the impairment of visuomotor integration in AD is already taking place at early stages of visual processing. [ABSTRACT FROM AUTHOR]

Summary: Homeostatic plasticity is important to maintain a set level of activity in neuronal circuits and has been most extensively studied in cell cultures following activity blockade. It is still unclear, however, whether activity changes associated with mechanisms of homeostatic plasticity occur in vivo, for example after changes in sensory input. Here, we show that activity levels in the visual cortex are significantly decreased after sensory deprivation by retinal lesions, followed by a gradual increase in activity levels in the 48 hr after deprivation. These activity changes are associated with synaptic scaling, manifested in vitro by an increase in mEPSC amplitude and in vivo by an increase in spine size. Together, these data show that homeostatic activity changes occur in vivo in parallel with synaptic scaling. [Copyright &y& Elsevier]

Summary: Studies in anesthetized animals have suggested that activity in early visual cortex is mainly driven by visual input and is well described by a feedforward processing hierarchy. However, evidence from experiments on awake animals has shown that both eye movements and behavioral state can strongly modulate responses of neurons in visual cortex; the functional significance of this modulation, however, remains elusive. Using visual-flow feedback manipulations during locomotion in a virtual reality environment, we found that responses in layer 2/3 of mouse primary visual cortex are strongly driven by locomotion and by mismatch between actual and expected visual feedback. These data suggest that processing in visual cortex may be based on predictive coding strategies that use motor-related and visual input to detect mismatches between predicted and actual visual feedback. [ABSTRACT FROM AUTHOR]

Processing in cortical circuits is driven by combinations of cortical and subcortical inputs. These inputs are often conceptually categorized as bottom-up, conveying sensory information, and top-down, conveying contextual information. Using intracellular recordings in mouse primary visual cortex, we measured neuronal responses to visual input, locomotion, and visuomotor mismatches. We show that layer 2/3 (L2/3) neurons compute a difference between top-down motor-related input and bottom-up visual flow input. Most L2/3 neurons responded to visuomotor mismatch with either hyperpolarization or depolarization, and the size of this response was correlated with distinct physiological properties. Consistent with a subtraction of bottom-up and top-down input, visual and motor-related inputs had opposing influence on L2/3 neurons. In infragranular neurons, we found no evidence of a difference computation and responses were consistent with positive integration of visuomotor inputs. Our results provide evidence that L2/3 functions as a bidirectional comparator of top-down and bottom-up input. • Layer 2/3 neurons show widespread subthreshold mismatch responses • Mismatch response sign is predicted by visual flow and locomotion-related responses • Layer 5/6 has a scarcity of depolarizing mismatch responses • Visual flow and locomotion speed have opposing signs of influence only in layer 2/3 Jordan and Keller use whole cell recordings in mice navigating in virtual reality to show that neurons only in superficial cortical layers have a special property: they integrate visual flow and locomotion speed with opposing signs, allowing them to compute bidirectional mismatches between actual and expected visual flow speeds. [ABSTRACT FROM AUTHOR]

Predictive processing postulates the existence of prediction error neurons in cortex. Neurons with both negative and positive prediction error response properties have been identified in layer 2/3 of visual cortex, but whether they correspond to transcriptionally defined subpopulations is unclear. Here we used the activity-dependent, photoconvertible marker CaMPARI2 to tag neurons in layer 2/3 of mouse visual cortex during stimuli and behaviors designed to evoke prediction errors. We performed single-cell RNA-sequencing on these populations and found that previously annotated Adamts2 and Rrad layer 2/3 transcriptional cell types were enriched when photolabeling during stimuli that drive negative or positive prediction error responses, respectively. Finally, we validated these results functionally by designing artificial promoters for use in AAV vectors to express genetically encoded calcium indicators. Thus, transcriptionally distinct cell types in layer 2/3 that can be targeted using AAV vectors exhibit distinguishable negative and positive prediction error responses. • In vivo functional tagging identifies molecularly defined cell types in cortex • Adamts2 and Baz1a cell types have distinct prediction error response profiles • We designed artificial AAV vectors to target these prediction error types O'Toole et al. investigated the relationship between transcriptional identity and computational functions in layer 2/3 of mouse visual cortex. Through a combination of in vivo functional labeling, single-cell RNA-sequencing, and cell type-specific AAVs, they demonstrate that neurons signaling unexpected stimuli are transcriptionally distinct. [ABSTRACT FROM AUTHOR]

Summary Motor cortex (M1) lesions result in motor impairments, yet how M1 contributes to the control of movement remains controversial. To investigate the role of M1 in sensory guided motor coordination, we trained mice to navigate a virtual corridor using a spherical treadmill. This task required directional adjustments through spontaneous turning, while unexpected visual offset perturbations prompted induced turning. We found that M1 is essential for execution and learning of this visually guided task. Turn-selective layer 2/3 and layer 5 pyramidal tract (PT) neuron activation was shaped differentially with learning but scaled linearly with turn acceleration during spontaneous turns. During induced turns, however, layer 2/3 neurons were activated independent of behavioral response, while PT neurons still encoded behavioral response magnitude. Our results are consistent with a role of M1 in the detection of sensory perturbations that result in deviations from intended motor state and the initiation of an appropriate corrective response. Highlights • Motor cortex (M1) is necessary for the motor response to an unexpected perturbation • M1 is not necessary when the same movement is carried out spontaneously • Layer 2/3 neurons are differentially activated by unexpected visual perturbations • Activity in layer 5 PT neurons correlates with behavioral responses The role of motor cortex in movement control is controversial. Heindorf et al. demonstrate that motor cortex mediates corrective behavioral responses to unexpected visual perturbations, paralleled by layer-specific cortical responses distinct from the ones during the same movement without perturbation. [ABSTRACT FROM AUTHOR]

Summary The emergence of sensory-guided behavior depends on sensorimotor coupling during development. How sensorimotor experience shapes neural processing is unclear. Here, we show that the coupling between motor output and visual feedback is necessary for the functional development of visual processing in layer 2/3 (L2/3) of primary visual cortex (V1) of the mouse. Using a virtual reality system, we reared mice in conditions of normal or random visuomotor coupling. We recorded the activity of identified excitatory and inhibitory L2/3 neurons in response to transient visuomotor mismatches in both groups of mice. Mismatch responses in excitatory neurons were strongly experience dependent and driven by a transient release from inhibition mediated by somatostatin-positive interneurons. These data are consistent with a model in which L2/3 of V1 computes a difference between an inhibitory visual input and an excitatory locomotion-related input, where the balance between these two inputs is finely tuned by visuomotor experience. [ABSTRACT FROM AUTHOR]

Summary We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli. [ABSTRACT FROM AUTHOR]

Summary Homeostatic regulation has been shown to restore cortical activity in vivo following sensory deprivation, but it is unclear whether this recovery is uniform across all cells or specific to a subset of the network. To address this issue, we used chronic calcium imaging in behaving adult mice to examine the activity of individual excitatory and inhibitory neurons in the same region of the layer 2/3 monocular visual cortex following enucleation. We found that only a fraction of excitatory neurons homeostatically recover activity after deprivation and inhibitory neurons show no recovery. Prior to deprivation, excitatory cells that did recover were more likely to have significantly correlated activity with other recovering excitatory neurons, thus forming a subnetwork of recovering neurons. These network level changes are accompanied by a reduction in synaptic inhibition onto all excitatory neurons, suggesting that both synaptic mechanisms and subnetwork activity are important for homeostatic recovery of activity after deprivation. [ABSTRACT FROM AUTHOR]

Summary Synaptic scaling is a key homeostatic plasticity mechanism and is thought to be involved in the regulation of cortical activity levels. Here we investigated the spatial scale of homeostatic changes in spine size following sensory deprivation in a subset of inhibitory (layer 2/3 GAD65 -positive) and excitatory (layer 5 Thy1 -positive) neurons in mouse visual cortex. Using repeated in vivo two-photon imaging, we find that increases in spine size are tumor necrosis factor alpha (TNF-α) dependent and thus are likely associated with synaptic scaling. Rather than occurring at all spines, the observed increases in spine size are spatially localized to a subset of dendritic branches and are correlated with the degree of recent local spine loss within that branch. Using simulations, we show that such a compartmentalized form of synaptic scaling has computational benefits over cell-wide scaling for information processing within the cell. [ABSTRACT FROM AUTHOR]

Summary The cortex is organized as a hierarchical processing structure. Feedback from higher levels of the hierarchy, known as top-down signals, have been shown to be involved in attentional and contextual modulation of sensory responses. Here we argue that top-down input to the primary visual cortex (V1) from A24b and the adjacent secondary motor cortex (M2) signals a prediction of visual flow based on motor output. A24b/M2 sends a dense and topographically organized projection to V1 that targets most neurons in layer 2/3. By imaging the activity of A24b/M2 axons in V1 of mice learning to navigate a 2D virtual environment, we found that their activity was strongly correlated with locomotion and resulting visual flow feedback in an experience-dependent manner. When mice were trained to navigate a left-right inverted virtual environment, correlations of neural activity with behavior reversed to match visual flow. These findings are consistent with a predictive coding interpretation of visual processing. [ABSTRACT FROM AUTHOR]

Here, we present a sample collection protocol for single-cell RNA sequencing of functionally identified neuronal populations in vivo with a virally delivered activity-dependent labeling tool (CaMPARI2). We describe steps for photoconversion in mice during the onset of computationally relevant events in a virtual reality environment, followed by removal and dissociation of the photo-labeled tissue, and separation of differentially labeled groups with fluorescence-activated cell sorting (FACS). We then detail procedures for characterizing and examining functionally relevant groups using standard bioinformatic techniques. For complete details on the use and execution of this protocol, please refer to O'Toole et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The aim of this work is to provide a comment on a recent paper by Muzzu and Saleem (2021), which claims that visuomotor mismatch responses in mouse visual cortex can be explained by a locomotion-induced gain of visual halt responses. Our primary concern is that without directly comparing these responses with mismatch responses, the claim that one response can explain the other appears difficult to uphold, more so because previous work finds that a uniform locomotion-induced gain cannot explain mismatch responses. To support these arguments, we analyze layer 2/3 calcium imaging datasets and show that coupling between visual flow and locomotion greatly enhances mismatch responses in an experience-dependent manner compared with halts in non-coupled visual flow. This is consistent with mismatch responses representing visuomotor prediction errors. Thus, we conclude that while feature selectivity might contribute to mismatch responses in mouse visual cortex, it cannot explain these responses., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)