Predictability-dependent encoding of statistical regularities in the early visual cortex

Previous research recognized that humans could extract statistical regularities of the environment to automatically predict upcoming events. However, it has remained unexplored how the brain encodes the distribution of statistical regularities if it continuously changes. To investigate this question, we devised an fMRI paradigm where participants (N = 32) completed a visual four-choice reaction time (RT) task consisting of statistical regularities. Two types of blocks involving the same perceptual elements alternated with one another throughout the task: While the distribution of statistical regularities was predictable in one block type, it was unpredictable in the other. Participants were unaware of the presence of statistical regularities and of their changing distribution across the subsequent task blocks. Based on the RT results, although statistical regularities were processed similarly in both the predictable and unpredictable blocks, participants acquired less statistical knowledge in the unpredictable as compared with the predictable blocks. Whole-brain random-effects analyses showed increased activity in the early visual cortex and decreased activity in the precuneus for the predictable as compared with the unpredictable blocks. Therefore, the actual predictability of statistical regularities is likely to be represented already at the early stages of visual cortical processing. However, decreased precuneus activity suggests that these representations are imperfectly updated to track the multiple shifts in predictability throughout the task. The results also highlight that the processing of statistical regularities in a changing environment could be habitual.

Attention modulates neural representation to render reconstructions according to subjective appearance

Stimulus images can be reconstructed from visual cortical activity. However, our perception of stimuli is shaped by both stimulus-induced and top-down processes, and it is unclear whether and how reconstructions reflect top-down aspects of perception. Here, we investigate the effect of attention on reconstructions using fMRI activity measured while subjects attend to one of two superimposed images. A state-of-the-art method is used for image reconstruction, in which brain activity is translated (decoded) to deep neural network (DNN) features of hierarchical layers then to an image. Reconstructions resemble the attended rather than unattended images. They can be modeled by superimposed images with biased contrasts, comparable to the appearance during attention. Attentional modulations are found in a broad range of hierarchical visual representations and mirror the brain–DNN correspondence. Our results demonstrate that top-down attention counters stimulus-induced responses, modulating neural representations to render reconstructions in accordance with subjective appearance.

Inter-individual deep image reconstruction

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. While anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual contents. In this study, we evaluated machine learning models called neural code converters that predict one's brain activity pattern (target) from another's (source) given the same stimulus by the decoding of hierarchical visual features and the reconstruction of perceived images. The training data for converters consisted of fMRI data obtained with identical sets of natural images presented to pairs of individuals. Converters were trained using the whole visual cortical voxels from V1 through the ventral object areas, without explicit labels of visual areas. We decoded the converted brain activity patterns into hierarchical visual features of a deep neural network (DNN) using decoders pre-trained on the target brain and then reconstructed images via the decoded features. Without explicit information about visual cortical hierarchy, the converters automatically learned the correspondence between the visual areas of the same levels. DNN feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The viewed images were faithfully reconstructed with recognizable silhouettes of objects even with relatively small amounts of data for converter training. The conversion also allows pooling data across multiple individuals, leading to stably high reconstruction accuracy compared to those converted between individuals. These results demonstrate that the conversion learns hierarchical correspondence and preserves the fine-grained representations of visual features, enabling visual image reconstruction using decoders trained on other individuals.

Efficient learning in children with rapid GABA boosting after training

It is unclear why and how children learn more efficiently than adults, although inhibitory systems, which play an important role in stabilizing learning, are immature in children. Here, we found that despite a lower baseline concentration of gamma-aminobutyric acid (GABA) in early visual cortical areas in children (8 to 11 years old) than adults (18 to 35 years old), children exhibited a rapid boost of GABA immediately after visual training, whereas the concentration of GABA in adults remained unchanged after training. Moreover, behavioral experiments showed that children stabilized visual learning much faster than adults, showing rapid development of resilience to retrograde interference. These results together suggest that inhibitory systems in children's brains are more dynamic and adapt more quickly to stabilize learning than in adults.

Moving bar of light evokes vectorial spatial selectivity in the immobile rat hippocampus

Visual cortical neurons encode the position and motion direction of specific stimuli retrospectively, without any locomotion or task demand. Hippocampus, a part of visual system, is hypothesized to require self-motion or cognitive task to generate allocentric spatial selectivity that is scalar, abstract, and prospective. To bridge these seeming disparities, we measured rodent hippocampal selectivity to a moving bar of light in a body-fixed rat. About 70% of dorsal CA1 neurons showed stable activity modulation as a function of the bar angular position, independent of behavior and rewards. A third of tuned cells also encoded the direction of revolution. In other experiments, neurons encoded the distance of the bar, with preference for approaching motion. Collectively, these demonstrate visually evoked vectorial selectivity (VEVS). Unlike place cells, VEVS was retrospective. Changes in the visual stimulus or its trajectory did not cause remapping but only caused gradual changes. Most VEVS-tuned neurons behaved like place cells during spatial exploration and the two selectivities were correlated. Thus, VEVS could form the basic building block of hippocampal activity. When combined with self-motion, reward, or multisensory stimuli, it can generate the complexity of prospective representations including allocentric space, time, and episodes.

Inhibition of Cdk5 in PV Neurons Reactivates Experience-Dependent Plasticity in Adult Visual Cortex

Cyclin-dependent kinase 5 (Cdk5) has been shown to play a critical role in brain development, learning, memory and neural processing in general. Cdk5 is widely distributed in many neuron types in the central nervous system, while its cell-specific role is largely unknown. Our previous study showed that Cdk5 inhibition restored ocular dominance (OD) plasticity in adulthood. In this study, we specifically knocked down Cdk5 in different types of neurons in the visual cortex and examined OD plasticity by optical imaging of intrinsic signals. Downregulation of Cdk5 in parvalbumin-expressing (PV) inhibitory neurons, but not other neurons, reactivated adult mouse visual cortical plasticity. Cdk5 knockdown in PV neurons reduced the evoked firing rate, which was accompanied by an increment in the threshold current for the generation of a single action potential (AP) and hyperpolarization of the resting membrane potential. Moreover, chemogenetic activation of PV neurons in the visual cortex can attenuate the restoration of OD plasticity by Cdk5 inhibition. Taken together, our results suggest that Cdk5 in PV interneurons may play a role in modulating the excitation and inhibition balance to control the plasticity of the visual cortex.

Selective representations of texture and motion in mouse higher visual areas

Mice have a constellation of higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine neuronal representations of complex visual stimuli across mouse higher visual areas, measured using large field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how popular models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.