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Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us tomap the natural image preferences of the neural population at 100-μmscale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4. 

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons. 

Abstract The integration of synaptic inputs onto dendrites provides the basis for neuronal computation. Whereas recent studies have begun to outline the spatial organization of synaptic inputs on individual neurons, the underlying principles related to the specific neural functions are not well understood. Here we perform two-photon dendritic imaging with a genetically-encoded glutamate sensor in awake monkeys, and map the excitatory synaptic inputs on dendrites of individual V1 superficial layer neurons with high spatial and temporal resolution. We find a functional integration and trade-off between orientation-selective and color-selective inputs in basal dendrites of individual V1 neurons. Synaptic inputs on dendrites are spatially clustered by stimulus feature, but functionally scattered in multidimensional feature space, providing a potential substrate of local feature integration on dendritic branches. Furthermore, apical dendrite inputs have larger receptive fields and longer response latencies than basal dendrite inputs, suggesting a dominant role for apical dendrites in integrating feedback in visual information processing. 

Several problems challenge mesoscopic imaging in the brain: 1) Difficulty with positioning high-NA objectives near the brain; 2) Creating a flat imaging window against the surface of the brain; 3) Adjusting the imaging window in the face of changes in swelling and pressure in the brain; 4) Preventing growth of dura and biofilms that obscure the imaging window; 5) Follow-on MRI imaging of the animal post-implantation. We propose here an ultra-large window radiolucent implant to address these issues. Our approach provides a 2 cm diameter window for non-human primates (NHPs) that regulates pressure and employs a stable, strong, and thin design. The system is mechanically modeled and stress-tested to achieve access to the brain by large objectives, with design features that allow for manual repositioning of the imaging lens. To optimize the distance between the objective and the brain, we prioritize a thin implant design. A strong radiolucent implant is created using PEEK plastic, a strong, thermoresistant and biostable material. We heighten strength of the chamber’s attachment to the skull by using titanium screws that are normal to the surface of the bone at each point. The implant design has several parts and contemplates a potential method to maintain pressure on the brain. This method uses an engineered silicone mount to maintain even pressure of the imaging window on the brain’s surface, despite brain motion. The mechanical properties of the silicone are manipulated to closely resemble that of brain tissue to be more biomimetic and act as a cushion for motion. This method also allows for themanual repositioning of the cover slip to create a flat imaging window. Lastly, our approach prevents dural growth by blocking the migration of migratory biofilm-forming cells; we hypothesize that use of dynamic pressure maintenance on the brain is key to this method’s success. We are also investigating methods to elongate the longevity of the implant and imaging site, such as silver sputtering on implants and blue light therapy. These methods have produced an ultra-large field of view with 2P image results in <60,000 neurons. As such the chambers are expected to enhance recording window longevity and may prove to be a critical advance in NHP and human brain imaging. 

Several problems challenge mesoscopic imaging in the brain: 1) Difficulty with positioning high-NA objectives near the brain; 2) Creating a flat imaging window against the surface of the brain; 3) Adjusting the imaging window in the face of changes in swelling and pressure in the brain; 4) Preventing growth of dura and biofilms that obscure the imaging window; 5) Follow-on MRI imaging of the animal post-implantation. We propose here an ultra-large window radiolucent implant to address these issues. Our approach provides a 2 cm diameter window for non-human primates (NHPs) that regulates pressure and employs a stable, strong, and thin design. The system is mechanically modeled and stress-tested to achieve access to the brain by large objectives, with design features that allow for manual repositioning of the imaging lens. To optimize the distance between the objective and the brain, we prioritize a thin implant design. A strong radiolucent implant is created using PEEK plastic, a strong, thermoresistant and biostable material. We heighten strength of the chamber’s attachment to the skull by using titanium screws that are normal to the surface of the bone at each point. The implant design has several parts and contemplates a potential method to maintain pressure on the brain. This method uses an engineered silicone mount to maintain even pressure of the imaging window on the brain’s surface, despite brain motion. The mechanical properties of the silicone are manipulated to closely resemble that of brain tissue to be more biomimetic and act as a cushion for motion. This method also allows for the manual repositioning of the cover slip to create a flat imaging window. Lastly, our approach prevents dural growth by blocking the migration of migratory biofilm-forming cells; we hypothesize that use of dynamic pressure maintenance on the brain is key to this method’s success. We are also investigating methods to elongate the longevity of the implant and imaging site, such as silver sputtering on implants and blue light therapy. These methods have produced an ultra-large field of view with 2P image results in <60,000 neurons. As such the chambers are expected to enhance recording window longevity and may prove to be a critical advance in NHP and human brain imaging. 

The integration of synaptic inputs onto dendrites provides the basis for computation within individual neurons. Whereas recent studies have begun to outline the spatial organization of synaptic inputs on individual neurons, the underlying principles related to the specific neural functions is not well known. Here we performed two-photon dendritic imaging with genetically-encoded glutamate sensor in awake monkeys, and successfully mapped the excitatory synaptic inputs on dendrites of individual V1 neurons with high spatial and temporal resolution. We found that although synaptic inputs on dendrites were functionally clustered by feature, they were highly scattered in multidimensional feature space, providing a potential substrate of local feature integration on dendritic branches. We also found that nearly all individual neurons received both abundant orientation-selective and color-selective inputs. Furthermore, we found apical dendrites received more diverse inputs than basal dendrites, with larger receptive fields, and relatively longer response latencies, suggesting a specific apical role in integrating feedback in visual information processing.