Neural Networking for Human Neuroplasticity With Deep Neural Network for Finding Human Talent

A key component of understanding the human brain's information processing mechanism is neural networking, which seeks to create a quantifiable connection between the stimuli and the triggered brain processes. Population receptive field (pRF) models have gained a lot of traction and demonstrated steady advancement in the field of visual neuroscience recently. These models can describe the functioning of neurons in basic neuroplasticity. However, the expressiveness and interpretability of previous models are significantly constrained by their reliance on either imprecise parameter estimation methods or tight prior beliefs regarding pRF. In this study, we present a unique neural networking architecture that trains deep neural networks to recognize “where” and “what.” Two separate elements that are involved in neuroplasticity are the spatial feature (“where”) and feature selection (“what”) of neuron clusters. The method described involves three main steps. The initial phase is utilizing a pretrained deep neural network (DNN) to encode visual data into preliminary features. To find “where,” these features are further refined through the use of channel attention and spatial receptive field. In order to determine “what,” the improved features are then simultaneously regressed onto voxel activity. This modeling approach incorporates smoothness and sparsity regularizations, enabling us to automatically determine the essential receptive field size without assuming any specific shape in advance. Using multi-voxel joint networking models, the study investigates extending voxel-wise modeling to successfully improve the accuracy of underperforming voxels. The research demonstrates that this approach, which involves fewer prior constraints but achieves better networking performance, offers an effective way to design neural networks for studying human neuroplasticity.