Deep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation. The goal of this paper is to test whether such a representation is indeed generally characteristic for visual cortex, i.e. generalizes between animals of a species, and what factors contribute to obtaining such a generalizing core. To push all non-linear computations into the core where the generalizing cortical features should be learned, we devise a novel readout that reduces the number of parameters per neuron in the readout by up to two orders of magnitude compared to the previous state-of-the-art. It does so by taking advantage of retinotopy and learns a Gaussian distribution over the neuron’s receptive field position. With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network. We then investigate whether the convolutional core indeed captures general cortical features by using the core in transfer learning to a different animal. When transferring a core trained on thousands of neurons from various animals and scans we exceed the performance of training directly on that animal by 12%, and outperform a commonly used VGG16 core pre-trained on imagenet by 33%. In addition, transfer learning with our data-driven core is more data-efficient than direct training, achieving the same performance with only 40% of the data. Our model with its novel readout thus sets a new state-of-the-art for neural response prediction in mouse visual cortex from natural images, generalizes between animals, and captures better characteristic cortical features than current task-driven pre-training approaches such as VGG16.
Recent research reveal that the Working Memory (WM) is more powerful than IQ as a predictor of academic success. However, there are factors that may influence WM performance, such as Attention. Although the impact of attention is well documented using ERPs; yet, the underlying brain connectivity of the interaction of these two constructs is not sufficiently understood. In this study, a Delay-Response task and electroencephalography (EEG) data are used to investigate the brain connectivity during two stages of Working Memory: Encoding and Maintenance. We have presented distraction in both stages, and a secondary task in maintenance stage. Scalp EEG data of 19 participants were recorded. These results not only reveal the underlying brain connectivity of each task, but also highlights the differences between distraction and multitasking. The results show significant brain connectivity changes in the frontal and occipital areas of the brain depending on the WM stage where the distraction is presented.
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