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Decoding visual stimuli from cortical activity
Vašek, Vojtěch ; Antolík, Ján (advisor) ; Šikudová, Elena (referee)
This thesis aims to develop a machine learning model that can decode stimu- lus images from cortical activity in the primary visual cortex (V1) to understand the relationship between V1 activity and visual perception. The limited avai- lability of biological data makes it necessary to use the spiking neural network model of V1 to generate the underlying training data. Machine learning tech- niques, particularly neural networks, will be explored to generate high-quality stimulus images. Standard loss functions, as well as discriminator loss from GAN networks training, will be used to train the decoding models. Linear regression models will be used baseline. The research questions to be addressed include the best decoding approach, the impact of the number of neurons recorded or stimuli presented, the loss of information in high frequencies domain and the effect of intrinsic noise in neural responses on reconstructing visual stimuli. This thesis proposes a trainable convolutional network, which outperforms other baseline models such as linear regression. We observe that the loss function producing the best results is the MSSSIM. However, the intrinsic noise in neural respon- ses limits the reconstruction, and only low frequencies are being reconstructed. The size of the dataset and the number of cortical...
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