Models of psychophysics tasks can aid in understanding the information processing mechanisms in the human brain as well in the development of models for more complex tasks. Flicker fusion is a well studied domain in visual psychophysics, useful for its apparent simplicity. Experiments on flicker fusion phenomena involve presenting a human subject with an intermittent stimulus and the subject classifying the stimulus as either flickering or steady. Mathematical models that seek to account for flicker perception have been proposed, though many of these models were put forward before the advanced computational techniques, available today, were developed. The present work attempts to develop a DNN based Brain Computational Model of flicker fusion that could be trained and tested on pscyhophysics data of a single subject. The mapping of flicker stimuli to the classifications made by a human subject, allows a network to model the phenomena via supervised learning. Since the flicker stimulus is a time series data, we set out to find an appropriate recurrent neural network to model the phenomena. We propose a Convolutional Recurrent Neural Network (CRNN) along with logarithmic transformations of the input representation to model the data. We show that the model that has been pretrained on a particular subject could be used to train data of another subject much faster. The proposed model is trained using stimuli that could be defined with a few parameters. We tested the model on different categories of flicker stimuli that it was not trained with, and made subjective comparisons of the results with previously published psychophysics data. The model has not been evaluated with standard techniques, but the subjective comparisons show that the proposed model will be useful for developing more psychophysically faithful models in the future.
The ten hertz alpha oscillations are the strongest rhythmic encodings from human brain. They arise from occipital lobes and are connected with visual perception and visual cognition. A flicker stimulus can also evoke oscillations in human EEG that are of the same frequency as the flicker stimulus. The oscillations are evoked even when there is no conscious perception of the flicker stimulus. The oscillations so evoked show resonances at 10, 20, 40 and 80 hertz. When a subject is not able to perceive the flicker and the source appears as a steady source of light, flicker fusion is said to have occurred. Psychophysics experiments on flicker fusion involves presenting a human subject with a flicker stimulus, and the subject classifying the stimulus as either flickering or fused. The physical parameters associated with the stimulus that determines whether the stimulus is classified as flickering or fused by the subject, are varied in psychophysics experiments. Deep neural networks, on the other hand, have been proposed as a model for cognition and perception that can make falsifiable predictions. In this work, motivated by the feedforward and feedback visual pathways, we propose a deep convolutional recurrent neural network model that may be trained using psychophysics data. The neural network takes the time series representation of the flicker stimulus as input, and the binary classification made by the subject on whether stimulus appears flickering or fused, is set as the output. We show that an intermediate convolution layer of such a recurrent neural network trained on psychophysics data can give sinusoidal output on the input of a representation of ten hertz stimulus, signifying that an in-silica computation of alpha oscillations is possible through such a network.
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