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- W4378902082 abstract "Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli distributed representations and processing, are fundamentally based on averages and, therefore, can focus precisely on the features that are most stable across stimulus presentations. But what are these stable features that may sustain stable perception across experiences is still poorly understood. This question is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy observed in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding —and, therefore, likely reflect stable mechanisms of stimulus representation— have an oscillatory component that spans multiple channels, frequencies and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory, faster component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations." @default.
- W4378902082 created "2023-06-01" @default.
- W4378902082 date "2023-05-31" @default.
- W4378902082 modified "2023-10-03" @default.
- W4378902082 title "Author response: A generative model of electrophysiological brain responses to stimulation" @default.
- W4378902082 doi "https://doi.org/10.7554/elife.87729.1.sa3" @default.
- W4378902082 hasPublicationYear "2023" @default.
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