<p>Thomas and I talk about what happens in the brain’s visual system when you see something versus imagine it. He uses generative encoding and decoding models and brain signals like fMRI and EEG to test the nature of mental imagery. We also discuss the huge fMRI dataset of natural images he’s collected to infer models of the entire visual system, how we’ve still not tapped the potential of fMRI, and more.</p>



<ul class="wp-block-list"><li>Thomas's <a href="https://www.naselarislab.net/">lab website</a>.  </li><li>Papers we discuss or mention: <ul><li><a href="https://www.naselarislab.net/s/1-s20-S1364661315001692-main.pdf">Resolving Ambiguities of MVPA Using Explicit Models of Representation</a>. </li><li><a href="https://www.biorxiv.org/content/10.1101/462226v2">Human brain activity during mental imagery exhibits signatures of inference in a hierarchical generative model</a>. </li></ul></li></ul>

Thomas and I talk about what happens in the brain’s visual system when you see something versus imagine it. He uses generative encoding and decoding models and brain signals like fMRI and EEG to test the nature of mental imagery. We also discuss the huge fMRI dataset of natural images he’s collected to infer models of the entire visual system, how we’ve still not tapped the potential of fMRI, and more. Thomas's lab website. Papers we discuss or mention: Resolving Ambiguities of MVPA Using Explicit Models of Representation. Human brain activity during mental imagery exhibits signatures of inference in a hierarchical generative model.

<p>Thomas and I talk about what happens in the brain’s visual system when you see something versus imagine it. He uses generative encoding and decoding models and brain signals like fMRI and EEG to test the nature of mental imagery. We also discuss the huge fMRI dataset of natural images he’s collected to infer models of the entire visual system, how we’ve still not tapped the potential of fMRI, and more.</p>



<ul><li>Thomas's <a href="https://www.naselarislab.net/" rel="noopener noreferrer">lab website</a>.&nbsp; </li><li>Papers we discuss or mention: <ul><li><a href="https://www.naselarislab.net/s/1-s20-S1364661315001692-main.pdf" rel="noopener noreferrer">Resolving Ambiguities of MVPA Using Explicit Models of Representation</a>. </li><li><a href="https://www.biorxiv.org/content/10.1101/462226v2" rel="noopener noreferrer">Human brain activity during mental imagery exhibits signatures of inference in a hierarchical generative model</a>. </li></ul></li></ul>

BI 055 Thomas Naselaris: Seeing Versus Imagining

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

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BI 055 Thomas Naselaris: Seeing Versus Imagining

BI 055 Thomas Naselaris: Seeing Versus Imagining

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