<p>Show notes:</p>
<ul>
<li><a href="https://www.ru.nl/donders/">Donders Institute for Brain, Cognition and Behaviour</a></li>
<li><a href="https://twitter.com/artcogsys">Artificial Cognitive Systems</a> on Twitter.</li>
<li><a href="http://artcogsys.com/">Artificial Cognitive Systems</a> research group.</li>
<li>The paper we discuss:
<ul>
<li><a href="https://www.biorxiv.org/content/early/2018/06/29/226688">Generative adversarial networks for reconstructing natural images from brain activity</a>.</li>
</ul>
</li>
<li>Read their <a href="https://mindcodec.ai/">Mindcodec</a> blog.</li>
<li>Nice overview of GANs (appropriately titled): <a href="https://arxiv.org/pdf/1710.07035.pdf">Generative Adversarial Networks: An Overview</a>.</li>
</ul>

Show notes: Donders Institute for Brain, Cognition and Behaviour Artificial Cognitive Systems on Twitter. Artificial Cognitive Systems research group. The paper we discuss: Generative adversarial networks for reconstructing natural images from brain activity. Read their Mindcodec blog. Nice overview of GANs (appropriately titled): Generative Adversarial Networks: An Overview.

<p>Show notes:</p>
<ul>
<li><a href="https://www.ru.nl/donders/" rel="noopener noreferrer">Donders Institute for Brain, Cognition and Behaviour</a></li>
<li><a href="https://twitter.com/artcogsys" rel="noopener noreferrer">Artificial Cognitive Systems</a> on Twitter.</li>
<li><a href="http://artcogsys.com/" rel="noopener noreferrer">Artificial Cognitive Systems</a> research group.</li>
<li>The paper we discuss:
<ul>
<li><a href="https://www.biorxiv.org/content/early/2018/06/29/226688" rel="noopener noreferrer">Generative adversarial networks for reconstructing natural images from brain activity</a>.</li>
</ul>
</li>
<li>Read their <a href="https://mindcodec.ai/" rel="noopener noreferrer">Mindcodec</a> blog.</li>
<li>Nice overview of GANs (appropriately titled):&nbsp;<a href="https://arxiv.org/pdf/1710.07035.pdf" rel="noopener noreferrer">Generative Adversarial Networks: An Overview</a>.</li>
</ul>

BI 023 Marcel van Gerven: Mind Decoding with GANs

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.

Share BI 023 Marcel van Gerven: Mind Decoding with GANs

Sign up to save your podcasts

BI 023 Marcel van Gerven: Mind Decoding with GANs

BI 023 Marcel van Gerven: Mind Decoding with GANs

More shows like Brain Inspired

Making Sense with Sam Harris

Into the Impossible With Brian Keating

80,000 Hours Podcast

The Michael Shermer Show

Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

Your Undivided Attention

The Jim Rutt Show

Google DeepMind: The Podcast

COMPLEXITY

Machine Learning Street Talk (MLST)

Dwarkesh Podcast

Clearer Thinking with Spencer Greenberg

The Joy of Why

Robinson's Podcast

Theoretical Neuroscience Podcast