<p>Mentioned in the show:</p>
<ul>
<li><a href="https://www.humphries-lab.org/">Mark’s lab</a></li>
<li>The excellent blog <a href="https://medium.com/the-spike">he writes on Medium</a></li>
<li>The paper we discuss: <a href="https://www.nature.com/articles/s41467-018-04638-2">An ensemble code in medial prefrontal cortex links prior events to outcomes during learning</a></li>
<li>The code to <a href="https://github.com/sibangi/PFCensemble_ITI">replicate their findings</a></li>
<li>Dynamical networks: <a href="https://www.mitpressjournals.org/doi/abs/10.1162/NETN_a_00020">Finding, measuring, and tracking neural population activity using network science</a></li>
</ul>

Mentioned in the show: Mark’s lab The excellent blog he writes on Medium The paper we discuss: An ensemble code in medial prefrontal cortex links prior events to outcomes during learning The code to replicate their findings Dynamical networks: Finding, measuring, and tracking neural population activity using network science

<p>Mentioned in the show:</p>
<ul>
<li><a href="https://www.humphries-lab.org/" rel="noopener noreferrer">Mark’s lab</a></li>
<li>The excellent blog <a href="https://medium.com/the-spike" rel="noopener noreferrer">he writes on Medium</a></li>
<li>The paper we discuss:&nbsp;<a href="https://www.nature.com/articles/s41467-018-04638-2" rel="noopener noreferrer">An ensemble code in medial prefrontal cortex links prior events to outcomes during learning</a></li>
<li>The code to <a href="https://github.com/sibangi/PFCensemble_ITI" rel="noopener noreferrer">replicate their findings</a></li>
<li>Dynamical networks: <a href="https://www.mitpressjournals.org/doi/abs/10.1162/NETN_a_00020" rel="noopener noreferrer">Finding, measuring, and tracking neural population activity using network science</a></li>
</ul>

BI 004 Mark Humphries: Learning to Remember

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 004 Mark Humphries: Learning to Remember

BI 004 Mark Humphries: Learning to Remember

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