The recent advances in deep learning have done more than just make money for startups and tech companies. They've also infiltrated neuroscience! Deep neural networks---models originally inspired by the basics of the nervous system---are finding ever more applications in the quest to understand the brain. We talk about many of those uses in the episode. After first describing more traditional approaches to modeling behavior, we talk about how neuroscientists compare deep net models to real brains using both performance and neural activity. We then get into the attempts by the field of machine learning to understand their own models and how ML and neuroscience can share methods (and maybe certain cultural tendencies). Finally we talk about the use of deep nets to generate stimuli specifically tailored to drive real neurons to their extremes. Throughout, we notice how deep learning is "complicating the narrative", ask "are deep nets
normative models?", and struggle to talk about a topic we actually know about.
We read:
Deep neural network models of sensory systems: windows onto the role of task constraints
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
Neural population control via deep image synthesis
Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences
And we mentioned previous episodes:
Deep Learning
"Just-So" Stories of Bayesian Modeling in Psychology
Learning Rules, Biological vs. Artificial
Grace has also written a blog on comparing CNNs to the visual system:
Deep Convolutional Neural Networks as Models of the Visual System: Q&A
Finally, for those who get to the end of the episode, these are the images we're talking about (you decide which ones are pleasant and which are creepy AF....):
To listen to (or download) this episode, (right) click
here
As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)
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