BI 112: Ali Mohebi and Ben Engelhard The Many Faces of Dopamine Announcement: Ben has started his new lab and is recruiting grad students. Check out his lab here and apply! Engelhard Lab   Ali and Ben discuss the ever-expanding discoveries about the roles dopamine plays for our cognition. Dopamine is known to play a role in learning – dopamine (DA) neurons fire when our reward expectations aren’t met, and that signal helps adjust our expectation. Roughly, DA corresponds to a reward prediction error. The reward prediction error has helped reinforcement learning in AI develop into a raging success, specially with deep reinforcement learning models trained to out-perform humans in games like chess and Go. But DA likely contributes a lot more to brain function. We discuss many of those possible roles, how to think about computation with respect to neuromodulators like DA, how different time and spatial scales interact, and more. Dopamine: A Simple AND Complex Story  by Daphne Cornelisse Guests Ali Mohebi @mohebial Ben Engelhard Timestamps: 0:00 – Intro 5:02 – Virtual Dopamine Conference 9:56 – History of dopamine’s roles 16:47 – Dopamine circuits 21:13 – Multiple roles for dopamine 31:43 – Deep learning panel discussion 50:14 – Computation and neuromodulation

BI NMA 05: NLP and Generative Models Panel This is the 5th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the 2nd of 3 in the deep learning series. In this episode, the panelists discuss their experiences “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs). Panelists Brad Wyble. @bradpwyble. Kyunghyun Cho. @kchonyc. He He. @hhexiy. João Sedoc. @JoaoSedoc. The other panels: First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning. Second panel, about linear systems, real neurons, and dynamic networks. Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality. Fourth panel, about some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization. Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

BI NMA 04: Deep Learning Basics Panel This is the 4th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the first of 3 in the deep learning series. In this episode, the panelists discuss their experiences with some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization. Guests Amita Kapoor Lyle Ungar @LyleUngar Surya Ganguli @SuryaGanguli The other panels: First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning. Second panel, about linear systems, real neurons, and dynamic networks. Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality. Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs). Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.   Timestamps:  

Erik, Kevin, and I discuss... well a lot of things. Erik's recent novel The Revelations is a story about a group of neuroscientists trying to develop a good theory of consciousness (with a murder mystery plot). Kevin's book Innate - How the Wiring of Our Brains Shapes Who We Are describes the messy process of getting from DNA, traversing epigenetics and development, to our personalities. We talk about both books, then dive deeper into topics like whether brains evolved for moving our bodies vs. consciousness, how information theory is lending insights to emergent phenomena, and the role of agency with respect to what counts as intelligence. Kevin's website.Eriks' website.Twitter: @WiringtheBrain (Kevin); @erikphoel (Erik)Books:INNATE – How the Wiring of Our Brains Shapes Who We AreThe RevelationsPapersErikFalsification and consciousness.The emergence of informative higher scales in complex networks.Emergence as the conversion of information: A unifying theory. Timestamps 0:00 - Intro 3:28 - The Revelations - Erik's novel 15:15 - Innate - Kevin's book 22:56 - Cycle of progress 29:05 - Brains for movement or consciousness? 46:46 - Freud's influence 59:18 - Theories of consciousness 1:02:02 - Meaning and emergence 1:05:50 - Reduction in neuroscience 1:23:03 - Micro and macro - emergence 1:29:35 - Agency and intelligence

Panelists: Yael Niv.@yael_nivKonrad Kording@KordingLab.Previous BI episodes:BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.BI 014 Konrad Kording: Regulators, Mount Up!Sam Gershman.@gershbrain.Previous BI episodes:BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?BI 028 Sam Gershman: Free Energy Principle & Human Machines.Tim Behrens.@behrenstim.Previous BI episodes:BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.BI 024 Tim Behrens: Cognitive Maps. This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality. The other panels: First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.Second panel, about linear systems, real neurons, and dynamic networks.Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Panelists: Adrienne Fairhall.@alfairhall.Bing Brunton.@bingbrunton.Kanaka Rajan.@rajankdr.BI 054 Kanaka Rajan: How Do We Switch Behaviors? This is the second in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with linear systems, real neurons, and dynamic networks. Other panels: First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Panelists: Athena Akrami: @AthenaAkrami.Demba Ba.Gunnar Blohm: @GunnarBlohm.Kunlin Wei. This is the first in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with model fitting, GLMs/machine learning, dimensionality reduction, and deep learning. Other panels: Second panel, about linear systems, real neurons, and dynamic networks.Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Catherine, Jess, and I use some of the ideas from their recent papers to discuss how different types of explanations in neuroscience and AI could be unified into explanations of intelligence, natural or artificial. Catherine has written about how models are related to the target system they are built to explain. She suggests both the model and the target system should be considered as instantiations of a specific kind of phenomenon, and explanation is a product of relating the model and the target system to that specific aspect they both share. Jess has suggested we shift our focus of explanation from objects - like a brain area or a deep learning model - to the shared class of phenomenon performed by those objects. Doing so may help bridge the gap between the different forms of explanation currently used in neuroscience and AI. We also discuss Henk de Regt's conception of scientific understanding and its relation to explanation (they're different!), and plenty more. Catherine's website.Jessica's blog.Twitter: Jess: @tsonj.Related papersFrom Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence - CatherineForms of explanation and understanding for neuroscience and artificial intelligence - JessJess is a postdoc in Chris Summerfield's lab, and Chris and San Gershman were on a recent episode.Understanding Scientific Understanding by Henk de Regt. Timestamps: 0:00 - Intro 11:11 - Background and approaches 27:00 - Understanding distinct from explanation 36:00 - Explanations as programs (early explanation) 40:42 - Explaining classes of phenomena 52:05 - Constitutive (neuro) vs. etiological (AI) explanations 1:04:04 - Do nonphysical objects count for explanation? 1:10:51 - Advice for early philosopher/scientists