BI NMA 03: Stochastic Processes Panel

BI NMA 03: Stochastic Processes Panel

Brain Inspired
BI NMA 03: Stochastic Processes Panel
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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.

BI NMA 02: Dynamical Systems Panel

BI NMA 02: Dynamical Systems Panel

Brain Inspired
BI NMA 02: Dynamical Systems Panel
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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.

BI NMA 01: Machine Learning Panel

BI NMA 01: Machine Learning Panel

Brain Inspired
BI NMA 01: Machine Learning Panel
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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.

BI 110 Catherine Stinson and Jessica Thompson: Neuro-AI Explanation

BI 110 Catherine Stinson and Jessica Thompson: Neuro-AI Explanation

Brain Inspired
BI 110 Catherine Stinson and Jessica Thompson: Neuro-AI Explanation
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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.

BI 109 Mark Bickhard: Interactivism

BI 109 Mark Bickhard: Interactivism

Brain Inspired
BI 109 Mark Bickhard: Interactivism
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Mark and I discuss a wide range of topics surrounding his Interactivism framework for explaining cognition. Interactivism stems from Mark’s account of representations and how what we represent in our minds is related to the external world – a challenge that has plagued the mind-body problem since the beginning. Basically, representations are anticipated interactions with the world, that can be true (if enacting one helps an organism maintain its thermodynamic relation with the world) or false (if it doesn’t). And representations are functional, in that they function to maintain far from equilibrium thermodynamics for the organism for self-maintenance. Over the years, Mark has filled out Interactivism, starting with a process metaphysics foundation and building from there to account for representations, how our brains might implement representations, and why AI is hindered by our modern “encoding” version of representation. We also compare interactivism to other similar frameworks, like enactivism, predictive processing, and the free energy principle