Mechanistic Models of Cognition: from Perception to Navigation to Semantic Development



We will show how to combine large scale neural recordings and mechanistic neural network models to advance our conceptual understanding of how neural circuits mediate cognitive functions like perception, navigation and semantic cognition.  With additional mathematical analysis, we can also even explain why some neural circuits might be organized the way they are.  First, we will describe state of the art models of the retinal response to natural movies, and use explainable artificial intelligence to understand how they recapitulate over 20 years of retinal physiology experiments.  Second we will describe how the 4 most dominant cell-types in our retina emerge naturally as a consequence of optimal spatiotemporal processing of natural movies.  Third, we will demonstrate how hexagonal grid cell firing fields must obligatorily arise in any biologically plausible neural network that is capable of performing path-integration.   And fourth, time permitting, we will describe how the hierarchical differentiation of concepts that unfolds over time in infant semantic development can be accounted for by simple mechanistic neural models. 

Learning objectives:

1. Describe how to create mechanistic neural models of cognitive phenomena

2. Understand explainable artificial intelligence and how to use it to extract conceptual insights from mechanistic models

3. Learn about optimality principles for explaining why some neural circuits are organized the way they are

4. Learn about specific phenomena in perception, navigation, and semantic cognition

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