NMC4 Keynote: An all-natural deep recurrent neural network architecture for flexible navigation
A wide variety of animals and some artificial agents can adapt their behavior to changing cues, contexts, and goals. But what neural network architectures support such behavioral flexibility? Agents with loosely structured network architectures and random connections can be trained over millions of trials to display flexibility in specific tasks, but many animals must adapt and learn with much less experience just to survive. Further, it has been challenging to understand how the structure of trained deep neural networks relates to their functional properties, an important objective for neuroscience. In my talk, I will use a combination of behavioral, physiological and connectomic evidence from the fly to make the case that the built-in modularity and structure of its networks incorporate key aspects of the animal’s ecological niche, enabling rapid flexibility by constraining learning to operate on a restricted parameter set. It is not unlikely that this is also a feature of many biological neural networks across other animals, large and small, and with and without vertebrae.
Evidence for electrical coupling between proximal axons of principal neurons
The seminar will present the origin of the hypothesis of electrical coupling between proximal axons, physiological and immunostaining evidence for the presence of the requisite gap junctions and will explain how electrical coupling could account for very fast network oscillations at >80 hz.