Degeneracy in neuronal representations and learning
Platica dada por Timothy O´Leary (Department of Engineering, University of Cambridge, UK) como parte del Seminario de Biologia Teorica el jueves 11 de abril del 2019 en la sala S-105 del Departamento de Matematicas, Facultad de Ciencias.
Abstract: Neurons and neural circuits have lots of parameters that are subject to biological feedback control. Often, multiple components affect a relatively simple, global physiological variable that is then used to control the expression of each component, a scenario we call degenerate feedback. In single neurons, receptors and ion channels shape average electrical activity, and this activity itself regulates expression of receptors and channels over slow timescales. In neuronal networks, synaptic connections are shaped by reward signals during learning, while the connectivity of the network determines performance, and therefore reward. I will describe recent work that explores the consequences of degenerate feedback control in both situations. In single neurons we find that degeneracy permits flexibility in a neuron’s membrane properties at the cost of robustness to specific perturbations. In networks we find that noisy feedback on task performance can enable a network to learn, and redundancy in the network connectivity can make a fixed task easier to learn in the presence of an imperfect learning rule.
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