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Contributed Talk Session: Thursday, August 14, 10:00 – 11:00 am, Room C1.03
Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Developmental plasticity rules facilitate representation learning in a model of visual ventral stream

Ariane Delrocq1, Zihan Wu1, Guillaume Bellec2, Wulfram Gerstner1; 1EPFL - EPF Lausanne, 2Technische Universität Wien

Presenter: Ariane Delrocq

It is known that different cortical areas have different critical periods for the most fundamental learning. However, the type of developmental plasticity rules that lead to high-level representations of objects are unknown. Here, we study a model of the visual ventral stream trained by a generalized Hebbian plasticity rule. The learning rule uses only quantities that are locally available at the site of the synapse, is consistent with recent plasticity experiments in pyramidal neurons, and, as opposed to the backpropagation algorithm, does not need a detailed feedback architecture. Our model shows that limiting plasticity in time to critical periods of development improves the quality of learned representation. Our model achieves state-of-the-art performance for bio-plausible plasticity models on the STL10 large image dataset designed for unsupervised learning.

Topic Area: Visual Processing & Computational Vision

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