Mechanisms for spontaneous symmetry breaking in developing visual cortex
Francesco Fumarola, Bettina Hein, Kenneth D. Miller

TL;DR
This paper presents a theoretical model showing how Hebbian learning and recurrent interactions can spontaneously break symmetries in the visual cortex, explaining orientation selectivity development.
Contribution
It introduces a novel mechanism where competition among connections from the same neuron triggers symmetry breaking, aligning theory with experimental observations.
Findings
Symmetry breaking occurs with sufficiently long-range recurrent interactions.
Competition among afferent connections can induce symmetry breaking.
The model predicts specific length-scale conditions for symmetry breaking.
Abstract
For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions -- an archetypal example of unsupervised learning. The dominant framework for unsupervised learning in biology is Hebb's principle, but how Hebbian learning could break such symmetries is a longstanding biophysical riddle. Theoretical studies agree that this should require inputs to visual cortex to invert the relative magnitude of their correlations at long distances. Empirical measurements have searched in vain for such an inversion, and report the opposite to be true. We formally approach the question through the hermitianization of a multi-layer model, which maps it into a problem of zero-temperature phase transitions. In the emerging phase diagram, both symmetries break spontaneously as long as (1) recurrent interactions are…
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Taxonomy
TopicsNeural dynamics and brain function · Photoreceptor and optogenetics research · Molecular spectroscopy and chirality
