Structured recurrent connectivity can drive synaptic learning even in fully plastic networks.

Structured recurrent connectivity can drive synaptic learning even in fully plastic networks.

<p><b>a.</b> A diagram of the fully recurrent plastic network. A comparison of the (<b>b.</b>) weight co-tuning (<i>CT</i><sub><i>w</i></sub>) and (<b>c.</b>) diversity (<i>D</i>) metrics for plastic networks w...

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Bibliographic Details
Main Author:	Emmanouil Giannakakis (8415591) (author)
Other Authors:	Oleg Vinogradov (19996957) (author), Victor Buendía (19996960) (author), Anna Levina (792049) (author)
Published:	2024
Subjects:	Biophysics Neuroscience Sociology Infectious Diseases Biological Sciences not elsewhere classified specific stimulus characteristics network dynamics created introducing specific structures highly recurrent networks favourable correlation structure develop strong preferences receive direct input efficient coding units structured recurrent connectivity neuron receiving input delicately balanced excitation neurons receiving noisy tuning </ p typically noisy readout neuron learn input input selectivity coding efficiency tuned connectivity synaptic connectivity presynaptic connectivity theoretical studies synaptic plasticity structural influences raises questions population activity plasticity rules plasticity mechanisms plastic pre output relationships observations suggest noise signals neurons driven inhibitory populations findings indicate cortical signals
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