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- W4220781697 abstract "Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between excitatory and inhibitory neurons. However, it remains unclear how the required synaptic connectivity can emerge in circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections – Hebbian learning that is stabilized by the synapse-type-specific competition for synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned neurons and exhibit response normalization and orientation-specific center-surround suppression. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the formation of cortical circuits." @default.
- W4220781697 created "2022-04-03" @default.
- W4220781697 creator A5055788094 @default.
- W4220781697 creator A5071197750 @default.
- W4220781697 date "2022-03-14" @default.
- W4220781697 modified "2023-09-27" @default.
- W4220781697 title "Synapse-type-specific competitive Hebbian learning forms functional recurrent networks" @default.
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