1. Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.
- Author
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Eckmann, Samuel, Young, Edward James, and Gjorgjieva, Julijana
- Subjects
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STIMULUS & response (Psychology) , *MODEL theory , *NEURONS , *NEUROPLASTICITY , *PERCEPTUAL learning - Abstract
Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emergeindevelopingcircuitswheresynapsesbetweenexcitatoryandinhibitoryneurons 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 a limited supply of 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 excitatory and inhibitory neurons and exhibit response normalization and orientation-specific centersurround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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