Repository logo
 

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Young, Edward 
Gjorgjieva, Julijana 

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 emerge in developing 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 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 center-surround 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.

Description

Keywords

Journal Title

Proceedings of the National Academy of Sciences of the United States of America

Conference Name

Journal ISSN

0027-8424
1091-6490

Volume Title

Publisher

National Academy of Sciences

Publisher DOI

Publisher URL