Synapse-type-specific competitive Hebbian learning forms functional recurrent networks
成果类型：
Article
署名作者：
Eckmann, Samuel; Young, Edward James; Gjorgjieva, Julijana
署名单位：
Max Planck Society; University of Cambridge; Technical University of Munich
刊物名称：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN：
0027-13256
DOI：
10.1073/pnas.2305326121
发表日期：
2024-06-18
页码：
1-12
关键词：
cross-orientation suppression
timing-dependent plasticity
critical-period plasticity
ferret visual-cortex
inhibitory interneurons
striate cortex
gabaergic interneurons
thalamocortical input
direction selectivity
cortical networks
摘要：
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 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.