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

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. Significance Statement Cortical circuits perform diverse computations, primarily determined by highly structured synaptic connectivity patterns that develop during early sensory experience via synaptic plasticity. To understand how these structured connectivity patterns emerge, we introduce a general learning framework for networks of recurrently connected neurons. The framework is rooted in the biologically plausible assumption that synapses compete for limited synaptic resources, which stabilizes synaptic growth. Motivated by the unique protein composition of different synapse types, we assume that different synapse types compete for separate resource pools. Using theory and simulation, we show how this synapse-type-specific competition allows the stable development of structured synaptic connectivity patterns, as well as diverse computations like response normalization and surround suppression.