Structured stabilization in recurrent neural circuits through inhibitory synaptic plasticity

Abstract

In cortical circuits, inhibitory interneurons play a dual role: they regulate overall activity levels to prevent runaway excitation, and contribute to diverse computations. While unstructured inhibitory synaptic connections achieve the first role by homeostatically regulating firing rates, computational tasks often require structured excitatory-inhibitory (E/I) connectivity. Here, we consider a broad class of pairwise inhibitory spike-timing dependent plasticity (iSTDP) rules, demonstrating how inhibitory connections can self-organize to both stabilize excitation and generate functionally relevant connectivity motifs--a process we call "structured stabilization". We show that in both small E/I circuits and large spiking recurrent neural networks the choice of iSTDP rule can lead to either mutually connected E/I pairs, or to lateral inhibition, where an inhibitory neuron connects to an excitatory neuron that does not directly connect back to it. In a one-dimensional ring network with two inhibitory subpopulations following these distinct iSTDP rules, the effective connectivity within the excitatory units self-organizes into a Mexican-hat-like profile, with excitatory influence in the center and inhibitory influence away from the center. This leads to emergent network responses such as contextual modulation effects as in the visual cortex and spatially modular activity characteristic of developmental spontaneous activity. Our theoretical work introduces a family of rules that retains the broad applicability and simplicity of spike-timing-based plasticity, while stabilizing activity and promoting specific connectivity motifs which support emergent network computations. Significance StatementNeural circuits are faced with the dual challenge of keeping activity stable while modifying synaptic strengths during development and learning. A prominent mechanism to keep excitation in check is inhibitory synaptic plasticity. In recurrent circuit models, we show that plasticity rules based on spike timing at synapses from inhibitory to excitatory neurons can stabilize activity while reinforcing specific connectivity motifs. The shape of these rules dictates the resulting motifs, and determines whether inhibitory neurons form strong reciprocal connections with their excitatory partners or lateral connections onto excitatory neurons that do not connect back. In recurrent networks with local excitatory connectivity, these motifs generate surround suppression as in the visual cortex, and long-range spatial correlations characteristic of developmental spontaneous activity.