Control of Synchrony by Inhibitory Motifs in Spiking Neural Networks

Over-representing chain and convergent di-synaptic motifs in the excitatory population of an EI spiking neural network increases synchrony, but inhibitory matching motifs quench it.

• Why it matters: Biological connectivity departs from Erdos-Renyi randomness, and excitatory motif over-representation can raise synchrony, yet cortical activity is typically asynchronous, leaving how synchrony is reduced unclear.
• What they did: The authors systematically vary the prevalence of reciprocal, convergent, divergent, and chain motifs in an Excitatory-Inhibitory spiking network, comparing firing rates and synchrony with and without inhibitory motifs.
• The result: Excess synchrony from excitatory chain and convergent motifs is reduced when the same motifs are introduced among inhibitory neurons, via weakly coupled inhibitory cells driven by uncorrelated external input, lowering both synchrony and firing rates.

AI summary of the abstract · may contain errors · not medical advice · read the paper.