Distinct subtypes of inhibitory interneurons differentially promote the propagation of rate and temporal codes in the feedforward neural network.

Sensory information is believed to be encoded in neuronal spikes using two different neural codes, the rate code (spike firing rate) and the temporal code (precisely-timed spikes). Since the sensory cortex has a highly hierarchical feedforward structure, sensory information-carrying neural codes should reliably propagate across the feedforward network (FFN) of the cortex. Experimental evidence suggests that inhibitory interneurons, such as the parvalbumin-positive (PV) and somatostatin-positive (SST) interneurons, that have distinctively different electrophysiological and synaptic properties, modulate the neural codes during sensory information processing in the cortex. However, how PV and SST interneurons impact on the neural code propagation in the cortical FFN is unknown. We address this question by building a five-layer FFN model consisting of a physiologically realistic Hodgkin-Huxley-type models of excitatory neurons and PV/SST interneurons at different ratios. In response to different firing rate inputs (20-80 Hz), a higher ratio of PV over SST interneurons promoted a reliable propagation of all ranges of firing rate inputs. In contrast, in response to a range of precisely-timed spikes in the form of pulse-packets [with a different number of spikes (α, 40-400 spikes) and degree of dispersion (σ, 0-20 ms)], a higher ratio of SST over PV interneurons promoted a reliable propagation of pulse-packets. Our simulation results show that PV and SST interneurons differentially promote a reliable propagation of the rate and temporal codes, respectively, indicating that the dynamic recruitment of PV and SST interneurons may play critical roles in a reliable propagation of sensory information-carrying neural codes in the cortical FFN.

Citation

Jeongheon Gwak, Jeehyun Kwag. Distinct subtypes of inhibitory interneurons differentially promote the propagation of rate and temporal codes in the feedforward neural network. Chaos (Woodbury, N.Y.). 2020 May;30(5):053102

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PMID: 32491918

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