Abstract
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.
Highlights
Neuronal electrical signals called action potentials are believed to encode sensory information.[1,2,3,4] many different coding schemes have been suggested, generally, two different types of the neural codes are considered to be important: the rate and temporal codes.[5,6] The rate code is represented by the number of spikes in a given time-window called spike firing rate, which is often directly proportional to the intensity of the stimuli, such as the force applied to muscle[1] and light intensity.[7]
When the same asynchronous firing rate inputs were fed into the excitatory neuron (EX)-INPV-feedforward network (FFN) and EX-INSST-FFN models, the propagation characteristics were distinctly different in each model
By modeling the PV and SST interneurons’ electrophysiological properties, such as intrinsic properties, spike adaptation ratio, I-f curve and short-term synaptic plasticity characteristics based on experimental studies[52–58,67,69] (Fig. 1), we show, for the first time, how PV and SST-like interneuron models differentially and dynamically shape the propagation of the rate and temporal codes in the FFN models (Figs. 2–12)
Summary
Neuronal electrical signals called action potentials (spikes) are believed to encode sensory information.[1,2,3,4] many different coding schemes have been suggested, generally, two different types of the neural codes are considered to be important: the rate and temporal codes.[5,6] The rate code is represented by the number of spikes in a given time-window called spike firing rate, which is often directly proportional to the intensity of the stimuli, such as the force applied to muscle[1] and light intensity.[7]. Excitatory postsynaptic potentials (EPSPs) recorded in PV interneurons decrease in amplitude with repetitive presynaptic excitatory inputs, showing short-term synaptic depression; while EPSPs recorded in SST interneurons increase in amplitude with repetitive presynaptic excitatory inputs, showing short-term synaptic facilitation, as measured in cortical PV and SST interneurons in vivo.[45–48] These differing STP properties are suggested to have functional roles in sensory adaptation, sensitization,[42,49] and temporal selectivity of sensory information.[50]. Implementing these biologically realistic spiking and synaptic characteristics of PV and SST interneurons will allow us to better understand the conditions under which neural codes propagate in the cortical FFN for efficient neural information processing. Using the FFN model, we investigated how PV and SST interneurons modulate the propagation of the rate and temporal codes
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