Excitability and Threshold Mechanism for Enhanced Neuronal Response Induced by Inhibition Preceding Excitation

Geometric characterization of dynamical structure for neural firing activities induced by inhibitory pulse

In general, inhibitory stimuli are thought to inhibit neuronal firing, but they may actually enhance firing sometimes, such as post-inhibitory rebound spike (PIR spike) and post-inhibitory facilitation (PIF) phenomena, which play an important role in human neuronal activities. We study responses to inhibitory pulse in a classical neuron model (Quartic adaptive Integrate-and-fire model) well known to reproduce a number of biologically realistic behaviors. The three phenomena that we study are PIR, in which a neuron fires after an inhibitory pulse, and PIF, in which a subthreshold excitatory input can induce a spike if it is applied with proper timing after an inhibitory pulse, as well as period firing after inhibitory pulse. When the system features focus and saddle two equilibriums, the three phenomena will be occurred under the inhibitory pulse, while all three phenomena will not be induced when the system features node and saddle two equilibriums. Using dynamical systems theory, we explain the threshold mechanism of enhancement of neural firing response induced by inhibitory pulse and analyze the origin of these phenomena from several factors. We also describe the geometric characterization of dynamical structures of these three phenomena. This study therefore enrich the paradoxical phenomena that induced by inhibitory input and advance our understanding of its role.

Rational designing of oscillatory rhythmicity for memory rescue in plasticity-impaired learning networks

In the brain, oscillatory strength embedded in network rhythmicity is important for processing experiences, and this process is disrupted in certain psychiatric disorders. The use of rhythmic network stimuli can change these oscillations and has shown promise in terms of improving cognitive function, although the underlying mechanisms are poorly understood. Here, we combine a two-layer learning model, with experiments involving genetically modified mice, that provides precise control of experience-driven oscillations by manipulating long-term potentiation of excitatory synapses onto inhibitory interneurons (LTP_E→I). We find that, in the absence of LTP_E→I, impaired network dynamics and memory are rescued by activating inhibitory neurons to augment the power in theta and gamma frequencies, which prevents network overexcitation with less inhibitory rebound. In contrast, increasing either theta or gamma power alone was less effective. Thus, inducing network changes at dual frequencies is involved in memory encoding, indicating a potentially feasible strategy for optimizing network-stimulating therapies.

Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem

Spatial hearing allows animals to rapidly detect and localize auditory events in the surrounding environment. The auditory brainstem plays a central role in processing and extracting binaural spatial cues through microsecond-precise binaural integration, especially for detecting interaural time differences (ITDs) of low-frequency sounds at the medial superior olive (MSO). A series of mechanisms exist in the underlying neural circuits for preserving accurate action potential timing across multiple fibers, synapses and nuclei along this pathway. One of these is the myelination of afferent fibers that ensures reliable and temporally precise action potential propagation in the axon. There are several reports of fine-tuned myelination patterns in the MSO circuit, but how specifically myelination influences the precision of sound localization remains incompletely understood. Here we present a spiking neural network (SNN) model of the Mongolian gerbil auditory brainstem with myelinated axons to investigate whether different axon myelination thicknesses alter the sound localization process. Our model demonstrates that axon myelin thickness along the contralateral pathways can substantially modulate ITD detection. Furthermore, optimal ITD sensitivity is reached when the MSO receives contralateral inhibition via thicker myelinated axons compared to contralateral excitation, a result that is consistent with previously reported experimental observations. Our results suggest specific roles of axon myelination for extracting temporal dynamics in ITD decoding, especially in the pathway of the contralateral inhibition.