Spike-frequency adaptation inhibits the pairwise spike correlation.

Introduction: The spike train output correlation with pairwise neurons determines the neural population coding, which depends on the average firing rate of individual neurons. Spike frequency adaptation (SFA), which serves as an essential cellular encoding strategy, modulates the firing rates of individual neurons. However, the mechanism by which the SFA modulates the output correlation of the spike trains remains unclear. Methods: We introduce a pairwise neuron model that receives correlated inputs to generate spike trains, and the output correlation is qualified using Pearson correlation coefficient. The SFA is modeled using adaptation currents to examine its effect on the output correlation. Moreover, we use dynamic thresholds to explore the effect of SFA on output correlation. Furthermore, a simple phenomenological neuron model with a threshold-linear transfer function is utilized to confirm the effect of SFA on decreasing the output correlation. Results: The results show that the adaptation currents decreased the output correlation by reducing the firing rate of a single neuron. At the onset of a correlated input, a transient process shows a decrease in interspike intervals (ISIs), resulting in a temporary increase in the correlation. When the adaptation current is sufficiently activated, the correlation reached a steady state, and the ISIs are maintained at higher values. The enhanced adaptation current achieved by increasing the adaptation conductance further reduces the pairwise correlation. While the time and slide windows influence the correlation, they make no difference in the effect of SFA on decreasing the output correlation. Moreover, SFA simulated by dynamic thresholds also decreases the output correlation. Furthermore, the simple phenomenological neuron model with a threshold-linear transfer function confirms the effect of SFA on decreasing the output correlation. The strength of the signal input and the slope of the linear component of the transfer function, the latter of which can be decreased by SFA, could together modulate the strength of the output correlation. Stronger SFA will decrease the slope and hence decrease the output correlation. Conclusions: The results reveal that the SFA reduces the output correlation with pairwise neurons in the network by reducing the firing rate of individual neurons. This study provides a link between cellular non-linear mechanisms and network coding strategies.

The passive properties of dendrites modulate the propagation of slowly-varying firing rate in feedforward networks.

The propagation of slowly-varying firing rates has been proved significant for the development of the central nervous system. Recent reports have shown that the membrane passive properties of dendrites play a key role in the computation of the single neuron, which is of great importance to the function of neural networks. However, it is still unclear how dendritic passive properties affect the ability of cortical networks to propagate slowly-varying spiking activity. Here, we use two-compartment biophysical models to construct multilayered feedforward neural networks (FFNs) to investigate how dendritic passive properties affect the propagation of the slow-varying inputs. In the two-compartment biophysical models, one compartment represents apical dendrites, and the other compartment describes the soma plus the axon initial segment. Area proportion occupied by somatic compartment and coupling conductance between dendritic and somatic compartments are abstracted to capture the dendritic passive properties. A time-varying signal is injected into the first layer of the FFNs and the fidelity of the signal during propagation is used to qualify the ability of the FFN to transmit wave-like signals. Numerical results reveal an optimal value of coupling conductance between dendritic and somatic compartments to maximize the fidelity of the initial spiking activity. An increase of the dendritic area enhances the initial firing rate of neurons in the first layer by increasing the response of neurons to slow-varying wave-like input, resulting in a delay of attenuation of the firing rate, thus promoting the transmission of signals in FFN. Using a mean-field approach, we examine that changes in area proportion occupied by somatic compartment and coupling conductance between dendritic and somatic compartment affect the signal propagation ability of the FFN by adjusting the input-output transform of a single neuron. With the participation of external noise, a wide range of initial firing rates maintains a unique representation during propagation, which ensures the reliable transmission of slow-varying signals in FFNs. These findings are helpful to understand how passive properties of dendrites participate in the propagation of slowly varying signals in the cerebellum.

Frequency-dependent response in cortical network with periodic electrical stimulation.

Electrical stimulation can shape oscillations in brain activity. However, the mechanism of how periodic electrical stimulation modulates brain oscillations by time-delayed neural networks is poorly understood at present. To address this question, we investigate the effects of periodic stimulations on the oscillations generated via a time-delayed neural network. We specifically study the effect of unipolar and asymmetric bidirectional pulse stimulations by altering amplitude and frequency in a systematic manner. Our findings suggest that electrical stimulations play a central role in altering oscillations in the time-delayed neural network and that these alterations are strongly dependent on the stimulus frequency. We observe that the time-delayed neural network responds differently as the stimulation frequency is altered, as manifested by changes in resonance, entrainment, non-linear oscillation, or oscillation suppression. The results also indicate that the network presents similar response activities with increasing stimulus frequency under different excitation-inhibition ratios. Collectively, our findings pave the way for exploring the potential mechanism underlying the frequency-dependent modulation of network activity via electrical stimulations and provide new insights into possible electrical stimulation therapies to the neurological and psychological disorders in clinical practice.