Control of neural information transmission by synaptic dynamics

Relationship Between Gross Motor Skills and Inhibitory Control in Preschool Children: A Pilot Study

Purpose

Gross motor skills (GMS) and inhibitory control (IC) which are both development in preschool stage is significant for preschooler to healthy growth. However, the evidence of relationship between them in preschoolers are still insufficient, most of studies only focus on youth. Thus, the aim of this research is to examine the association between GMS and IC in preschool children.

Methods

This cross-sectional study used baseline data from a previous intervention study of preschoolers conducted in 2018. GMS were assessed by using the Test for Gross Motor Development (2nd edition) in preschoolers, which includes two subtests of locomotor and object control skills. Total GMS is calculated from the sum of these two subtests. The Fish Flanker task was used to evaluate both accuracy and reaction time of IC. Multivariate linear regression models were established to analyze the relationships between GMS and IC.

Results

A total of 123 preschool-age children (55 girls, 68 boys) were included in the final analysis. After adjusting for confounders, GMS (β = −8.27 ms, 95%CI: −14.2, −2.34), locomotor (β = −11.2 ms, 95%CI: −21.43, −0.97), and object control skills (β = −12.15 ms, 95%CI: −22.07, −2.23) were all negatively related with reaction time of IC.

Conclusion

There was a significant negative correlation between gross motor skills and the reaction time of inhibitory control in preschool children. Further research is needed to verify this finding in prospective and experimental studies.

Effect of Neuromodulation of Short-term Plasticity on Information Processing in Hippocampal Interneuron Synapses

Neurons in a micro-circuit connected by chemical synapses can have their connectivity affected by the prior activity of the cells. The number of synapses available for releasing neurotransmitter can be decreased by repetitive activation through depletion of readily releasable neurotransmitter (NT), or increased through facilitation, where the probability of release of NT is increased by prior activation. These competing effects can create a complicated and subtle range of time-dependent connectivity. Here we investigate the probabilistic properties of facilitation and depression (FD) for a presynaptic neuron that is receiving a Poisson spike train of input. We use a model of FD that is parameterized with experimental data from a hippocampal basket cell and pyramidal cell connection, for fixed frequency input spikes at frequencies in the range of theta (3-8 Hz) and gamma (20-100 Hz) oscillations. Hence our results will apply to micro-circuits in the hippocampus that are responsible for the interaction of theta and gamma rhythms associated with learning and memory. A control situation is compared with one in which a pharmaceutical neuromodulator (muscarine) is employed. We apply standard information-theoretic measures such as entropy and mutual information, and find a closed form approximate expression for the probability distribution of release probability. We also use techniques that measure the dependence of the response on the exact history of stimulation the synapse has received, which uncovers some unexpected differences between control and muscarine-added cases.

Determining synaptic parameters using high-frequency activation

BACKGROUND

The specific properties of a synapse determine how neuronal activity evokes neurotransmitter release. Evaluating changes in synaptic properties during sustained activity is essential to understanding how genetic manipulations and neuromodulators regulate neurotransmitter release. Analyses of postsynaptic responses to high-frequency stimulation have provided estimates of the size of the readily-releasable pool (RRP) of vesicles (N0) and the probability of vesicular release (p) at multiple synapses.

NEW METHOD

Here, we introduce a model-based approach at the calyx of Held synapse in which depletion and the rate of replenishment (R) determine the number of available vesicles, and facilitation leads to a use-dependent increase in p when initial p is low.

RESULTS

When p is high and R is low, we find excellent agreement between estimates based on all three methods and the model. However, when p is low or when significant replenishment occurs between stimuli, estimates of different methods diverge, and model estimates are between the extreme estimates provided by the other approaches.

COMPARISON WITH OTHER METHODS

We compare our model-based approach to three other approaches that rely on different simplifying assumptions. Our findings suggest that our model provides a better estimate of N0 and p than previously-established methods, likely due to inaccurate assumptions about replenishment. More generally, our findings suggest that approaches commonly used to estimate N0 and p at other synapses are often applied under experimental conditions that yield inaccurate estimates.

CONCLUSIONS

Careful application of appropriate methods can greatly improve estimates of synaptic parameters.

Neuromodulatory control of neocortical microcircuits with activity-dependent short-term synaptic depression

A biophysical model of a neocortical microcircuit system is formulated and employed in studies of neuromodulatory control of dynamics and function. The model is based on recent observations of reciprocal connections between pyramidal cells and inhibitory interneurons and incorporates a new type of activity-dependent short-term depression of synaptic couplings recently observed. The model neurons are of a low-dimensional type also accounting for neuronal adaptation, i.e. the coupling between neuronal activity and excitability, which can be regulated by various neuromodulators in the brain. The results obtained demonstrate a capacity for neuromodulatory control of dynamical mode linked to functional mode. The functional aspects considered refer to the observed resolution of multiple objects in working memory as well as the binding of different features for the perception of an object. The effects of neuromodulators displayed by the model are in accordance with many observations on neuromodulatory influence on cognitive functions and brain disorders.

Computational non-linear dynamical psychiatry: a new methodological paradigm for diagnosis and course of illness

The goal of this article is to highlight the significant potential benefits of applying computational mathematical models to the field of psychiatry, specifically in relation to diagnostic conceptualization. The purpose of these models is to augment the current diagnostic categories that utilize a "snapshot" approach to describing mental states. We hope to convey to researchers and clinicians that non-linear dynamics can provide an additional useful longitudinal framework to understand mental illness. Psychiatric phenomena are complex processes that evolve in time, similar to many other processes in nature that have been successfully described and understood within deterministic chaos and non-linear dynamic computational models. Dynamical models describe mental processes and phenomena that change over time, more like a movie than a photograph, with multiple variables interacting over time. The use of these models may help us understand why and how current diagnostic categories are insufficient. They may also provide a new, more descriptive and ultimately more predictive approach leading to better understanding of the interrelationship between psychological, neurobiological, and genetic underpinnings of mental illness.