Frontiers in Neural Circuits最新文献

Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.

Parvalbumin (PV) neurons play an integral role in regulating neural dynamics and plasticity. Therefore, understanding the factors that regulate PV expression is important for revealing modulators of brain function. While the contribution of PV neurons to neural processes has been studied in mammals, relatively little is known about PV function in non-mammalian species, and discerning similarities in the regulation of PV across species can provide insight into evolutionary conservation in the role of PV neurons. Here we investigated factors that affect the abundance of PV in PV neurons in sensory and motor circuits of songbirds and rodents. In particular, we examined the degree to which perineuronal nets (PNNs), extracellular matrices that preferentially surround PV neurons, modulate PV abundance as well as how the relationship between PV and PNN expression differs across brain areas and species and changes over development. We generally found that cortical PV neurons that are surrounded by PNNs (PV+PNN neurons) are more enriched with PV than PV neurons without PNNs (PV-PNN neurons) across both rodents and songbirds. Interestingly, the relationship between PV and PNN expression in the vocal portion of the basal ganglia of songbirds (Area X) differed from that in other areas, with PV+PNN neurons having lower PV expression compared to PV-PNN neurons. These relationships remained consistent across development in vocal motor circuits of the songbird brain. Finally, we discovered a causal contribution of PNNs to PV expression in songbirds because degradation of PNNs led to a diminution of PV expression in PV neurons. These findings reveal a conserved relationship between PV and PNN expression in sensory and motor cortices and across songbirds and rodents and suggest that PV neurons could modulate plasticity and neural dynamics in similar ways across songbirds and rodents.

Autism Spectrum Disorder (ASD) is characterized by rigidity of routines and restricted interests, and atypical social communication and interaction. Recent evidence for altered synchronization of neuro-oscillatory brain activity with regularities in the environment and of altered peripheral nervous system function in ASD present promising novel directions for studying pathophysiology and its relationship to ASD clinical phenotype. Human cognition and action are significantly influenced by physiological rhythmic processes that are generated by both the central nervous system (CNS) and the autonomic nervous system (ANS). Normally, perception occurs in a dynamic context, where brain oscillations and autonomic signals synchronize with external events to optimally receive temporally predictable rhythmic information, leading to improved performance. The recent findings on the time-sensitive coupling between the brain and the periphery in effective perception and successful social interactions in typically developed highlight studying the interactions within the brain–body-environment triad as a critical direction in the study of ASD. Here we offer a novel perspective of autism as a case where the temporal dynamics of brain–body-environment coupling is impaired. We present evidence from the literature to support the idea that in autism the nervous system fails to operate in an adaptive manner to synchronize with temporally predictable events in the environment to optimize perception and behavior. This framework could potentially lead to novel biomarkers of hallmark deficits in ASD such as cognitive rigidity and altered social interaction.

The sub-regions of the hippocampal formation are essential for episodic learning and memory formation, yet the spike dynamics of each region contributing to this function are poorly understood, in part because of a lack of access to the inter-regional communicating axons. Here, we reconstructed hippocampal networks confined to four subcompartments in 2D cultures on a multi-electrode array that monitors individual communicating axons. In our novel device, somal, and axonal activity was measured simultaneously with the ability to ascertain the direction and speed of information transmission. Each sub-region and inter-regional axons had unique power-law spiking dynamics, indicating differences in computational functions, with abundant axonal feedback. After stimulation, spiking, and burst rates decreased in all sub-regions, spikes per burst generally decreased, intraburst spike rates increased, and burst duration decreased, which were specific for each sub-region. These changes in spiking dynamics post-stimulation were found to occupy a narrow range, consistent with the maintenance of the network at a critical state. Functional connections between the sub-region neurons and communicating axons in our device revealed homeostatic network routing strategies post-stimulation in which spontaneous feedback activity was selectively decreased and balanced by decreased feed-forward activity. Post-stimulation, the number of functional connections per array decreased, but the reliability of those connections increased. The networks maintained a balance in spiking and bursting dynamics in response to stimulation and sharpened network routing. These plastic characteristics of the network revealed the dynamic architecture of hippocampal computations in response to stimulation by selective routing on a spatiotemporal scale in single axons.