scholarly journals Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition

2021 ◽  
Vol 17 (6) ◽  
pp. e1009163
Author(s):  
Yuanhong Tang ◽  
Lingling An ◽  
Quan Wang ◽  
Jian K. Liu
Keyword(s):  
Gap Junctions ◽  
Granular Layer ◽  
Cerebellar Granule Cells ◽  
Feedback Inhibition ◽  
Mossy Fibers ◽  
Inhibitory Neurons ◽  
Neural Population ◽  
Different Types ◽  
Synchronous Oscillations ◽  
Oscillation Dynamics

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.

scholarly journals Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

2018 ◽  
Vol 115 (45) ◽  
pp. 11619-11624 ◽  
Author(s):  
Wei P. Dai ◽  
Douglas Zhou ◽  
David W. McLaughlin ◽  
David Cai
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Orientation Tuning ◽  
Response Properties ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cortical Excitation ◽  
Contrast Invariance

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

Gap Junctions, Neural Population Models and

2014 ◽  
pp. 1-7
Author(s):  
Moira Steyn-Ross ◽  
D. Alistair Steyn-Ross ◽  
Jamie Sleigh
Keyword(s):  
Gap Junctions ◽  
Population Models ◽  
Neural Population

scholarly journals Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

2020 ◽  
Author(s):  
Belle Liu ◽  
Alexander James White ◽  
Chung-Chuan Lo
Keyword(s):  
Dynamical Systems ◽  
Bifurcation Theory ◽  
Neural Circuits ◽  
Feedback Inhibition ◽  
Central Pattern Generators ◽  
Recurrent Network ◽  
Inhibitory Neurons ◽  
Mutual Inhibition ◽  
Input Type ◽  
Pattern Generators

AbstractOne of the most intriguing observations of recurrent neural circuits is their flexibility. Seemingly, this flexibility extends far beyond the ability to learn, but includes the ability to use learned procedures to respond to novel situations. Here, we report that this flexibility arises from the synergistic interplay between recurrent mutual excitation and recurrent mutual inhibition. Specifically, we show that mutual inhibition is critical in expanding the functionality of the circuit, far beyond what feedback inhibition alone can accomplish. By taking advantage of dynamical systems theory and bifurcation analysis, we show mutual inhibition doubles the number of cusp bifurcations in the system in small neural circuits. As a concrete example, we build a simulation model of a class of functional motifs we call Coupled Recurrent inhibitory and Recurrent excitatory Loops (CRIRELs). These CRIRELs have the advantage of being multi-functional, performing a plethora of functions, including decisions, switches, toggles, central pattern generators, depending solely on the input type. We then use bifurcation theory to show how mutual inhibition gives rise to this broad repertoire of possible functions. Finally, we demonstrate how this trend also holds for larger networks, and how mutual inhibition greatly expands the amount of information a recurrent network can hold.

scholarly journals What are the roles of the many different types of potassium channel expressed in cerebellar granule cells?

2003 ◽  
Vol 2 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Alistair Mathie ◽  
Emma L Veale ◽  
Catherine E Clarke ◽  
Kishani M Ranatunga
Keyword(s):  
Potassium Channel ◽  
Granule Cells ◽  
Cerebellar Granule Cells ◽  
Cerebellar Granule ◽  
Different Types ◽  
The Many

Cell junctions in the developing compound eye of the desert locust Schistocerca gregaria

Development ◽  
1976 ◽  
Vol 36 (2) ◽  
pp. 409-423
Author(s):  
S. Eley ◽  
P. M. J. Shelton
Keyword(s):  
Gap Junctions ◽  
Compound Eye ◽  
Schistocerca Gregaria ◽  
Intercellular Junctions ◽  
Cell Junctions ◽  
Different Types ◽  
Undifferentiated Cells ◽  
Development Gap ◽  
Sequential Appearance ◽  
Subsequent Disappearance

Intercellular junctions in the developing retina of the locust Schistocerca gregaria have been examined by electron microscopy. Different types of junction appear in a well-defined sequence during development. Five stages of ommatidial development are described. Close junctions and punctate junctions are present throughout development. Gap junctions appear transiently amongst the undifferentiated cells, before clearly defined preommatidia can be distinguished. The subsequent disappearance of gap junctions may be correlated with cell determination. Lanthanum studies confirm these findings. The later sequential appearance of adhesive junction types is described. These include septate desmosomes and two types of desmosomes. In the fully differentiated ommatidium only two types of junction remain, these are: desmosomes and rhabdomeric junctions.

scholarly journals Hyperactive mTOR signals in the proopiomelanocortin-expressing hippocampal neurons cause age-dependent epilepsy and premature death in mice

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yuki Matsushita ◽  
Yasunari Sakai ◽  
Mitsunori Shimmura ◽  
Hiroshi Shigeto ◽  
Miki Nishio ◽  
...  
Keyword(s):  
Hippocampal Neurons ◽  
Focal Cortical Dysplasia ◽  
Mossy Fibers ◽  
Premature Death ◽  
Mtor Pathway ◽  
Conditional Knockout ◽  
Inhibitory Neurons ◽  
Sequencing Data ◽  
Functional Roles ◽  
Synaptic Markers

Abstract Epilepsy is a frequent comorbidity in patients with focal cortical dysplasia (FCD). Recent studies utilizing massive sequencing data identified subsets of genes that are associated with epilepsy and FCD. AKT and mTOR-related signals have been recently implicated in the pathogenic processes of epilepsy and FCD. To clarify the functional roles of the AKT-mTOR pathway in the hippocampal neurons, we generated conditional knockout mice harboring the deletion of Pten (Pten-cKO) in Proopiomelanocortin-expressing neurons. The Pten-cKO mice developed normally until 8 weeks of age, then presented generalized seizures at 8–10 weeks of age. Video-monitored electroencephalograms detected paroxysmal discharges emerging from the cerebral cortex and hippocampus. These mice showed progressive hypertrophy of the dentate gyrus (DG) with increased expressions of excitatory synaptic markers (Psd95, Shank3 and Homer). In contrast, the expression of inhibitory neurons (Gad67) was decreased at 6–8 weeks of age. Immunofluorescence studies revealed the abnormal sprouting of mossy fibers in the DG of the Pten-cKO mice prior to the onset of seizures. The treatment of these mice with an mTOR inhibitor rapamycin successfully prevented the development of seizures and reversed these molecular phenotypes. These data indicate that the mTOR pathway regulates hippocampal excitability in the postnatal brain.

scholarly journals Inhibitory control of the excitatory/inhibitory balance in psychiatric disorders

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 23 ◽  
Author(s):  
Martijn Selten ◽  
Hans van Bokhoven ◽  
Nael Nadif Kasri
Keyword(s):  
Psychiatric Disorders ◽  
Relative Strength ◽  
Inhibitory Neurons ◽  
Specific Class ◽  
Post Mortem ◽  
Inhibitory System ◽  
Network Function ◽  
Different Types ◽  
Flow Of Information

Neuronal networks consist of different types of neurons that all play their own role in order to maintain proper network function. The two main types of neurons segregate in excitatory and inhibitory neurons, which together regulate the flow of information through the network. It has been proposed that changes in the relative strength in these two opposing forces underlie the symptoms observed in psychiatric disorders, including autism and schizophrenia. Here, we review the role of alterations to the function of the inhibitory system as a cause of psychiatric disorders. First, we explore both patient and post-mortem evidence of inhibitory deficiency. We then discuss the function of different interneuron subtypes in the network and focus on the central role of a specific class of inhibitory neurons, parvalbumin-positive interneurons. Finally, we discuss genes known to be affected in different disorders and the effects that mutations in these genes have on the inhibitory system in cortex and hippocampus. We conclude that alterations to the inhibitory system are consistently identified in animal models of psychiatric disorders and, more specifically, that mutations affecting the function of parvalbumin-positive interneurons seem to play a central role in the symptoms observed in these disorders.

scholarly journals Simultaneous silence organizes structured higher-order interactions in neural populations

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Hideaki Shimazaki ◽  
Kolia Sadeghi ◽  
Tomoe Ishikawa ◽  
Yuji Ikegaya ◽  
Taro Toyoizumi
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Hippocampal Slices ◽  
Higher Order ◽  
Inhibitory Neurons ◽  
Neural Population ◽  
Biological Mechanisms ◽  
Individual Activity ◽  
Pairwise Correlations ◽  
Neural Populations

Abstract Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.

scholarly journals On the Validity of Neural Mass Models

2021 ◽  
Vol 14 ◽  
Author(s):  
Nicolás Deschle ◽  
Juan Ignacio Gossn ◽  
Prejaas Tewarie ◽  
Björn Schelter ◽  
Andreas Daffertshofer
Keyword(s):  
Mean Field ◽  
Low Frequency ◽  
Inhibitory Neuron ◽  
Inhibitory Neurons ◽  
Neural Population ◽  
Mass Model ◽  
Time Scale Separation ◽  
Neural Mass ◽  
The Mean ◽  
Parameter Ranges

Modeling the dynamics of neural masses is a common approach in the study of neural populations. Various models have been proven useful to describe a plenitude of empirical observations including self-sustained local oscillations and patterns of distant synchronization. We discuss the extent to which mass models really resemble the mean dynamics of a neural population. In particular, we question the validity of neural mass models if the population under study comprises a mixture of excitatory and inhibitory neurons that are densely (inter-)connected. Starting from a network of noisy leaky integrate-and-fire neurons, we formulated two different population dynamics that both fall into the category of seminal Freeman neural mass models. The derivations contained several mean-field assumptions and time scale separation(s) between membrane and synapse dynamics. Our comparison of these neural mass models with the averaged dynamics of the population reveals bounds in the fraction of excitatory/inhibitory neuron as well as overall network degree for a mass model to provide adequate estimates. For substantial parameter ranges, our models fail to mimic the neural network's dynamics proper, be that in de-synchronized or in (high-frequency) synchronized states. Only around the onset of low-frequency synchronization our models provide proper estimates of the mean potential dynamics. While this shows their potential for, e.g., studying resting state dynamics obtained by encephalography with focus on the transition region, we must accept that predicting the more general dynamic outcome of a neural network via its mass dynamics requires great care.

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