scholarly journals The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Qi ◽  
Rubin Wang ◽  
Xianfa Jiao ◽  
Ying Du
Keyword(s):  
Coupling Strength ◽  
Neural Coding ◽  
Inhibitory Neuron ◽  
Higher Order ◽  
Inhibitory Neurons ◽  
Neural Oscillator ◽  
Number Density ◽  
External Stimulation ◽  
Average Number Density ◽  
Inhibitory Coupling

We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution.

scholarly journals Implications of Extended Inhibitory Neuron Development

2021 ◽  
Vol 22 (10) ◽  
pp. 5113
Author(s):  
Jae-Yeon Kim ◽  
Mercedes F. Paredes
Keyword(s):  
Neural Circuit ◽  
Inhibitory Neuron ◽  
Postnatal Period ◽  
Specific Pattern ◽  
Inhibitory Neurons ◽  
Gabaergic Interneuron ◽  
Gabaergic Interneurons ◽  
Neuron Development ◽  
Cortical Regions ◽  
Circuit Formation

A prolonged developmental timeline for GABA (γ-aminobutyric acid)-expressing inhibitory neurons (GABAergic interneurons) is an amplified trait in larger, gyrencephalic animals. In several species, the generation, migration, and maturation of interneurons take place over several months, in some cases persisting after birth. The late integration of GABAergic interneurons occurs in a region-specific pattern, especially during the early postnatal period. These changes can contribute to the formation of functional connectivity and plasticity, especially in the cortical regions responsible for higher cognitive tasks. In this review, we discuss GABAergic interneuron development in the late gestational and postnatal forebrain. We propose the protracted development of interneurons at each stage (neurogenesis, neuronal migration, and network integration), as a mechanism for increased complexity and cognitive flexibility in larger, gyrencephalic brains. This developmental feature of interneurons also provides an avenue for environmental influences to shape neural circuit formation.

scholarly journals Amyloid-β Oligomers Induce Only Mild Changes to Inhibitory Bouton Dynamics

2021 ◽  
Vol 5 (1) ◽  
pp. 153-160 ◽  
Author(s):  
Marvin Ruiter ◽  
Christine Lützkendorf ◽  
Jian Liang ◽  
Corette J. Wierenga
Keyword(s):  
Hippocampal Slices ◽  
Inhibitory Neuron ◽  
Amyloid Β ◽  
Inhibitory Neurons ◽  
Inhibitory Synapses ◽  
Amyloid Β Protein ◽  
Protein Precursor ◽  
Β Protein ◽  
Plaque Load ◽  
Early Alzheimer’S Disease

The amyloid-β protein precursor is highly expressed in a subset of inhibitory neuron in the hippocampus, and inhibitory neurons have been suggested to play an important role in early Alzheimer’s disease plaque load. Here we investigated bouton dynamics in axons of hippocampal interneurons in two independent amyloidosis models. Short-term (24 h) amyloid-β (Aβ)-oligomer application to organotypic hippocampal slices slightly increased inhibitory bouton dynamics, but bouton density and dynamics were unchanged in hippocampus slices of young-adult AppNL - F - G-mice, in which Aβ levels are chronically elevated. These results indicate that loss or defective adaptation of inhibitory synapses are not a major contribution to Aβ-induced hyperexcitability.

scholarly journals Lateral entorhinal cortex inputs modulate hippocampal dendritic excitability by recruiting a local disinhibitory microcircuit

2022 ◽  
Author(s):  
Olesia M Bilash ◽  
Spyridon Chavlis ◽  
Panayiota Poirazi ◽  
Jayeeta Basu
Keyword(s):  
Entorhinal Cortex ◽  
Pyramidal Neurons ◽  
Inhibitory Neuron ◽  
Inhibitory Neurons ◽  
Environmental Cues ◽  
Memory Processing ◽  
Ca1 Pyramidal Neurons ◽  
Dendritic Spikes ◽  
Hippocampal Area ◽  
Insight Into

The lateral entorhinal cortex (LEC) provides information about multi-sensory environmental cues to the hippocampus through direct inputs to the distal dendrites of CA1 pyramidal neurons. A growing body of work suggests that LEC neurons perform important functions for episodic memory processing, coding for contextually-salient elements of an environment or the experience within it. However, we know little about the functional circuit interactions between LEC and the hippocampus. In this study, we combine functional circuit mapping and computational modeling to examine how long-range glutamatergic LEC projections modulate compartment-specific excitation-inhibition dynamics in hippocampal area CA1. We demonstrate that glutamatergic LEC inputs can drive local dendritic spikes in CA1 pyramidal neurons, aided by the recruitment of a disinhibitory vasoactive intestinal peptide (VIP)-expressing inhibitory neuron microcircuit. Our circuit mapping further reveals that, in parallel, LEC also recruits cholecystokinin (CCK)-expressing inhibitory neurons, which our model predicts act as a strong suppressor of dendritic spikes. These results provide new insight into a cortically-driven GABAergic microcircuit mechanism that gates non-linear dendritic computations, which may support compartment-specific coding of multi-sensory contextual features within the hippocampus.

Influence of Hopf Bifurcation Dynamics on Conduction Failure of Action Potentials Along Nerve C-Fiber

2019 ◽  
Vol 29 (07) ◽  
pp. 1950093 ◽  
Author(s):  
Xinjing Zhang ◽  
Huaguang Gu
Keyword(s):  
Hopf Bifurcation ◽  
Coupling Strength ◽  
The Other ◽  
Dimensional Parameter ◽  
External Stimulation ◽  
C Fiber ◽  
Current Threshold ◽  
Stimulation Period ◽  
Conduction Failure ◽  
A Chain

Contrary to faithful conduction of every action potential or spike along the axon, some spikes induced by the external stimulation with a high frequency at one end of the unmyelinated nerve fiber (C-fiber) disappear during the conduction process to the other end, which leads to conduction failure. Many physiological functions such as information coding or pathological pain are involved. In the present paper, the dynamic mechanism of the conduction failure is well interpreted by two characteristics of the focus near Hopf bifurcation of the Hodgkin–Huxley (HH) model. One is that the current threshold to evoke a spike from the after-potential corresponding to the focus exhibits damping oscillations, and the other is that the damping oscillations exhibit an internal period. A chain network model composed of HH neurons and stimulated by the external periodic stimulation is used to stimulate C-fiber. In the two-dimensional parameter space of the stimulation period and coupling strength, the conduction failure appears for the coupling strength lower than that of the faithful conduction, which is due to some maximal values of the coupling current for low coupling strength not being strong enough to evoke spikes, and the coupling strength threshold between the faithful conduction and conduction failure exhibiting damping oscillations with respect to the stimulation period, due to the damping oscillations of the current threshold. The damping oscillations of the coupling strength exhibit close correlations to those of the current threshold. The coupling strength for the conduction failure exhibits maximal values as the stimulation period is approximated to 1-, 2-, 3- or 4-times of the internal period and the maximal values decrease with increasing stimulation period. In addition, the correspondence between the simulation results and the previous experimental observations is discussed. The results present deep insights into the dynamics of the conduction failure with Hopf bifurcation and are helpful to investigate the influence of other modulation factors on the conduction failure.

Epileptiform activity in microcultures containing one excitatory hippocampal neuron

1991 ◽  
Vol 65 (4) ◽  
pp. 761-770 ◽  
Author(s):  
M. M. Segal
Keyword(s):  
Action Potentials ◽  
Epileptiform Activity ◽  
Inhibitory Neuron ◽  
Excitatory Neuron ◽  
Inhibitory Neurons ◽  
Gamma Aminobutyric Acid ◽  
Intracellular Recordings ◽  
Voltage Fluctuations ◽  
Excitatory Neurons ◽  
Small Voltage

1. Paroxysmal depolarizing shifts (PDSs) occur during interictal epileptiform activity. Sustained depolarizations are characteristic of ictal activity, and events resembling PDSs also occur during the sustained depolarizations. To study these elements of epileptiform activity in a simpler context, I used the in vitro chronic-excitatory-block model of epilepsy of Furshpan and Potter and the microculture technique of Segal and Furshpan. 2. Intracellular recordings were made from 93 single-neuron microcultures. Forty of these solitary neurons were excitatory, their action potentials were replaced by PDS-like events or sustained depolarizations as kynurenate was removed from the perfusion solution. PDS-like events were similar to PDSs in intact cortex, mass cultures, and microcultures with more than one neuron. Small voltage fluctuations were also seen in solitary excitatory neurons in the absence of recorded action potentials. Sustained depolarizations developed in 5 of the 40 excitatory neurons. Forty-eight of the 93 solitary neurons were inhibitory, with bicuculline-sensitive hyperpolarizations after the action potential (ascribable to gamma-aminobutyric acid-A autapses). None of the solitary inhibitory neurons displayed sustained depolarizations. Five of the 93 neurons were insensitive to both kynurenate and bicuculline and were not placed in either the excitatory or the inhibitory category. 3. Both N-methyl-D-aspartate (NMDA) and non-NMDA glutamate receptors contributed to the PDS-like events and sustained depolarizations. Only a non-NMDA glutamate receptor component was evident for the small voltage fluctuations. 4. Intracellular recordings were also made from two-neuron microcultures, each containing one excitatory neuron and one inhibitory neuron. Sustained depolarizations developed in five microcultures, in each case only in the excitatory neuron.

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 Two dynamically distinct circuits driving inhibition in sensory thalamus

2020 ◽  
Author(s):  
Rosa I. Martinez-Garcia ◽  
Bettina Voelcker ◽  
Julia B. Zaltsman ◽  
Saundra L. Patrick ◽  
Tanya R. Stevens ◽  
...  
Keyword(s):  
Sensory Information ◽  
Cell Types ◽  
Higher Order ◽  
Inhibitory Neurons ◽  
Thalamic Reticular Nucleus ◽  
Reticular Nucleus ◽  
Order Structure ◽  
Multiple Sources ◽  
Cell Groups ◽  
And Function

AbstractMost sensory information destined for the neocortex is relayed through the thalamus, where considerable transformation occurs1,2. One powerful means of transformation involves interactions between excitatory thalamocortical neurons that carry data to cortex and inhibitory neurons of the thalamic reticular nucleus (TRN) that regulate flow of those data3-6. Despite enduring recognition of its importance7-9, understanding of TRN cell types, their organization, and their functional properties has lagged that of the thalamocortical systems they control.Here we address this, investigating somatosensory and visual circuits of the TRN. In the somatosensory TRN we observed two groups of genetically defined neurons that are topographically segregated, physiologically distinct, and connect reciprocally with independent thalamocortical nuclei via dynamically divergent synapses. Calbindin-expressing cells, located in the central core, connect with the ventral posterior nucleus (VP), the primary somatosensory thalamocortical relay. In contrast, somatostatin-expressing cells, residing along the surrounding edges of TRN, synapse with the posterior medial thalamic nucleus (POM), a higher-order structure that carries both top-down and bottom-up information10-12. The two TRN cell groups process their inputs in pathway-specific ways. Synapses from VP to central TRN cells transmit rapid excitatory currents that depress deeply during repetitive activity, driving phasic spike output. Synapses from POM to edge TRN cells evoke slower, less depressing excitatory currents that drive more persistent spiking. Differences in intrinsic physiology of TRN cell types, including state-dependent bursting, contribute to these output dynamics. Thus, processing specializations of two somatosensory TRN subcircuits appear to be tuned to the signals they carry—a primary central subcircuit to discrete sensory events, and a higher-order edge subcircuit to temporally distributed signals integrated from multiple sources. The structure and function of visual TRN subcircuits closely resemble those of the somatosensory TRN. These results provide fundamental insights about how subnetworks of TRN neurons may differentially process distinct classes of thalamic information.

scholarly journals Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
B Semihcan Sermet ◽  
Pavel Truschow ◽  
Michael Feyerabend ◽  
Johannes M Mayrhofer ◽  
Tess B Oram ◽  
...  
Keyword(s):  
Thalamic Nucleus ◽  
Barrel Cortex ◽  
Sensory Information ◽  
Cell Types ◽  
Excitatory Input ◽  
Higher Order ◽  
Inhibitory Neurons ◽  
Thalamic Nuclei ◽  
Thalamic Input ◽  
Layer 4

Mouse primary somatosensory barrel cortex (wS1) processes whisker sensory information, receiving input from two distinct thalamic nuclei. The first-order ventral posterior medial (VPM) somatosensory thalamic nucleus most densely innervates layer 4 (L4) barrels, whereas the higher-order posterior thalamic nucleus (medial part, POm) most densely innervates L1 and L5A. We optogenetically stimulated VPM or POm axons, and recorded evoked excitatory postsynaptic potentials (EPSPs) in different cell-types across cortical layers in wS1. We found that excitatory neurons and parvalbumin-expressing inhibitory neurons received the largest EPSPs, dominated by VPM input to L4 and POm input to L5A. In contrast, somatostatin-expressing inhibitory neurons received very little input from either pathway in any layer. Vasoactive intestinal peptide-expressing inhibitory neurons received an intermediate level of excitatory input with less apparent layer-specificity. Our data help understand how wS1 neocortical microcircuits might process and integrate sensory and higher-order inputs.

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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