scholarly journals A unified theory of E/I synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking

2020 ◽  
Author(s):  
Mauricio Girardi-Schappo ◽  
Emilio F. Galera ◽  
Tawan T. A. Carvalho ◽  
Ludmila Brochini ◽  
Nilton L. Kamiji ◽  
...  
Keyword(s):  
Critical Point ◽  
Spontaneous Activity ◽  
Firing Pattern ◽  
Weight Ratio ◽  
Network Activity ◽  
Inhibitory Synapses ◽  
Static System ◽  
Adaptation Mechanisms ◽  
Neuronal Avalanches ◽  
The Brain

AbstractNeuronal avalanches and asynchronous irregular (AI) firing patterns have been thought to represent distinct frameworks to understand the brain spontaneous activity. The former is typically present in systems where there is a balance between the slow accumulation of tension and its fast dissipation, whereas the latter is accompanied by the balance between synaptic excitation and inhibition (E/I). Here, we develop a new theory of E/I balance that relies on two homeostatic adaptation mechanisms: the short-term depression of inhibition and the spike-dependent threshold increase. First, we turn off the adaptation and show that the so-called static system has a typical critical point commonly attributed to self-organized critical models. Then, we turn on the adaptation and show that the network evolves to a dynamic regime in which: (I) E/I synapses balance regardless of any parameter choice; (II) an AI firing pattern emerges; and (III) neuronal avalanches display visually accurate power laws. This is the first time that these three phenomena appear simultaneously in the same network activity. Thus, we show that the once thought opposing frameworks may be unified into a single dynamics, provided that adaptation mechanisms are in place. In our model, the AI firing pattern is a direct consequence of the hovering close to the critical line where external inputs are compensated by threshold growth, creating synaptic balance for any E/I weight ratio.Author summaryTwo competing frameworks are employed to understand the brain spontaneous activity, both of which are backed by computational and experimental evidence: globally asynchronous and locally irregular (AI) activity arises in excitatory/inhibitory balanced networks subjected to external stimuli, whereas avalanche activity emerge in excitable systems on the critical point between active and inactive states. Here, we develop a new theory for E/I networks and show that there is a state where synaptic balance coexists with AI firing and power-law distributed neuronal avalanches. This regime is achieved through the introducing of short-time depression of inhibitory synapses and spike-dependent threshold adaptation. Thus, the system self-organizes towards the balance point, such that its AI activity arises from quasicritical fluctuations. The need for two independent adaptive mechanisms explains why different dynamical states are observed in the brain.

scholarly journals Extracellular matrix supports excitation-inhibition balance in neuronal networks by stabilizing inhibitory synapses

2020 ◽  
Author(s):  
Egor Dzyubenko ◽  
Michael Fleischer ◽  
Daniel Manrique-Castano ◽  
Mina Borbor ◽  
Christoph Kleinschnitz ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Neuronal Network ◽  
Neuronal Plasticity ◽  
Neuronal Networks ◽  
Network Activity ◽  
Inhibitory Synapses ◽  
Neuronal Network Activity ◽  
The Brain

AbstractMaintaining the balance between excitation and inhibition is essential for the appropriate control of neuronal network activity. Sustained excitation-inhibition (E-I) balance relies on the orchestrated adjustment of synaptic strength, neuronal activity and network circuitry. While growing evidence indicates that extracellular matrix (ECM) of the brain is a crucial regulator of neuronal excitability and synaptic plasticity, it remains unclear whether and how ECM contributes to neuronal circuit stability. Here we demonstrate that the integrity of ECM supports the maintenance of E-I balance by retaining inhibitory connectivity. Depletion of ECM in mature neuronal networks preferentially decreases the density of inhibitory synapses and the size of individual inhibitory postsynaptic scaffolds. After ECM depletion, inhibitory synapse strength homeostatically increases via the reduction of presynaptic GABAB receptors. However, the inhibitory connectivity reduces to an extent that inhibitory synapse scaling is no longer efficient in controlling neuronal network activity. Our results indicate that the brain ECM preserves the balanced network state by stabilizing inhibitory synapses.Significance statementThe question how the brain’s extracellular matrix (ECM) controls neuronal plasticity and network activity is key for an appropriate understanding of brain functioning. In this study, we demonstrate that ECM depletion much more strongly affects the integrity of inhibitory than excitatory synapses in vitro and in vivo. We revealed that by retaining inhibitory connectivity, ECM ensures the efficiency of inhibitory control over neuronal network activity. Our work significantly expands our current state of knowledge about the mechanisms of neuronal network activity regulation. Our findings are similarly relevant for researchers working on the physiological regulation of neuronal plasticity in vitro and in vivo and for researchers studying the remodeling of neuronal networks upon brain injury, where prominent ECM alterations occur.

scholarly journals Beyond resting state neuronal avalanches in the somatosensory barrel cortex

2021 ◽  
Author(s):  
Benedetta Mariani ◽  
Giorgio Nicoletti ◽  
Marta Bisio ◽  
Marta Maschietto ◽  
Roberto Oboe ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Local Field ◽  
Sensory Input ◽  
Barrel Cortex ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Cortical Layers ◽  
The One ◽  
Neuronal Avalanches ◽  
The Brain

Since its first experimental signatures, the so called "critical brain hypothesis" has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both spikes and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both spikes and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to an across-layers synchronization mode that appears to dominate during cortical representation of the single sensory input.

scholarly journals Neuronal Avalanches Across the Rat Somatosensory Barrel Cortex and the Effect of Single Whisker Stimulation

2021 ◽  
Vol 15 ◽  
Author(s):  
Benedetta Mariani ◽  
Giorgio Nicoletti ◽  
Marta Bisio ◽  
Marta Maschietto ◽  
Roberto Oboe ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Local Field ◽  
Sensory Input ◽  
Barrel Cortex ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Whisker Stimulation ◽  
The One ◽  
Neuronal Avalanches ◽  
The Brain

Since its first experimental signatures, the so called “critical brain hypothesis” has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both multi-unit activities and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both multi-unit activities and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to a transient across-layers synchronization mode that appears to dominate the cortical representation of the single sensory input.

scholarly journals CB1 antagonism increases excitatory synaptogenesis in a cortical spheroid model of fetal brain development

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexis Papariello ◽  
David Taylor ◽  
Ken Soderstrom ◽  
Karen Litwa
Keyword(s):  
Brain Development ◽  
Spontaneous Activity ◽  
Microelectrode Array ◽  
Cannabinoid Receptor ◽  
Fetal Brain ◽  
Endocannabinoid System ◽  
Autism Spectrum ◽  
Nervous System Development ◽  
Inhibitory Synapses ◽  
Fetal Brain Development

AbstractThe endocannabinoid system (ECS) plays a complex role in the development of neural circuitry during fetal brain development. The cannabinoid receptor type 1 (CB1) controls synaptic strength at both excitatory and inhibitory synapses and thus contributes to the balance of excitatory and inhibitory signaling. Imbalances in the ratio of excitatory to inhibitory synapses have been implicated in various neuropsychiatric disorders associated with dysregulated central nervous system development including autism spectrum disorder, epilepsy, and schizophrenia. The role of CB1 in human brain development has been difficult to study but advances in induced pluripotent stem cell technology have allowed us to model the fetal brain environment. Cortical spheroids resemble the cortex of the dorsal telencephalon during mid-fetal gestation and possess functional synapses, spontaneous activity, an astrocyte population, and pseudo-laminar organization. We first characterized the ECS using STORM microscopy and observed synaptic localization of components similar to that which is observed in the fetal brain. Next, using the CB1-selective antagonist SR141716A, we observed an increase in excitatory, and to a lesser extent, inhibitory synaptogenesis as measured by confocal image analysis. Further, CB1 antagonism increased the variability of spontaneous activity within developing neural networks, as measured by microelectrode array. Overall, we have established that cortical spheroids express ECS components and are thus a useful model for exploring endocannabinoid mediation of childhood neuropsychiatric disease.

scholarly journals Energy constraints on brain network formation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kosuke Takagi
Keyword(s):  
Network Formation ◽  
Cost Minimization ◽  
Brain Network ◽  
Network Activity ◽  
Rich Functionality ◽  
Energy Constraints ◽  
Dependent Form ◽  
Statistical Similarity ◽  
Simple Ratio ◽  
The Brain

AbstractEnergy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

The role of GABAA receptor biogenesis, structure and function in epilepsy

2006 ◽  
Vol 34 (5) ◽  
pp. 863-867 ◽  
Author(s):  
S. Mizielinska ◽  
S. Greenwood ◽  
C.N. Connolly
Keyword(s):  
Gabaa Receptor ◽  
Structure And Function ◽  
Inhibitory Synapses ◽  
Normal Brain ◽  
Synaptic Targeting ◽  
Acid Type ◽  
Normal Brain Function ◽  
And Function ◽  
The Brain

Maintaining the correct balance in neuronal activation is of paramount importance to normal brain function. Imbalances due to changes in excitation or inhibition can lead to a variety of disorders ranging from the clinically extreme (e.g. epilepsy) to the more subtle (e.g. anxiety). In the brain, the most common inhibitory synapses are regulated by GABAA (γ-aminobutyric acid type A) receptors, a role commensurate with their importance as therapeutic targets. Remarkably, we still know relatively little about GABAA receptor biogenesis. Receptors are constructed as pentameric ion channels, with α and β subunits being the minimal requirement, and the incorporation of a γ subunit being necessary for benzodiazepine modulation and synaptic targeting. Insights have been provided by the discovery of several specific assembly signals within different GABAA receptor subunits. Moreover, a number of recent studies on GABAA receptor mutations associated with epilepsy have further enhanced our understanding of GABAA receptor biogenesis, structure and function.

scholarly journals A supervised machine learning approach to characterize spinal network function

2019 ◽  
Vol 121 (6) ◽  
pp. 2001-2012 ◽  
Author(s):  
A. N. Dalrymple ◽  
S. A. Sharples ◽  
N. Osachoff ◽  
A. P. Lognon ◽  
P. J. Whelan
Keyword(s):  
Machine Learning ◽  
Spinal Cord ◽  
Nervous System ◽  
Spontaneous Activity ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Network Activity ◽  
Multilayer Perceptrons ◽  
Network Development ◽  
Newborn Mice

Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0–P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system.NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

Beyond the Reward Pathway: Coding Reward Magnitude and Error in the Rat Subthalamic Nucleus

2009 ◽  
Vol 102 (4) ◽  
pp. 2526-2537 ◽  
Author(s):  
Sylvie Lardeux ◽  
Remy Pernaud ◽  
Dany Paleressompoulle ◽  
Christelle Baunez
Keyword(s):  
Subthalamic Nucleus ◽  
Firing Pattern ◽  
Sucrose Solution ◽  
Critical Role ◽  
The Other ◽  
Reward Circuitry ◽  
Reward Delivery ◽  
Reward Pathway ◽  
Subthalamic Neurons ◽  
The Brain

It was recently shown that subthalamic nucleus (STN) lesions affect motivation for food, cocaine, and alcohol, differentially, according to either the nature of the reward or the preference for it. The STN may thus code a reward according to its value. Here, we investigated how the firing of subthalamic neurons is modulated during expectation of a predicted reward between two possibilities (4 or 32% sucrose solution). The firing pattern of neurons responding to predictive cues and to reward delivery indicates that STN neurons can be divided into subpopulations responding specifically to one reward and less or giving no response to the other. In addition, some neurons (“oops” neurons) specifically encode errors as they respond only during error trials. These results reveal that the STN plays a critical role in ascertaining the value of the reward and seems to encode that value differently depending on the magnitude of the reward. These data highlight the importance of the STN in the reward circuitry of the brain.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

Differential Involvement of Projection Neurons During Emergence of Spontaneous Activity in the Developing Avian Hindbrain

2009 ◽  
Vol 101 (2) ◽  
pp. 591-602 ◽  
Author(s):  
Hiraku Mochida ◽  
Gilles Fortin ◽  
Jean Champagnat ◽  
Joel C. Glover
Keyword(s):  
Spontaneous Activity ◽  
Projection Neuron ◽  
Projection Neurons ◽  
Charge Coupled Device ◽  
Neuron Populations ◽  
Motor Nerves ◽  
Differential Involvement ◽  
A Charge ◽  
The Brain ◽  
Calcium Green

To better characterize the emergence of spontaneous neuronal activity in the developing hindbrain, spontaneous activity was recorded optically from defined projection neuron populations in isolated preparations of the brain stem of the chicken embryo. Ipsilaterally projecting reticulospinal (RS) neurons and several groups of vestibuloocular (VO) neurons were labeled retrogradely with Calcium Green-1 dextran amine and spontaneous calcium transients were recorded using a charge-coupled-device camera mounted on a fluorescence microscope. Simultaneous extracellular recordings were made from one of the trigeminal motor nerves (nV) to register the occurrence of spontaneous synchronous bursts of activity. Two types of spontaneous activity were observed: synchronous events (SEs), which occurred in register with spontaneous bursts in nV once every few minutes and were tetrodotoxin (TTX) dependent, and asynchronous events (AEs), which occurred in the intervals between SEs and were TTX resistant. AEs occurred developmentally before SEs and were in general smaller and more variable in amplitude than SEs. SEs appeared at the same stage as nV bursts early on embryonic day 4, first in RS neurons and then in VO neurons. All RS neurons participated equally in SEs from the outset, whereas different subpopulations of VO neurons participated differentially, both in terms of the proportion of neurons that exhibited SEs, the fidelity with which the SEs in individual neurons followed the nV bursts, and the developmental stage at which SEs appeared and matured. The results show that spontaneous activity is expressed heterogeneously among hindbrain projection neuron populations, suggesting its differential involvement in the formation of different functional neuronal circuits.

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