Carbon Nanotubes, Directly Grown on Supporting Surfaces, Improve Neuronal Activity in Hippocampal Neuronal Networks

2019 ◽  
Vol 3 (5) ◽  
pp. 1800286 ◽  
Author(s):  
Ilaria Rago ◽  
Rossana Rauti ◽  
Manuela Bevilacqua ◽  
Ivo Calaresu ◽  
Alessandro Pozzato ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Neuronal Activity ◽  
Neuronal Networks

An integrated microfluidic/microelectrode array for the study of activity-dependent intracellular dynamics in neuronal networks

Lab on a Chip ◽  
2018 ◽  
Vol 18 (22) ◽  
pp. 3425-3435 ◽  
Author(s):  
Eve Moutaux ◽  
Benoit Charlot ◽  
Aurélie Genoux ◽  
Frédéric Saudou ◽  
Maxime Cazorla
Keyword(s):  
Neuronal Activity ◽  
Neuronal Networks ◽  
Microelectrode Array ◽  
Activity Dependent

A microfluidics/MEA platform was developed to control neuronal activity while imaging intracellular dynamics within reconstituted neuronal networks.

scholarly journals Deuterated Glutamate-Mediated Neuronal Activity on Micro-Electrode Arrays

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 830
Author(s):  
Wataru Minoshima ◽  
Kyoko Masui ◽  
Tomomi Tani ◽  
Yasunori Nawa ◽  
Satoshi Fujita ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Neuronal Activity ◽  
Hippocampal Neurons ◽  
Neuronal Networks ◽  
Microelectrode Array ◽  
Mammalian Brain ◽  
Electrode Arrays ◽  
Physiological Properties ◽  
Micro Electrode Arrays

The excitatory synaptic transmission is mediated by glutamate (GLU) in neuronal networks of the mammalian brain. In addition to the synaptic GLU, extra-synaptic GLU is known to modulate the neuronal activity. In neuronal networks, GLU uptake is an important role of neurons and glial cells for lowering the concentration of extracellular GLU and to avoid the excitotoxicity. Monitoring the spatial distribution of intracellular GLU is important to study the uptake of GLU, but the approach has been hampered by the absence of appropriate GLU analogs that report the localization of GLU. Deuterium-labeled glutamate (GLU-D) is a promising tracer for monitoring the intracellular concentration of glutamate, but physiological properties of GLU-D have not been studied. Here we study the effects of extracellular GLU-D for the neuronal activity by using primary cultured rat hippocampal neurons that form neuronal networks on microelectrode array. The frequency of firing in the spontaneous activity of neurons increased with the increasing concentration of extracellular GLU-D. The frequency of synchronized burst activity in neurons increased similarly as we observed in the spontaneous activity. These changes of the neuronal activity with extracellular GLU-D were suppressed by antagonists of glutamate receptors. These results suggest that GLU-D can be used as an analog of GLU with equivalent effects for facilitating the neuronal activity. We anticipate GLU-D developing as a promising analog of GLU for studying the dynamics of glutamate during neuronal activity.

scholarly journals Single Layer Graphene Promotes Neuronal Activity by Regulating Potassium Ion Channels in Cultured Neuronal Networks

2018 ◽  
Vol 114 (3) ◽  
pp. 393a
Author(s):  
Niccolò Paolo Pampaloni ◽  
Martin Lottner ◽  
Michele Giugliano ◽  
Alessia Matruglio ◽  
Francesco D'Amico ◽  
...  
Keyword(s):  
Ion Channels ◽  
Neuronal Activity ◽  
Neuronal Networks ◽  
Single Layer ◽  
Potassium Ion ◽  
Single Layer Graphene ◽  
Potassium Ion Channels ◽  
Layer Graphene

scholarly journals Carbon based substrates for interfacing neurons: Comparing pristine with functionalized carbon nanotubes effects on cultured neuronal networks

Carbon ◽  
2016 ◽  
Vol 97 ◽  
pp. 87-91 ◽  
Author(s):  
Susanna Bosi ◽  
Alessandra Fabbro ◽  
Cristina Cantarutti ◽  
Marko Mihajlovic ◽  
Laura Ballerini ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Neuronal Networks ◽  
Functionalized Carbon Nanotubes ◽  
Carbon Based

scholarly journals Growth Rules for the Repair of Asynchronous Irregular Neuronal Networks after Peripheral Lesions

2019 ◽  
Author(s):  
Ankur Sinha ◽  
Christoph Metzner ◽  
Neil Davey ◽  
Roderick Adams ◽  
Michael Schmuker ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Network Model ◽  
Neuronal Networks ◽  
Structural Plasticity ◽  
Activity Levels ◽  
Peripheral Lesion ◽  
Simulation Results ◽  
Dependent Growth ◽  
Activity Dependent ◽  
The Brain

AbstractSeveral homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model, simulations of which accurately reproduce network rewiring after peripheral lesions as reported in experiments, to study these activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we deafferent a biologically realistic network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex.Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed model of homeostatic structural plasticity and the inhibitory synaptic plasticity mechanism that also balances our AI network are both necessary for successful rewiring of the network.Author summaryAn accumulating body of evidence suggests that our brain can compensate for peripheral lesions by adaptive rewiring of its neuronal circuitry. The underlying process, structural plasticity, can modify the connectivity of neuronal networks in the brain, thus affecting their function. To better understand the mechanisms of structural plasticity in the brain, we have developed a novel model of peripheral lesions and the resulting activity-dependent rewiring in a simplified cortical network model that exhibits biologically realistic asynchronous irregular activity. In order to accurately reproduce the directionality and time course of rewiring after injury that is observed in peripheral lesion experiments, we derive activity dependent growth rules for different synaptic elements: dendritic and axonal contacts. Our simulation results suggest that excitatory and inhibitory synaptic elements have to react to changes in neuronal activity in opposite ways. We show that these rules result in a homeostatic stabilisation of activity in individual neurons. In our simulations, both synaptic and structural plasticity mechanisms are necessary for network repair. Furthermore, our simulations indicate that while activity is restored in neurons deprived by the peripheral lesion, the temporal firing characteristics of the network can be changed by the rewiring process.

Seizures

2016 ◽  
pp. 147-184
Author(s):  
Douglas J. Gelb
Keyword(s):  
Neuronal Activity ◽  
Neuronal Networks ◽  
Clinical Manifestations ◽  
Cerebral Hemispheres ◽  
Protective Mechanisms ◽  
Unprovoked Seizure ◽  
Seizure Focus ◽  
Systemic Factors ◽  
Large Groups

The clinical manifestations of a seizure depend on where the abnormal neuronal activity starts (the seizure focus) and how it spreads. Some seizures have no apparent focus—from the very beginning, they seem to involve neuronal networks spread throughout both cerebral hemispheres, called general seizures. Patients with seizures do not necessarily have epilepsy. Epilepsy represents a failure of the protective mechanisms that usually inhibit large groups of neurons from engaging in repetitive, entrained activity. Patients who present with seizures can be grouped into three broad categories: (1) those whose seizures are precipitated by clearly identified systemic factors, (2) those with epilepsy, and (3) those who experience a single unprovoked seizure with no evidence of predisposition to recurrent seizures.

scholarly journals The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

2019 ◽  
Author(s):  
Matthieu Gilson ◽  
David Dahmen ◽  
Rubén Moreno-Bote ◽  
Andrea Insabato ◽  
Moritz Helias
Keyword(s):  
Time Series ◽  
Neuronal Activity ◽  
Neuronal Networks ◽  
Temporal Structure ◽  
Second Order ◽  
Time Lags ◽  
New Paradigm ◽  
Experimental Conditions ◽  
Deterministic Function ◽  
The Mean

AbstractLearning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the visual system, where, for example, ON/OFF cells fire or not depending on the contrast in their receptive fields. Classical models of neuronal networks therefore map a set of input signals to a set of activity levels in the output of the network. Each category of inputs is thereby predominantly characterized by its mean. In the case of time series, fluctuations around this mean constitute noise in this view. For this paradigm, the high variability exhibited by the cortical activity may thus imply limitations or constraints, which have been discussed for many years. For example, the need for averaging neuronal activity over long periods or large groups of cells to assess a robust mean and to diminish the effect of noise correlations. To reconcile robust computations with variable neuronal activity, we here propose a conceptual change of perspective by employing variability of activity as the basis for stimulus-related information to be learned by neurons, rather than merely being the noise that corrupts the mean signal. In this new paradigm both afferent and recurrent weights in a network are tuned to shape the input-output mapping for covariances, the second-order statistics of the fluctuating activity. When including time lags, covariance patterns define a natural metric for time series that capture their propagating nature. We develop the theory for classification of time series based on their spatio-temporal covariances, which reflect dynamical properties. We demonstrate that recurrent connectivity is able to transform information contained in the temporal structure of the signal into spatial covariances. Finally, we use the MNIST database to show how the covariance perceptron can capture specific second-order statistical patterns generated by moving digits.Author summaryThe dynamics in cortex is characterized by highly fluctuating activity: Even under the very same experimental conditions the activity typically does not reproduce on the level of individual spikes. Given this variability, how then does the brain realize its quasi-deterministic function? One obvious solution is to compute averages over many cells, assuming that the mean activity, or rate, is actually the decisive signal. Variability across trials of an experiment is thus considered noise. We here explore the opposite view: Can fluctuations be used to actually represent information? And if yes, is there a benefit over a representation using the mean rate? We find that a fluctuation-based scheme is not only powerful in distinguishing signals into several classes, but also that networks can efficiently be trained in the new paradigm. Moreover, we argue why such a scheme of representation is more consistent with known forms of synaptic plasticity than rate-based network dynamics.

scholarly journals Carbon Nanotubes: Artificial Nanomaterials to Engineer Single Neurons and Neuronal Networks

2012 ◽  
Vol 3 (8) ◽  
pp. 611-618 ◽  
Author(s):  
Alessandra Fabbro ◽  
Susanna Bosi ◽  
Laura Ballerini ◽  
Maurizio Prato
Keyword(s):  
Carbon Nanotubes ◽  
Neuronal Networks ◽  
Single Neurons
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