scholarly journals Homeostatic plasticity and external input shape neural network dynamics

2018 ◽  
Author(s):  
Johannes Zierenberg ◽  
Jens Wilting ◽  
Viola Priesemann
Keyword(s):  
Neural Network ◽  
Brain Area ◽  
External Input ◽  
Homeostatic Plasticity ◽  
Dynamic State ◽  
Network Topologies ◽  
Neural Network Dynamics ◽  
Spiking Activity

In vitro and in vivo spiking activity clearly differ. Whereas networks in vitro develop strong bursts separated by periods of very little spiking activity, in vivo cortical networks show continuous activity. This is puzzling considering that both networks presumably share similar single-neuron dynamics and plasticity rules. We propose that the defining difference between in vitro and in vivo dynamics is the strength of external input. In vitro, networks are virtually isolated, whereas in vivo every brain area receives continuous input. We analyze a model of spiking neurons in which the input strength, mediated by spike rate homeostasis, determines the characteristics of the dynamical state. In more detail, our analytical and numerical results on various network topologies show consistently that under increasing input, homeostatic plasticity generates distinct dynamic states, from bursting, to close-to-critical, reverberating and irregular states. This implies that the dynamic state of a neural network is not fixed but can readily adapt to the input strengths. Indeed, our results match experimental spike recordings in vitro and in vivo: the in vitro bursting behavior is consistent with a state generated by very low network input (< 0.1%), whereas in vivo activity suggests that on the order of 1% recorded spikes are input-driven, resulting in reverberating dynamics. Importantly, this predicts that one can abolish the ubiquitous bursts of in vitro preparations, and instead impose dynamics comparable to in vivo activity by exposing the system to weak long-term stimulation, thereby opening new paths to establish an in vivo-like assay in vitro for basic as well as neurological studies.

scholarly journals Amyloid Beta-Protein and Neural Network Dysfunction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Fernando Peña-Ortega
Keyword(s):  
Neural Network ◽  
Cognitive Dysfunction ◽  
Amyloid Beta ◽  
Network Dynamics ◽  
Basic Knowledge ◽  
Specific Cell ◽  
Amyloid Beta Protein ◽  
Neural Network Dynamics

Understanding the neural mechanisms underlying brain dysfunction induced by amyloid beta-protein (Aβ) represents one of the major challenges for Alzheimer’s disease (AD) research. The most evident symptom of AD is a severe decline in cognition. Cognitive processes, as any other brain function, arise from the activity of specific cell assemblies of interconnected neurons that generate neural network dynamics based on their intrinsic and synaptic properties. Thus, the origin of Aβ-induced cognitive dysfunction, and possibly AD-related cognitive decline, must be found in specific alterations in properties of these cells and their consequences in neural network dynamics. The well-known relationship between AD and alterations in the activity of several neural networks is reflected in the slowing of the electroencephalographic (EEG) activity. Some features of the EEG slowing observed in AD, such as the diminished generation of different network oscillations, can be induced in vivo and in vitro upon Aβ application or by Aβ overproduction in transgenic models. This experimental approach offers the possibility to study the mechanisms involved in cognitive dysfunction produced by Aβ. This type of research may yield not only basic knowledge of neural network dysfunction associated with AD, but also novel options to treat this modern epidemic.

scholarly journals Silencing-Induced Metaplasticity in Hippocampal Cultured Neurons

2008 ◽  
Vol 100 (2) ◽  
pp. 690-697 ◽  
Author(s):  
Irina V. Sokolova ◽  
Istvan Mody
Keyword(s):  
Hippocampal Neurons ◽  
Channel Blocker ◽  
Recovery Period ◽  
Long Term Potentiation ◽  
Homeostatic Plasticity ◽  
Membrane Properties ◽  
Postsynaptic Currents ◽  
Short Period ◽  
Spiking Activity

Silencing-induced homeostatic plasticity is usually expressed as a change in the amplitude or the frequency of miniature postsynaptic currents. Here we report that, prolonged (∼24 h) silencing of mature (20–22 days in vitro) cultured hippocampal neurons using the voltage-gated sodium channel blocker tetrodotoxin (TTX) produced no effects on the amplitude or frequency of the miniature excitatory postsynaptic currents (mEPSCs). However, the silencing changed the intrinsic membrane properties of the neurons, resulting in an increased excitability and rate of action potentials firing upon TTX washout. Allowing neurons to recover in TTX-free recording solution for a short period of time after the silencing resulted in potentiation of mEPSC amplitudes. This form of activity-dependent potentiation is different from classical long-term potentiation, as similar potentiation was not seen in nonsilenced neurons treated with bicuculline to raise their spiking activity to the same level displayed by the silenced neurons during TTX washout. Also, the potentiation of mEPSC amplitudes after the recovery period was not affected by the N-methyl-d-aspartate receptor blocker d-2-amino-5-phosponopentanoic acid or by the calcium/calmodulin-dependent kinase II (CaMKII) inhibitor KN-62 but was abolished by the L-type calcium channel blocker nifedipine. We thus conclude that the potentiation of mEPSC amplitudes following brief recovery of spiking activity in chronically silenced neurons represents a novel form of metaplasticity that differs from the conventional models of homeostatic synaptic plasticity.

scholarly journals Artificial Neural Network in Drug Delivery and Pharmaceutical Research

2013 ◽  
Vol 7 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Vijaykumar Sutariya ◽  
Anastasia Groshev ◽  
Prabodh Sadana ◽  
Deepak Bhatia ◽  
Yashwant Pathak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Delivery ◽  
Pattern Recognition ◽  
Analytical Data ◽  
Pharmaceutical Research ◽  
Artificial Neural ◽  
The Brain

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

Homeostatic Plasticity in the CNS

2019 ◽  
Author(s):  
Peter Wenner ◽  
Pernille Bülow
Keyword(s):  
Homeostatic Plasticity ◽  
Network Activity ◽  
Signaling Cascades ◽  
Neural Function ◽  
Activity Levels ◽  
Membrane Excitability ◽  
Activity Burst ◽  
Intrinsic Plasticity

Homeostatic plasticity refers to a collection of mechanisms that function to homeostatically maintain some feature of neural function. The field began with the view that homeostatic plasticity exists predominantly for the maintenance of spike rate. However, it has become clear that multiple features undergo some form of homeostatic control, including network activity, burst rate, or synaptic strength. There are several different forms of homeostatic plasticity, which are typically triggered following perturbations in activity levels. Homeostatic intrinsic plasticity (HIP) appears to compensate for the perturbation with changes in membrane excitability (voltage-gated conductances); synaptic scaling is thought to be a multiplicative increase or decrease of synaptic strengths throughout the cell following an activity perturbation; presynaptic homeostatic plasticity is a change in probability of release following a perturbation to postsynaptic receptor activity. Each form of homeostatic plasticity can be different in terms of the mechanisms that are engaged, the feature that is homeostatically regulated, the trigger that initiates the compensation, and the signaling cascades that mediate these processes. Homeostatic plasticity is often described in development, but can extend into maturity and has been described in vitro and in vivo.

scholarly journals Kv4.3 channel downregulation mediates chronic post-lesional pacemaker acceleration in surviving dopamine substantia nigra neurons

2021 ◽  
Author(s):  
Lora Kovacheva ◽  
Josef Shin ◽  
Navid Farassat ◽  
Jochen Roeper
Keyword(s):  
Substantia Nigra ◽  
Mouse Model ◽  
Homeostatic Plasticity ◽  
Cellular Mechanisms ◽  
Differential Vulnerability ◽  
Autonomous Adaptation ◽  
Partial Lesion ◽  
Firing Properties

Substantia nigra dopamine (SN DA) neurons are progressively lost in Parkinson disease (PD). While the molecular and cellular mechanisms of their differential vulnerability and degeneration have been extensively studied, we still know very little about potential functional adaptations of those SN DA neurons that at least for some time manage to survive during earlier stages of PD. We utilized a partial lesion 6-OHDA mouse model to characterize initial electrophysiological impairments and chronic adaptations of surviving identified SN DA neurons, both in vivo and in vitro. Early after lesion (3 weeks), we detected a selective loss of in vivo burst firing in surviving SN DA neurons, which was accompanied by in vitro pacemaker instability. In contrast, late after lesion (>2 months), in vivo firing properties of surviving SN DA neurons had recovered in the presence of 2-fold accelerated pacemaking in vitro. Finally, we show that this chronic cell-autonomous adaptation in surviving SN DA neurons was mediated by Kv4.3 channel downregulation. Our study demonstrates substantial homeostatic plasticity of surviving SN DA neurons after a single-hit non-progressive lesion, which might contribute to the phenotype of initially surviving SN DA neurons in PD.

scholarly journals A Deep Learning Approach for Learning Intrinsic Protein-RNA Binding Preferences

2018 ◽  
Author(s):  
Ilan Ben-Bassat ◽  
Benny Chor ◽  
Yaron Orenstein
Keyword(s):  
Neural Network ◽  
Rna Structure ◽  
Rna Binding ◽  
Gene Expression Regulation ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Binding Prediction ◽  
Binding Models

AbstractMotivationThe complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation, and viral replication. Understanding protein-RNA binding may thus provide important insights to the functionality and dynamics of many cellular processes. This has sparked substantial interest in exploring protein-RNA binding experimentally, and predicting it computationally. The key computational challenge is to efficiently and accurately infer RNA-binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest.ResultsWe developed DLPRB, a new deep neural network (DNN) approach for learning protein-RNA binding preferences and predicting novel interactions. We present two different network architectures: a convolutional neural network (CNN), and a recurrent neural network (RNN). The novelty of our network hinges upon two key aspects: (i) the joint analysis of both RNA sequence and structure, which is represented as a probability vector of different RNA structural contexts; (ii) novel features in the architecture of the networks, such as the application of RNNs to RNA-binding prediction, and the combination of hundreds of variable-length filters in the CNN. Our results in inferring accurate RNA-binding models from high-throughput in vitro data exhibit substantial improvements, compared to all previous approaches for protein-RNA binding prediction (both DNN and non-DNN based). A highly significant improvement is achieved for in vitro binding prediction, and a more modest, yet statistically significant,improvement for in vivo binding prediction. When incorporating experimentally-measured RNA structure compared to predicted one, the improvement on in vivo data increases. By visualizing the binding specificities, we can gain novel biological insights underlying the mechanism of protein RNA-binding.AvailabilityThe source code is publicly available at https://github.com/ilanbb/[email protected] informationSupplementary data are available at Bioinformatics online.

Induction of Parkinsonian-Like Changes via Targeted Downregulation of Astrocytic Glutamate Transporter GLT-1 in the Striatum

2021 ◽  
pp. 1-20
Author(s):  
Chao Ren ◽  
Kai-Jie He ◽  
Hua Hu ◽  
Jin-Bao Zhang ◽  
Li-Guo Dong ◽  
...  
Keyword(s):  
Western Blotting ◽  
High Performance ◽  
Brain Area ◽  
Glutamate Transporter ◽  
Glutamate Excitotoxicity ◽  
Calcium Signal ◽  
Abnormal Gait ◽  
Pet Ct

Background: Previous investigations have suggested that decreased expression of glutamate transporter-1 (GLT-1) is involved in glutamate excitotoxicity and contribute to the development of Parkinson’s disease (PD), GLT-1 is decreased in animal models of PD. GLT-1 is mainly expressed in astrocytes, and the striatum is a GLT-1-rich brain area. Objective: The aim was to explore the function and mechanism of astrocytic GLT-1 in PD-like changes. Methods: In the study, PD-like changes and their molecular mechanism in rodents were tested by a behavioral assessment, micro-positron emission tomography/computed tomography (PET/CT), western blotting, immunohistochemical and immunofluorescence staining, and high performance liquid chromatography pre-column derivatization with O-pthaldialdehida after downregulating astrocytic GLT-1 in vivo and in vitro. Results: In vivo, after 6 weeks of brain stereotactic injection of adeno-associated virus into the striatum, rats in the astrocytic GLT-1 knockdown group showed poorer motor performance, abnormal gait, and depression-like feature; but no olfactory disorders. The results of micro-PET/CT and western blotting indicated that the dopaminergic system was impaired in astrocytic GLT-1 knockdown rats. Similarly, tyrosine hydroxylase (TH) positive immune-staining in neurons of astrocytic GLT-1 knockdown rats showed deficit in cell count. In vitro, knockdown of astrocytic GLT-1 via RNA interference led to morphological injury of TH-positive neurons, which may be related to the abnormal calcium signal induced by glutamate accumulation after GLT-1 knockdown. Furthermore, the GLT-1 agonist ceftriaxone showed a protective effect on TH-positive neuron impairment. Conclusion: The present findings may shed new light on the future prevention and treatment of PD based on blocking glutamate excitotoxicity.

scholarly journals Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

2009 ◽  
Vol 21 (4) ◽  
pp. 1038-1067 ◽  
Author(s):  
Takuma Tanaka ◽  
Takeshi Kaneko ◽  
Toshio Aoyagi
Keyword(s):  
Learning Algorithm ◽  
Recurrent Network ◽  
External Input ◽  
Point Of View ◽  
State Transitions ◽  
Natural Scenes ◽  
Wide Range ◽  
Theoretic Point

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly–like and synfire chain–like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.

Sign in / Sign up

Export Citation Format

Share Document