scholarly journals Local dendritic balance enables learning of efficient representations in networks of spiking neurons

2021 ◽  
Vol 118 (50) ◽  
pp. e2021925118
Author(s):  
Fabian A. Mikulasch ◽  
Lucas Rudelt ◽  
Viola Priesemann
Keyword(s):  
Synaptic Plasticity ◽  
First Principles ◽  
Representation Learning ◽  
Neural Dynamics ◽  
High Dimensional ◽  
Transmission Delays ◽  
Learning Scheme ◽  
Voltage Dependent ◽  
Classical Models

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory–inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

Brivaracetam Prevents the Over-expression of Synaptic Vesicle Protein 2A and Rescues the Deficits of Hippocampal Long-term Potentiation In Vivo in Chronic Temporal Lobe Epilepsy Rats

2020 ◽  
Vol 17 (4) ◽  
pp. 354-360 ◽  
Author(s):  
Yu-Xing Ge ◽  
Ying-Ying Lin ◽  
Qian-Qian Bi ◽  
Yu-Juan Chen
Keyword(s):  
Synaptic Plasticity ◽  
Temporal Lobe Epilepsy ◽  
Synaptic Vesicle ◽  
Long Term Potentiation ◽  
Synaptic Dysfunction ◽  
Over Expression ◽  
Term Potentiation ◽  
Synaptic Vesicle Protein 2A

Background: Patients with temporal lobe epilepsy (TLE) usually suffer from cognitive deficits and recurrent seizures. Brivaracetam (BRV) is a novel anti-epileptic drug (AEDs) recently used for the treatment of partial seizures with or without secondary generalization. Different from other AEDs, BRV has some favorable properties on synaptic plasticity. However, the underlying mechanisms remain elusive. Objective: The aim of this study was to explore the neuroprotective mechanism of BRV on synaptic plasticity in experimental TLE rats. Methods: The effect of chronic treatment with BRV (10 mg/kg) was assessed on Pilocarpine induced TLE model through measurement of the field excitatory postsynaptic potentials (fEPSPs) in vivo. Differentially expressed synaptic vesicle protein 2A (SV2A) were identified with immunoblot. Then, fast phosphorylation of synaptosomal-associated protein 25 (SNAP-25) during long-term potentiation (LTP) induction was performed to investigate the potential roles of BRV on synaptic plasticity in the TLE model. Results: An increased level of SV2A accompanied by a depressed LTP in the hippocampus was shown in epileptic rats. Furthermore, BRV treatment continued for more than 30 days improved the over-expression of SV2A and reversed the synaptic dysfunction in epileptic rats. Additionally, BRV treatment alleviates the abnormal SNAP-25 phosphorylation at Ser187 during LTP induction in epileptic ones, which is relevant to the modulation of synaptic vesicles exocytosis and voltagegated calcium channels. Conclusion: BRV treatment ameliorated the over-expression of SV2A in the hippocampus and rescued the synaptic dysfunction in epileptic rats. These results identify the neuroprotective effect of BRV on TLE model.

Inhibition of dihydropyridine-sensitive calcium entry in hypoxic relaxation of airway smooth muscle

1995 ◽  
Vol 268 (2) ◽  
pp. L201-L206 ◽  
Author(s):  
C. Vannier ◽  
T. L. Croxton ◽  
L. S. Farley ◽  
C. A. Hirshman
Keyword(s):  
Smooth Muscle ◽  
Airway Smooth Muscle ◽  
Muscle Tone ◽  
Bay K 8644 ◽  
O2 Concentration ◽  
Voltage Dependent ◽  
Progressive Hypoxia ◽  
Carbamyl Choline

Hypoxia dilates airways in vivo and reduces active tension of airway smooth muscle in vitro. To determine whether hypoxia impairs Ca2+ entry through voltage-dependent channels (VDC), we tested the ability of dihydropyridines to modulate hypoxia-induced relaxation of KCl- and carbamyl choline (carbachol)-contracted porcine bronchi. Carbachol- or KCl-contracted bronchial rings were exposed to progressive hypoxia in the presence or absence of 1 microM BAY K 8644 (an L-type-channel agonist). In separate experiments, rings were contracted with carbachol or KCl, treated with nifedipine (a VDC antagonist), and finally exposed to hypoxia. BAY K 8644 prevented hypoxia-induced relaxation in KCl-contracted bronchi. Nifedipine (10(-5) M) totally relaxed KCl- contracted bronchi. Carbachol-contracted bronchi were only partially relaxed by nifedipine but were completely relaxed when the O2 concentration of the gas was reduced from 95 to 0%. These data indicate that hypoxia can reduce airway smooth muscle tone by limiting entry of Ca2+ through a dihydropyridine-sensitive pathway, but that other mechanisms also contribute to hypoxia-induced relaxation of carbachol-contracted bronchi.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

scholarly journals Dynamic modulation of spike timing-dependent calcium influx during corticostriatal upstates

2013 ◽  
Vol 110 (7) ◽  
pp. 1631-1645 ◽  
Author(s):  
R. C. Evans ◽  
Y. M. Maniar ◽  
K. T. Blackwell
Keyword(s):  
Synaptic Plasticity ◽  
Slow Wave Sleep ◽  
Calcium Influx ◽  
Spike Timing ◽  
Medium Spiny Neuron ◽  
Calcium Transients ◽  
Biophysical Model ◽  
Published Data ◽  
High Calcium

The striatum of the basal ganglia demonstrates distinctive upstate and downstate membrane potential oscillations during slow-wave sleep and under anesthetic. The upstates generate calcium transients in the dendrites, and the amplitude of these calcium transients depends strongly on the timing of the action potential (AP) within the upstate. Calcium is essential for synaptic plasticity in the striatum, and these large calcium transients during the upstates may control which synapses undergo plastic changes. To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). We have implemented sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion, which accurately replicate published data. Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. These findings have implications for synaptic plasticity in vivo during sleep when the upstate-downstate pattern is prominent in the striatum.

scholarly journals Optogenetic Regulation of Mitochondrial Function and Synaptic Plasticity In Vivo

2019 ◽  
Vol 116 (3) ◽  
pp. 267a
Author(s):  
Kristian M. Zapata ◽  
Illya Aronskyy ◽  
Stephen Madamba ◽  
Pablo M. Peixoto
Keyword(s):  
Synaptic Plasticity ◽  
Mitochondrial Function

scholarly journals Protein kinase D promotes plasticity-induced F-actin stabilization in dendritic spines and regulates memory formation

2015 ◽  
Vol 210 (5) ◽  
pp. 771-783 ◽  
Author(s):  
Norbert Bencsik ◽  
Zsófia Szíber ◽  
Hanna Liliom ◽  
Krisztián Tárnok ◽  
Sándor Borbély ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Protein Kinase ◽  
Dendritic Spines ◽  
Morphological Changes ◽  
Long Term Potentiation ◽  
Memory Formation ◽  
Protein Kinase D

Actin turnover in dendritic spines influences spine development, morphology, and plasticity, with functional consequences on learning and memory formation. In nonneuronal cells, protein kinase D (PKD) has an important role in stabilizing F-actin via multiple molecular pathways. Using in vitro models of neuronal plasticity, such as glycine-induced chemical long-term potentiation (LTP), known to evoke synaptic plasticity, or long-term depolarization block by KCl, leading to homeostatic morphological changes, we show that actin stabilization needed for the enlargement of dendritic spines is dependent on PKD activity. Consequently, impaired PKD functions attenuate activity-dependent changes in hippocampal dendritic spines, including LTP formation, cause morphological alterations in vivo, and have deleterious consequences on spatial memory formation. We thus provide compelling evidence that PKD controls synaptic plasticity and learning by regulating actin stability in dendritic spines.

In vivo BDNF modulation of adult functional and morphological synaptic plasticity at hippocampal mossy fibers

2008 ◽  
Vol 445 (1) ◽  
pp. 62-67 ◽  
Author(s):  
Andrea Gómez-Palacio-Schjetnan ◽  
Martha L. Escobar
Keyword(s):  
Synaptic Plasticity ◽  
Mossy Fibers

scholarly journals Regulation of GABAergic synapse formation and plasticity by GSK3β-dependent phosphorylation of gephyrin

2010 ◽  
Vol 108 (1) ◽  
pp. 379-384 ◽  
Author(s):  
Shiva K. Tyagarajan ◽  
Himanish Ghosh ◽  
Gonzalo E. Yévenes ◽  
Irina Nikonenko ◽  
Claire Ebeling ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Hippocampal Neurons ◽  
Glycogen Synthase ◽  
Phosphorylation Site ◽  
Scaffolding Protein ◽  
Scaffolding Proteins ◽  
Gabaergic Synapse ◽  
Gabaergic Synapses

Postsynaptic scaffolding proteins ensure efficient neurotransmission by anchoring receptors and signaling molecules in synapse-specific subcellular domains. In turn, posttranslational modifications of scaffolding proteins contribute to synaptic plasticity by remodeling the postsynaptic apparatus. Though these mechanisms are operant in glutamatergic synapses, little is known about regulation of GABAergic synapses, which mediate inhibitory transmission in the CNS. Here, we focused on gephyrin, the main scaffolding protein of GABAergic synapses. We identify a unique phosphorylation site in gephyrin, Ser270, targeted by glycogen synthase kinase 3β (GSK3β) to modulate GABAergic transmission. Abolishing Ser270 phosphorylation increased the density of gephyrin clusters and the frequency of miniature GABAergic postsynaptic currents in cultured hippocampal neurons. Enhanced, phosphorylation-dependent gephyrin clustering was also induced in vitro and in vivo with lithium chloride. Lithium is a GSK3β inhibitor used therapeutically as mood-stabilizing drug, which underscores the relevance of this posttranslational modification for synaptic plasticity. Conversely, we show that gephyrin availability for postsynaptic clustering is limited by Ca2+-dependent gephyrin cleavage by the cysteine protease calpain-1. Together, these findings identify gephyrin as synaptogenic molecule regulating GABAergic synaptic plasticity, likely contributing to the therapeutic action of lithium.

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