Activity-Dependent Regulation of Neural Networks: The Role of Inhibitory Synaptic Plasticity in Adaptive Gain Control in the Siphon Withdrawal Reflex of Aplysia

1997 ◽  
Vol 192 (1) ◽  
pp. 164-166 ◽  
Author(s):  
T. M. Fischer ◽  
T. J. Carew
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Gain Control ◽  
Withdrawal Reflex ◽  
Activity Dependent

scholarly journals Attractor neural networks with activity-dependent synapses: The role of synaptic facilitation

2007 ◽  
Vol 70 (10-12) ◽  
pp. 2022-2025 ◽  
Author(s):  
J.J. Torres ◽  
J.M. Cortes ◽  
J. Marro ◽  
H.J. Kappen
Keyword(s):  
Neural Networks ◽  
Synaptic Facilitation ◽  
Activity Dependent

scholarly journals Postsynaptic regulation of synaptic plasticity by synaptotagmin 4 requires both C2 domains

2009 ◽  
Vol 187 (2) ◽  
pp. 295-310 ◽  
Author(s):  
Cynthia F. Barber ◽  
Ramon A. Jorquera ◽  
Jan E. Melom ◽  
J. Troy Littleton
Keyword(s):  
Synaptic Plasticity ◽  
Messenger Rna ◽  
Molecular Mechanisms ◽  
Neuromuscular Junctions ◽  
Vesicle Fusion ◽  
Synaptic Growth ◽  
Synaptotagmin 1 ◽  
Presynaptic Vesicle ◽  
Activity Dependent

Ca2+ influx into synaptic compartments during activity is a key mediator of neuronal plasticity. Although the role of presynaptic Ca2+ in triggering vesicle fusion though the Ca2+ sensor synaptotagmin 1 (Syt 1) is established, molecular mechanisms that underlie responses to postsynaptic Ca2+ influx remain unclear. In this study, we demonstrate that fusion-competent Syt 4 vesicles localize postsynaptically at both neuromuscular junctions (NMJs) and central nervous system synapses in Drosophila melanogaster. Syt 4 messenger RNA and protein expression are strongly regulated by neuronal activity, whereas altered levels of postsynaptic Syt 4 modify synaptic growth and presynaptic release properties. Syt 4 is required for known forms of activity-dependent structural plasticity at NMJs. Synaptic proliferation and retrograde signaling mediated by Syt 4 requires functional C2A and C2B Ca2+–binding sites, as well as serine 284, an evolutionarily conserved substitution for a key Ca2+-binding aspartic acid found in other synaptotagmins. These data suggest that Syt 4 regulates activity-dependent release of postsynaptic retrograde signals that promote synaptic plasticity, similar to the role of Syt 1 as a Ca2+ sensor for presynaptic vesicle fusion.

scholarly journals Sequential Temporal Anticipation Characterized by Neural Power Modulation and in Recurrent Neural Networks

2021 ◽  
Author(s):  
Xiangbin Teng ◽  
Ru-Yuan Zhang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Gain Control ◽  
Beta Band ◽  
Power Modulation ◽  
Delta Band ◽  
Alpha Beta ◽  
The Brain ◽  
Dynamic Gain

Complex human behaviors involve perceiving continuous stimuli and planning actions at sequential time points, such as in perceiving/producing speech and music. To guide adaptive behavior, the brain needs to internally anticipate a sequence of prospective moments. How does the brain achieve this sequential temporal anticipation without relying on any external timing cues? To answer this question, we designed a premembering task: we tagged three temporal locations in white noise by asking human listeners to detect a tone presented at one of the temporal locations. We selectively probed the anticipating processes guided by memory in trials with only flat noise using novel modulation analyses. A multiscale anticipating scheme was revealed: the neural power modulation in the delta band encodes noise duration on a supra-second scale; the modulations in the alpha-beta band range mark the tagged temporal locations on a subsecond scale and correlate with tone detection performance. To unveil the functional role of those neural observations, we turned to recurrent neural networks (RNNs) optimized for the behavioral task. The RNN hidden dynamics resembled the neural modulations; further analyses and perturbations on RNNs suggest that the neural power modulations in the alpha/beta band emerged as a result of selectively suppressing irrelevant noise periods and increasing sensitivity to the anticipated temporal locations. Our neural, behavioral, and modelling findings convergingly demonstrate that the sequential temporal anticipation involves a process of dynamic gain control: to anticipate a few meaningful moments is also to actively ignore irrelevant events that happen most of the time.

scholarly journals Elements of a neurobiological theory of the hippocampus: the role of activity-dependent synaptic plasticity in memory

2003 ◽  
Vol 358 (1432) ◽  
pp. 773-786 ◽  
Author(s):  
R. G. M. Morris ◽  
E. I. Moser ◽  
G. Riedel ◽  
S. J. Martin ◽  
J. Sandin ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Long Term Potentiation ◽  
Cellular Mechanisms ◽  
Memory Processing ◽  
Functional Studies ◽  
Term Potentiation ◽  
Intermediate Storage ◽  
Activity Dependent

The hypothesis that synaptic plasticity is a critical component of the neural mechanisms underlying learning and memory is now widely accepted. In this article, we begin by outlining four criteria for evaluating the ‘synaptic plasticity and memory (SPM)’ hypothesis. We then attempt to lay the foundations for a specific neurobiological theory of hippocampal (HPC) function in which activity-dependent synaptic plasticity, such as long-term potentiation (LTP), plays a key part in the forms of memory mediated by this brain structure. HPC memory can, like other forms of memory, be divided into four processes: encoding, storage, consolidation and retrieval. We argue that synaptic plasticity is critical for the encoding and intermediate storage of memory traces that are automatically recorded in the hippocampus. These traces decay, but are sometimes retained by a process of cellular consolidation. However, we also argue that HPC synaptic plasticity is not involved in memory retrieval, and is unlikely to be involved in systems-level consolidation that depends on HPC-neocortical interactions, although neocortical synaptic plasticity does play a part. The information that has emerged from the worldwide focus on the mechanisms of induction and expression of plasticity at individual synapses has been very valuable in functional studies. Progress towards a comprehensive understanding of memory processing will also depend on the analysis of these synaptic changes within the context of a wider range of systems-level and cellular mechanisms of neuronal transmission and plasticity.

scholarly journals Dual regulation of spine-specific and synapse-to-nucleus signaling by PKCδ during plasticity

2021 ◽  
Author(s):  
Lesley A Colgan ◽  
Paula Parra-Bueno ◽  
Heather L. Holman ◽  
Mariah F Calubag ◽  
Jaime A Misler ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Pyramidal Neurons ◽  
The Novel ◽  
Dual Functional ◽  
Activity Sensors ◽  
Pkc Isozymes ◽  
And Behavior ◽  
Activity Dependent ◽  
Spine Plasticity

The activity-dependent plasticity of synapses is believed to be the cellular basis of learning. These synaptic changes are mediated through the coordination of local biochemical reactions in synapses and changes in gene transcription in the nucleus to modulate neuronal circuits and behavior. The protein kinase C (PKC) family of isozymes has long been established as critical for synaptic plasticity. However, due to a lack of suitable isozyme-specific tools, the role of the novel subfamily of PKC isozymes is largely unknown. Here, through the development of FLIM-FRET activity sensors, we investigate novel PKC isozymes in synaptic plasticity in mouse CA1 pyramidal neurons. We find that PKCδ is activated downstream of TrkB and that the spatiotemporal nature of its activation depends on the plasticity stimulation. In response to single spine plasticity, PKCδ is activated primarily in the stimulated spine and is required for local expression of plasticity. However, in response to multi-spine stimulation, a long-lasting and spreading activation of PKCδ scales with the number of spines stimulated and, by regulating CREB activity, couples spine plasticity to transcription in the nucleus. Thus, PKCδ plays a dual functional role in facilitating synaptic plasticity.

scholarly journals A New Computational Model for Astrocytes and Their Role in Biologically Realistic Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zahra Sajedinia ◽  
Sébastien Hélie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Synaptic Plasticity ◽  
Computational Model ◽  
Bipartite Network ◽  
Presynaptic Neuron ◽  
Tripartite Synapse ◽  
And Performance ◽  
Robust To Noise

Recent studies in neuroscience show that astrocytes alongside neurons participate in modulating synapses. It led to the new concept of “tripartite synapse”, which means that a synapse consists of three parts: presynaptic neuron, postsynaptic neuron, and neighboring astrocytes. However, it is still unclear what role is played by the astrocytes in the tripartite synapse. Detailed biocomputational modeling may help generate testable hypotheses. In this article, we aim to study the role of astrocytes in synaptic plasticity by exploring whether tripartite synapses are capable of improving the performance of a neural network. To achieve this goal, we developed a computational model of astrocytes based on the Izhikevich simple model of neurons. Next, two neural networks were implemented. The first network was only composed of neurons and had standard bipartite synapses. The second network included both neurons and astrocytes and had tripartite synapses. We used reinforcement learning and tested the networks on categorizing random stimuli. The results show that tripartite synapses are able to improve the performance of a neural network and lead to higher accuracy in a classification task. However, the bipartite network was more robust to noise. This research provides computational evidence to begin elucidating the possible beneficial role of astrocytes in synaptic plasticity and performance of a neural network.

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