scholarly journals Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity

2016 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Computational Models ◽  
Neural Circuit ◽  
Detailed Balance ◽  
Experimental Studies ◽  
Spike Timing ◽  
Experimental Results ◽  
Dendritic Branch ◽  
Synaptic Organization ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

AbstractBalance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. Here, by considering computational models of heterosynaptic spike-timing-dependent plasticity (STDP), we show that the detailed excitatory/inhibitory balance on dendritic branch is robustly achieved through heterosynaptic interaction between excitatory and inhibitory synapses. The model well reproduces experimental results on heterosynaptic STDP, and provides analytical insights. Furthermore, heterosynaptic STDP explains how maturation of inhibitory neurons modulates selectivity of excitatory neurons in critical period plasticity of binocular matching. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and resultant dendritic computation.Significance statementRecent experimental studies have revealed that relative spike timings among neighboring Glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in synaptic efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Here, through computational modeling, we show that heterosynaptic plasticity causes the detailed balance between excitatory and inhibitory inputs on the dendrite, at the parameter regime where previous experimental results are well reproduced. Our result reveals a potential principle of GABA-driven neural circuit formation.

scholarly journals Spike-timing-dependent plasticity can account for aftereffects of dual-site transcranial alternating current stimulation

2020 ◽  
Author(s):  
Bettina C. Schwab ◽  
Peter König ◽  
Andreas K. Engel
Keyword(s):  
Computational Models ◽  
Alternating Current ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Eeg Data ◽  
Transcranial Alternating Current Stimulation ◽  
Eeg Connectivity ◽  
Phase Lags ◽  
Dual Site

AbstractBackgroundTranscranial alternating current stimulation (tACS), applied to two brain sites with different phase lags, has been shown to modulate stimulation-outlasting functional connectivity between the targeted regions.ObjectiveHere, we test if spike-timing-dependent plasticity (STDP) can explain stimulation-outlasting connectivity modulation by dual-site tACS and explore the effects of tACS parameter choices.MethodsNetworks with two populations of spiking neurons were simulated. Synapses between the populations were subject to STDP. We re-analyzed resting-state EEG data to validate the model.ResultsSimulations showed stimulation-outlasting connectivity changes between in- and anti-phase tACS, dependent on both tACS frequency and conduction delays. Importantly, the model predicted that the largest effects would occur for short conduction delays between the stimulated regions, which agreed with experimental EEG connectivity modulation by 10 Hz tACS.ConclusionSTDP can explain connectivity aftereffects of dual-site tACS. However, not all combinations of tACS frequency and application sites are expected to effectively modulate connectivity via STDP. We therefore suggest using appropriate computational models and/or EEG analysis for planning and interpretation of dualsite tACS studies relying on aftereffects.

Formation and Regulation of Dynamic Patterns in Two-Dimensional Spiking Neural Circuits with Spike-Timing-Dependent Plasticity

2013 ◽  
Vol 25 (11) ◽  
pp. 2833-2857 ◽  
Author(s):  
John H. C. Palmer ◽  
Pulin Gong
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Spike Timing ◽  
Fine Tuning ◽  
Regulation Mechanism ◽  
Two Dimensional ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wave Patterns ◽  
Dynamic Patterns

Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongoing patterns in the neural circuit and STDP. This interaction is analyzed by developing a simple model able to capture its essential dynamics, which give rise to symmetry breaking. This occurs in a fundamentally self-organizing manner, without fine-tuning of the system parameters. Moreover, we find that STDP provides a synaptic mechanism to learn the paths taken by spiking waves and modulate the dynamics of their interactions, enabling them to be regulated. This regulation mechanism has error-correcting properties. Our results therefore highlight the important roles played by STDP in facilitating the formation and regulation of spiking wave patterns that may have crucial functional roles in brain information processing.

scholarly journals Enhancement of Synchronization in a Hybrid Neural Circuit by Spike-Timing Dependent Plasticity

2003 ◽  
Vol 23 (30) ◽  
pp. 9776-9785 ◽  
Author(s):  
Thomas Nowotny ◽  
Valentin P. Zhigulin ◽  
Allan I. Selverston ◽  
Henry D. I. Abarbanel ◽  
Mikhail I. Rabinovich
Keyword(s):  
Neural Circuit ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

Learning Polychronous Neuronal Groups Using Joint Weight-Delay Spike-Timing-Dependent Plasticity

2016 ◽  
Vol 28 (10) ◽  
pp. 2181-2212 ◽  
Author(s):  
Haoqi Sun ◽  
Olga Sourina ◽  
Guang-Bin Huang
Keyword(s):  
Computational Models ◽  
Spike Timing ◽  
Cell Assembly ◽  
Information Representation ◽  
Spike Timing Dependent Plasticity ◽  
Ongoing Activity ◽  
Dependent Plasticity ◽  
Neuronal Group ◽  
Neural Information ◽  
Neuronal Groups

Polychronous neuronal group (PNG), a type of cell assembly, is one of the putative mechanisms for neural information representation. According to the reader-centric definition, some readout neurons can become selective to the information represented by polychronous neuronal groups under ongoing activity. Here, in computational models, we show that the frequently activated polychronous neuronal groups can be learned by readout neurons with joint weight-delay spike-timing-dependent plasticity. The identity of neurons in the group and their expected spike timing at millisecond scale can be recovered from the incoming weights and delays of the readout neurons. The detection performance can be further improved by two layers of readout neurons. In this way, the detection of polychronous neuronal groups becomes an intrinsic part of the network, and the readout neurons become differentiated members in the group to indicate whether subsets of the group have been activated according to their spike timing. The readout spikes representing this information can be used to analyze how PNGs interact with each other or propagate to downstream networks for higher-level processing.

scholarly journals Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models

2018 ◽  
Vol 12 ◽  
Author(s):  
Alexandre Foncelle ◽  
Alexandre Mendes ◽  
Joanna Jędrzejewska-Szmek ◽  
Silvana Valtcheva ◽  
Hugues Berry ◽  
...  
Keyword(s):  
Computational Models ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2007 ◽  
Vol 19 (5) ◽  
pp. 1362-1399 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Stochastic Model ◽  
Ad Hoc ◽  
Detailed Examination ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Experimental Results ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Natural Way ◽  
Exact Timing

Recently we presented a stochastic, ensemble-based model of spike-timing-dependent plasticity. In this model, single synapses do not exhibit plasticity depending on the exact timing of pre- and postsynaptic spikes, but spike-timing-dependent plasticity emerges only at the temporal or synaptic ensemble level. We showed that such a model reproduces a variety of experimental results in a natural way, without the introduction of various, ad hoc nonlinearities characteristic of some alternative models. Our previous study was restricted to an examination, analytically, of two-spike interactions, while higher-order, multispike interactions were only briefly examined numerically. Here we derive exact, analytical results for the general n-spike interaction functions in our model. Our results form the basis for a detailed examination, performed elsewhere, of the significant differences between these functions and the implications these differences have for the presence, or otherwise, of stable, competitive dynamics in our model.

scholarly journals Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

2021 ◽  
Author(s):  
Beatriz Eymi Pimentel Mizusaki ◽  
Sally Si Ying Li ◽  
Rui Ponte Costa ◽  
Jesper Sjöström
Keyword(s):  
Synaptic Plasticity ◽  
Activity Patterns ◽  
Experimental Studies ◽  
Spike Timing ◽  
Response Amplitude ◽  
Synaptic Response ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Response Range

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity

2007 ◽  
Vol 19 (2) ◽  
pp. 371-403 ◽  
Author(s):  
Sander M. Bohte ◽  
Michael C. Mozer
Keyword(s):  
Experimental Studies ◽  
Response Variability ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Presynaptic Neuron ◽  
Spike Response ◽  
Dependent Plasticity ◽  
Synaptic Modulation ◽  
The Face ◽  
Spike Response Model

Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity (STDP). We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's response variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an objective function that minimizes response variability and the biophysically realistic spike-response model of Gerstner (2001), we simulate neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena, including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational principles and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of unsupervised cortical adaptation.

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