Synaptic and Temporal Ensemble Interpretation of Spike-Timing-Dependent Plasticity

2005 ◽  
Vol 17 (11) ◽  
pp. 2316-2336 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Neuronal Plasticity ◽  
Spike Timing ◽  
Experimental Results ◽  
Coincidence Detection ◽  
Spatial Averaging ◽  
Critical Window ◽  
Dependent Plasticity ◽  
Minimal Form ◽  
Temporal And Spatial ◽  
Ensemble Interpretation

We postulate that a simple, three-state synaptic switch governs changes in synaptic strength at individual synapses. Under this switch rule, we show that a variety of experimental results on timing-dependent plasticity can emerge from temporal and spatial averaging over multiple synapses and multiple spike pairings. In particular, we show that a critical window for the interaction of pre- and postsynaptic spikes emerges as an ensemble property of the collective system, with individual synapses exhibiting only a minimal form of spike coincidence detection. In addition, we show that a Bienenstock-Cooper-Munro—like, rate-based plasticity rule emerges directly from such a model. This demonstrates that two apparently separate forms of neuronal plasticity can emerge from a much simpler rule governing the plasticity of individual synapses.

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.

Effect of Stochastic Synaptic and Dendritic Dynamics on Synaptic Plasticity in Visual Cortex and Hippocampus

2007 ◽  
Vol 97 (1) ◽  
pp. 375-386 ◽  
Author(s):  
Yidao Cai ◽  
Jeffrey P. Gavornik ◽  
Leon N. Cooper ◽  
Luk C. Yeung ◽  
Harel Z. Shouval
Keyword(s):  
Synaptic Plasticity ◽  
Visual Cortex ◽  
Action Potentials ◽  
Spike Timing ◽  
Experimental Results ◽  
Linear Superposition ◽  
Dependent Plasticity ◽  
Calcium Dependent ◽  
Synaptic Dynamics ◽  
Better Than

Various forms of synaptic plasticity, including spike timing-dependent plasticity, can be accounted for by calcium-dependent models of synaptic plasticity. However, recent results in which synaptic plasticity is induced by multi-spike protocols cannot simply be accounted for by linear superposition of plasticity due to spike pairs or by existing calcium-dependent models. In this paper, we show that multi-spike protocols can be accounted for if, in addition to the dynamics of back-propagating action potentials, stochastic synaptic dynamics are taken into account. We show that a stochastic implementation can account for the data better than a deterministic implementation and is also more robust. Our results demonstrate that differences between experimental results obtained in hippocampus and visual cortex can be accounted for by the different synaptic and dendritic dynamics in these two systems.

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.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

Sign in / Sign up

Export Citation Format

Share Document