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

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.

Download Full-text

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

10.1101/2021.09.01.458493 ◽

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.

Download Full-text

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

10.1101/056093 ◽

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.

Download Full-text

Spike timing-dependent plasticity in sparse recurrent neural networks

IEICE Proceeding Series ◽

10.15248/proc.1.485 ◽

2014 ◽

Vol 1 ◽

pp. 485-488

Author(s):

Hideyuki Kato ◽

Tohru Ikeguchi

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Spike Timing ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity

Download Full-text

Faculty Opinions recommendation of Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1028219.335094 ◽

2005 ◽

Author(s):

Dan Johnston

Keyword(s):

Spike Timing ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity

Download Full-text

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

Neural Computation ◽

10.1162/neco.2006.18.10.2414 ◽

2006 ◽

Vol 18 (10) ◽

pp. 2414-2464 ◽

Cited By ~ 19

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.

Download Full-text